FemWIKI - User contributions [en]

Attack rate

2026-05-18T13:09:15Z

Bosmana fem: /* Attack rates and case fatality */

=Attack rates and case fatality=
Attack rates and case fatality rates are examples of how the epidemiological jargon may be confusing.
==Attack rates==
# Attack rates are actually [[Risks and rates|risks]] (or "incidence proportion" or "cumulative incidence")
# Attack rates are often expressed as a percentage.
An attack rate is not an [[incidence rate]]. It is actually a risk (also called incidence proportions), and the time contribution of each individual is not included in the denominator.
The denominator consists of the number of people present at the beginning of the outbreak, disregarding those who will leave, develop illness, or die. This means that the cases (numerators) are also included in the denominator: it is, therefore, a true proportion.
In outbreaks of short duration, the attack rate is frequently used instead of risk or incidence proportion. We often refer to "food-specific attack rates in a foodborne outbreak." In this circumstance, the denominator will consist of the number of people who ate a specific food, while the numerator will consist of the number of people who ate that food and became ill.

===Secondary attack rate===
The '''secondary attack rate''' measures the spread of disease from a known case to susceptible contacts within a defined group, such as a household, classroom, or ward. It is calculated as the number of new cases occurring among contacts of a primary case, divided by the total number of susceptible contacts, during the incubation period following exposure. The primary case itself is excluded from both the numerator and the denominator.

Secondary attack rates are particularly useful for assessing the transmissibility of an infectious agent in a closed setting, and for evaluating the effectiveness of interventions such as vaccination, prophylaxis, or isolation measures.

=Case fatality, rates, and ratios: all the same?=
No!
These are distinctly different concepts, though in many epidemiological manuscripts (and even textbooks), you will find that case fatality, case fatality rate, and case fatality ratio are synonyms. However, they are not.

==Case fatality==
Case fatality is the concept used to express the proportion of cases of a certain disease that actually dies due to the consequences of that disease. Since it is a proportion, it is usually expressed as percent or per 1000. The case fatality can be seen as a cumulative incidence. It is relevant to remember that the death has to be due to the consequences of the disease since otherwise, each disease would have a case fatality of 100% (since all people die eventually). It is a true proportion since the denominator includes all cases, even those who died (the numerator).

Example of case fatality: around 1850, the case fatality of cholera (for which then there was no effective treatment) was up to 40%. This means that out of each 100 cases of cholera, 40 would eventually die due to the disease, usually within 2 weeks after onset. In comparison, the case fatality of tuberculosis in those times was almost 100% within the first 2 years after diagnosis since there was no cure for tuberculosis either.

==Case fatality rate (CFR)==
The CFR is a case fatality expressed over time. It is, therefore, a true rate since time is included in the denominator. It can be expressed as the number of deaths among cases per 100 or 1000 person-years. Depending on the disease, it may also be expressed per person-weeks or person-months.

As a rate, it reflects the dynamic of the fatality over time, among cases.

To stay with the same example as above, around 1850, most cases of cholera had either recovered after 2 weeks, or had died. Once recovered from the disease, a person is no longer a case. That means that the person time of that person may no longer contribute to the denominator. If we assume that of the 100 cases of cholera, 40 die due to the disease after 2 weeks and the rest (60) recover from the disease after the same amount of time, then the CFR for cholera is in that situation 40 per 200 person-weeks (=1 per 5 person-weeks = 4 per 5 person-months = 10 per 1 person-year).

Likewise, of the 100 newly diagnosed tuberculosis patients, 50 would die in the first year, and 50 would die in the second year. That comes down to a case fatality of 1 per 2 person-years for the first year after diagnosis.

Here we can see the major difference between case fatality and CFR: tuberculosis is the 'greater killer' compared to cholera (because the case fatality is 100%, and of cholera, 'only' 40%). However, M.tuberculosis kills its victims much slower than Vibrio cholerae does.

==Case fatality ratio==
This is simply the comparison of two case fatalities, expressed as a ratio. So the cholera:tuberculosis case fatality ratio is 40:100 (or 4:10). Usually, we put the greater killer first, so the TB: cholera case fatality ratio is 2.5:1. In this sense, it is a comparison between 2 populations, similar to we do with odd ratio, risk ratio, sex ratio etc.

The Case Fatality Ratio could also be used to assess the impact of an intervention. For example, if untreated cholera has a case fatality of 40% and when treatment is given in time, the fatality could be below 1%. This leads to a Case Fatality Ratio of 40 or more when comparing untreated and treated groups of cholera patients.

<div style="display: inline-block; width: 25%; vertical-align: top; border: 1px solid #000; background-color: #d7effc; padding: 10px; margin: 5px;">
'''FEM PAGE CONTRIBUTORS 2007'''
;FEM Editors 2007===
:Sabrina Bacci
;Original Authors===
:Alain Moren
:Marta Valenciano
:Arnold Bosman
;FEM Contributors===
:Lisa Lazareck
:Naomi Boxall
:Arnold Bosman
:Sabrina Bacci
</div>

[[Category:Measures of Disease Occurrence]]

Category:Epidemic intelligence

2026-05-18T13:07:40Z

Bosmana fem:

Epidemic intelligence is the systematic detection, verification, analysis, and communication of information about events that may pose a threat to public health. It integrates two complementary components: indicator-based surveillance and event-based surveillance. Both aim to detect public health threats as early as possible and to monitor known threats until they are resolved. Epidemic intelligence covers both [[Formal Risk Assessment|risk assessment]] and risk monitoring.

== Components of epidemic intelligence ==

'''Indicator-based surveillance''' refers to structured data collected through routine [[surveillance principles|surveillance systems]]. These systems gather predefined indicators on identified risks, emerging risks, and non-human health-related risks (such as environmental or zoonotic factors).

'''[[Event-based surveillance]]''' refers to unstructured information gathered from formal and informal sources, including official reports, the media, scientific publications, and rumours circulating at national or international level.

The two components are complementary. Indicator-based surveillance provides continuous, comparable data on known threats over time. Event-based surveillance enables rapid detection of unexpected or emerging threats that structured systems may miss, often before they are captured by routine reporting.

[[File:eimodelecdc.png|600px|frameless|none|ECDC Epidemic Intelligence model]]

== Signals and events ==

A frequent source of confusion in epidemic intelligence is the distinction between a '''signal''' and an '''event'''.

A '''signal''' is a piece of raw information emerging from screening of formal or informal sources — a news item, a rumour, a hospital alert, an entry in a scientific publication — that ''may'' indicate a public health concern but has not yet been assessed for relevance or accuracy. Signals are abundant, frequently irrelevant, and often unverified.

An '''event''' is a signal (or cluster of signals) that has passed filtering and is judged to be potentially relevant to public health: for example, an unexplained cluster of illness, an unusual outbreak, or a reported hazardous exposure. An event is a hypothesis worth investigating, but it is not yet confirmed.

A '''validated event''' is an event that has been cross-checked against official or otherwise reliable sources and confirmed to be both real and adequately characterised. Only validated events proceed to risk analysis.

In short, the process is a funnel of decreasing volume and increasing confidence: raw inputs are '''screened''' into signals, signals are '''filtered''' into events, and events are '''validated''' before analysis.

== The epidemic intelligence process ==

Epidemic intelligence aims to produce timely, validated, and actionable intelligence on events related to communicable diseases — or to disease of unknown origin — that are of interest to public health authorities. The process can be divided into the early detection of new threats and the monitoring of threats already identified, including potential threats.

=== Early detection ===

Early detection comprises six elements:

# '''Screening''' news, official reports, notes, and rumours from a defined (e.g. European) perspective in order to identify meaningful signals, using predefined criteria.
# '''Filtering''' the signals to retain only those that represent potential public health events of interest.
# '''Validating''' events originating from unofficial sources by cross-checking against official or otherwise reliable sources, to confirm that the event is real and adequately understood.
# '''Analysing''' the validated event to capture the full picture, including epidemiological data, exposure information, and contextual factors.
# '''Assessing''' the risk associated with the event on the basis of the analysis (see [[Formal Risk Assessment|risk assessment]]).
# '''Communicating and documenting''' the identified threats. This element runs throughout the previous five steps and includes dissemination via ad-hoc communications, daily bulletins, and weekly bulletins.

=== Monitoring identified threats ===

Monitoring is the active follow-up of all information directly relevant to an identified threat. It is an iterative process that continues until the threat is considered to have subsided or until all appropriate public health measures have been implemented.

== Practical considerations ==

In a rapidly evolving situation, professional judgement is essential. Under severe time constraints it may be appropriate to skip or compress some of the steps above in order to share information quickly. However, when epidemic intelligence is gathered systematically as described, the outcome is a better-informed decision and more effective action.

<div style="display: inline-block; width: 25%; vertical-align: top; border: 1px solid #000; background-color: #d7effc; padding: 10px; margin: 5px;">
'''FEM PAGE CONTRIBUTORS 2007'''
; Editors
: Vladimir Prikazsky
: Anonymous
: Arnold Bosman
;Contributor
: Arnold Bosman
</div>

[[Category:Assessing the burden of disease and risk assessment]]

Category:Epidemic intelligence

2026-05-18T13:05:44Z

Bosmana fem:

Epidemic intelligence is the systematic detection, verification, analysis, and communication of information about events that may pose a threat to public health. It integrates two complementary components: indicator-based surveillance and event-based surveillance. Both aim to detect public health threats as early as possible and to monitor known threats until they are resolved. Epidemic intelligence covers both [[Formal Risk Assessment|risk assessment]] and risk monitoring.

== Components of epidemic intelligence ==

'''Indicator-based surveillance''' refers to structured data collected through routine [[surveillance principles|surveillance systems]]. These systems gather predefined indicators on identified risks, emerging risks, and non-human health-related risks (such as environmental or zoonotic factors).

'''[[Event-based surveillance]]''' refers to unstructured information gathered from formal and informal sources, including official reports, the media, scientific publications, and rumours circulating at national or international level.

The two components are complementary. Indicator-based surveillance provides continuous, comparable data on known threats over time. Event-based surveillance enables rapid detection of unexpected or emerging threats that structured systems may miss, often before they are captured by routine reporting.

[[File:eimodelecdc.png|600px|frameless|none|ECDC Epidemic Intelligence model]]

== Signals and events ==

A frequent source of confusion in epidemic intelligence is the distinction between a '''signal''' and an '''event'''.

A '''signal''' is a piece of raw information from a formal or informal source — a news item, a rumour, a hospital alert, an entry in a scientific publication — that ''may'' indicate a public health concern but has not yet been assessed for relevance or accuracy. Signals are abundant, frequently irrelevant, and often unverified. Identifying them is essentially a screening task.

An '''event''' is a signal (or cluster of signals) that has passed initial filtering and is judged to be potentially relevant to public health: for example, an unexplained cluster of illness, an unusual outbreak, or a reported hazardous exposure. An event is a hypothesis worth investigating, but it is not yet confirmed.

A '''validated event''' is an event that has been cross-checked against official or otherwise reliable sources and confirmed to be both real and adequately characterised. Only validated events proceed to risk analysis.

In short, the process is a funnel of decreasing volume and increasing confidence: '''signals → events → validated events'''.

== The epidemic intelligence process ==

Epidemic intelligence aims to produce timely, validated, and actionable intelligence on events related to communicable diseases — or to disease of unknown origin — that are of interest to public health authorities. The process can be divided into the early detection of new threats and the monitoring of threats already identified, including potential threats.

=== Early detection ===

Early detection comprises six elements:

# '''Screening''' news, official reports, notes, and rumours from a defined (e.g. European) perspective in order to identify meaningful signals, using predefined criteria.
# '''Filtering''' the signals to retain only those that represent potential public health events of interest.
# '''Validating''' events originating from unofficial sources by cross-checking against official or otherwise reliable sources, to confirm that the event is real and adequately understood.
# '''Analysing''' the validated event to capture the full picture, including epidemiological data, exposure information, and contextual factors.
# '''Assessing''' the risk associated with the event on the basis of the analysis (see [[Formal Risk Assessment|risk assessment]]).
# '''Communicating and documenting''' the identified threats. This element runs throughout the previous five steps and includes dissemination via ad-hoc communications, daily bulletins, and weekly bulletins.

=== Monitoring identified threats ===

Monitoring is the active follow-up of all information directly relevant to an identified threat. It is an iterative process that continues until the threat is considered to have subsided or until all appropriate public health measures have been implemented.

== Practical considerations ==

In a rapidly evolving situation, professional judgement is essential. Under severe time constraints it may be appropriate to skip or compress some of the steps above in order to share information quickly. However, when epidemic intelligence is gathered systematically as described, the outcome is a better-informed decision and more effective action.

<div style="display: inline-block; width: 25%; vertical-align: top; border: 1px solid #000; background-color: #d7effc; padding: 10px; margin: 5px;">
'''FEM PAGE CONTRIBUTORS 2007'''
; Editors
: Vladimir Prikazsky
: Anonymous
: Arnold Bosman
;Contributor
: Arnold Bosman
</div>

[[Category:Assessing the burden of disease and risk assessment]]

Transmissibility

2026-05-18T12:53:26Z

Bosmana fem:

== Infectiousness / Transmissibility ==

'''Infectiousness''' (or '''transmissibility''') refers to the ability of a pathogen or infectious agent to spread from an infected person to others. It quantifies how readily a disease is transmitted within a population and is a fundamental parameter in epidemiological modeling and disease control strategy.

Infectiousness is distinct from '''pathogenicity''' (the capacity to cause disease) and '''virulence''' (the severity of disease caused). A highly infectious disease may be mild, while a severe disease may be poorly transmitted.

== Key Parameters ==

=== Basic Reproduction Number (R₀) ===

The '''basic reproduction number''' (R₀, pronounced "R naught") is one of the most important epidemiological metrics. It represents the average number of secondary infections that result from a single infected individual in a completely susceptible population, assuming no interventions or behavioral changes.

==== Mathematical Definition ====

R₀ is calculated as:

: R₀ = β × c × d

Where:
* '''β''' = transmissibility (probability of transmission per contact)
* '''c''' = contact rate (average number of contacts per unit time)
* '''d''' = duration of infectiousness (average time a person remains contagious)

==== Interpretation ====

* '''R₀ < 1''': The infection will die out naturally; each infected person infects, on average, fewer than one other person
* '''R₀ = 1''': Endemic equilibrium; the infection sustains itself at a stable level
* '''R₀ > 1''': The infection will spread; each infected person infects more than one other person on average
* '''Larger R₀ values''': indicate more readily spreading infections and greater transmission potential

==== Examples of R₀ Values ====

{| class="wikitable sortable"
|-
! Disease !! R₀ Range !! Transmission
|-
| Seasonal influenza || 0.9–2.0 || Moderate
|-
| COVID-19 (original strain) || 2.0–3.0 || Moderate to high
|-
| COVID-19 (Delta variant) || 5–8 || High
|-
| COVID-19 (Omicron variant) || 8–12 || Very high
|-
| Chickenpox || 10–12 || Very high
|-
| Measles || 12–18 || Extremely high
|-
| Smallpox || 5–7 || High
|-
| Polio || 5–7 || High
|-
| HIV/AIDS || 0.5–3 || Low to moderate
|}

=== Effective Reproduction Number (Rₑ or Rₜ) ===

The '''effective reproduction number''' (Rₑ or Rₜ, where t indicates time) represents the average number of secondary infections caused by a single infected individual ''in the current population at a specific time'', accounting for immunity, interventions, behavioral changes, and non-susceptible individuals.

==== Mathematical Definition ====

: Rₑ(t) = R₀ × s(t)

Where:
* '''R₀''' = basic reproduction number
* '''s(t)''' = proportion of the population that is susceptible at time t

==== Key Differences from R₀ ====

{| class="wikitable"
|-
! Aspect !! R₀ !! Rₑ
|-
| Population || Completely susceptible || Current population state
|-
| Immunity || Not accounted for || Accounts for existing immunity
|-
| Interventions || Assumes none || Includes interventions, vaccines, behavior changes
|-
| Time dependence || Constant, intrinsic to pathogen || Changes over time and with circumstances
|-
| Practical use || Theoretical benchmark || Operational monitoring during outbreaks
|}

==== Epidemiological Significance ====

* '''Rₑ < 1''': The outbreak is declining or under control
* '''Rₑ = 1''': Steady-state transmission (cases neither increasing nor decreasing)
* '''Rₑ > 1''': The outbreak is accelerating or expanding

Because Rₑ incorporates real-world conditions, it is the more practically useful metric for monitoring epidemics and evaluating intervention effectiveness.

== Factors Affecting Transmissibility ==

=== Pathogen Characteristics ===

* '''Virus/bacteria shedding rate''': How much pathogen is released by infected individuals
* '''Genetic mutations''': Changes can increase transmissibility (e.g., variant emergence)
* '''Stability in environment''': How long the pathogen survives outside hosts
* '''Infectious period''': The window during which a person can transmit
* '''Asymptomatic transmission''': Capacity to spread before or without causing symptoms

=== Population and Environmental Factors ===

* '''Contact patterns''': Density of population, social structures, frequency of interactions
* '''Hygiene and sanitation''': Hand washing, water quality, sanitation infrastructure
* '''Climate and seasonality''': Temperature, humidity, seasonal behavior changes
* '''Vaccination coverage''': Proportion of population immune through vaccination
* '''Prior infection''': Natural immunity from previous exposure
* '''Age structure''': Different age groups may have different contact patterns and susceptibility
* '''Healthcare access''': Early detection and isolation reduce transmission

=== Behavioral Factors ===

* '''Mobility and travel''': Movement patterns spread pathogens across regions
* '''Social distancing''': Reduces contact rates significantly
* '''Mask usage''': Reduces transmissibility through respiratory droplets
* '''Isolation of sick individuals''': Removes infectious people from contact with others
* '''Risk perception''': Individual behavior changes based on perceived threat

== Measurement and Estimation ==

=== Direct Estimation ===

In an early epidemic with complete contact tracing data:

: R₀ ≈ (average number of secondary cases per infected individual)

=== Statistical Methods ===

* '''Generation time approach''': Uses the serial interval and growth rate
* '''Contact tracing data''': Tracks who infected whom
* '''Maximum likelihood estimation''': Fits epidemic models to observed data
* '''Bayesian methods''': Incorporates uncertainty and prior knowledge

=== Real-Time Estimation ===

Rₑ is estimated during epidemics using:

* '''Case incidence data''': Number of confirmed cases over time
* '''Serial interval''': Average time between case generations
* '''Smoothing techniques''': Account for reporting delays and data variability
* '''Multiple methods''': Cross-checking with different approaches improves reliability

==== Common Estimation Methods ====

* '''Wallinga-Teunis method'''
* '''Cori method'''
* '''Time-dependent methods''' using exponential growth rates

== Herd Immunity Threshold ==

The '''herd immunity threshold''' is the proportion of the population that must be immune (through vaccination or prior infection) to prevent sustained transmission.

: Herd Immunity Threshold = 1 - (1/R₀)

=== Examples ===

{| class="wikitable sortable"
|-
! Disease !! R₀ !! Herd Immunity Threshold
|-
| Seasonal flu || 1.3 || 23%
|-
| COVID-19 (original) || 2.5 || 60%
|-
| COVID-19 (Delta) || 6.5 || 85%
|-
| Measles || 15 || 95%
|-
| Polio || 6 || 83%
|}

When vaccination coverage exceeds this threshold, the disease cannot sustain itself in the population, protecting even those not vaccinated (indirect protection).

== Practical Applications ==

=== Outbreak Response ===

* '''Early assessment''': Initial R₀ estimates inform intervention intensity
* '''Real-time monitoring''': Tracking Rₑ determines if control measures are working
* '''Resource allocation''': Higher R values indicate need for more aggressive response

=== Vaccination Strategy ===

* '''Coverage targets''': Herd immunity threshold guides vaccination campaigns
* '''Booster decisions''': Changing Rₑ indicates when immunity-boosting measures are needed
* '''Variant concerns''': Increased R values prompt vaccine updates

=== Public Health Planning ===

* '''Hospital capacity''': Higher R values predict more cases and healthcare burden
* '''Containment feasibility''': R₀ < 1 suggests containment is possible; large R₀ suggests mitigation focus
* '''Intervention selection''': Different interventions target different components of the R formula

=== Disease Modeling ===

* '''Epidemic projections''': R values predict outbreak trajectory
* '''Intervention scenarios''': Modeling shows how different measures affect R
* '''Long-term planning''': Estimates inform pandemic preparedness

== Limitations and Considerations ==

=== Assumptions and Challenges ===

* '''Homogeneous mixing''': Actual populations have heterogeneous contact patterns (some people have many contacts, others few)
* '''Temporal variation''': Contact patterns and susceptibility change over time
* '''Data quality''': Relies on accurate case counts, testing, and reporting
* '''Reporting delays''': Case reporting lags affect real-time estimates
* '''Uncertainty''': Confidence intervals often wide during uncertainty

=== Heterogeneity ===

Real transmission is not uniformly random:

* '''Super-spreaders''': Some individuals transmit to many more than average
* '''Superspreading events''': Specific circumstances produce disproportionate transmission
* '''Spatial clustering''': Transmission follows geographic and social networks
* '''Age and risk stratification''': Transmission varies by age group and risk profile

=== Methodological Issues ===

* '''Estimation uncertainty''': Different methods may yield different R values
* '''Generational overlap''': Serial intervals difficult to estimate early in epidemics
* '''Incomplete data''': Asymptomatic and undetected cases complicate estimates
* '''Non-stationarity''': Changing conditions violate constant R assumptions

== Historical Context and Evolution ==

The concept of R₀ emerged from mathematical epidemiology in the early 20th century, with major developments by:

* '''William Hamer (1906)''': Formulated the "mass action principle"
* '''Anderson and May (1980s)''': Developed comprehensive R₀ theory in population dynamics
* '''Modern applications''': Real-time R estimation became standard during COVID-19 pandemic

The pandemic demonstrated both the utility and challenges of R-based monitoring, spurring improvements in methodology and real-time estimation techniques.

== See Also ==

* [[Epidemic Curves and Growth Rate]]
* [[Serial Interval and Generation Time]]
* [[Vaccination and Immunization]]
* [[Contact Tracing]]
* [[Mathematical Epidemiology]]
* [[Pathogenicity and Virulence]]

== References ==

* Keeling, M. J., & Rohani, P. (2008). ''Modeling infectious diseases''. Princeton University Press.
* Wallinga, J., & Teunis, P. (2004). Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. ''American Journal of Epidemiology'', 160(6), 509–516.
* Fraser, C. (2007). Estimating individual and household reproduction numbers in an emerging epidemic. ''PLoS Medicine'', 4(7), e300.
* Thompson, R. N., Stockwin, J. E., van Gaalen, R. D., et al. (2019). Improved inference of time-varying reproduction numbers during outbreaks. ''Epidemics'', 29, 100356.

{{Last updated|May 2026}}
{{Status|Complete}}
{{Difficulty level|Intermediate to Advanced}}

[[Category:Epidemiology]]
[[Category:Infectious diseases]]
[[Category:Public health]]
[[Category:Mathematical epidemiology]]

Zoonosis

2026-05-18T12:40:45Z

Bosmana fem: Created page with "== Zoonosis == '''Zoonosis''' (plural: '''zoonoses''') refers to any infectious disease that naturally transmits from animals to humans. The term derives from the Greek words ''zoon'' (animal) and ''nosos'' (disease). Zoonotic diseases represent a major public health burden globally, with approximately 75% of emerging infectious diseases being zoonotic in origin.<ref>Jones, K. E., Patel, N. G., Levy, M. A., et al. (2008). Global trends in emerging infectious diseases. N..."

== Zoonosis ==

'''Zoonosis''' (plural: '''zoonoses''') refers to any infectious disease that naturally transmits from animals to humans. The term derives from the Greek words ''zoon'' (animal) and ''nosos'' (disease). Zoonotic diseases represent a major public health burden globally, with approximately 75% of emerging infectious diseases being zoonotic in origin.<ref>Jones, K. E., Patel, N. G., Levy, M. A., et al. (2008). Global trends in emerging infectious diseases. Nature, 451(7181), 990-993.</ref>

== Definition and Terminology ==

A '''zoonotic disease''' is defined as any disease or infection that is naturally transmissible from animals to humans. The term encompasses:

* '''Direct zoonoses''': Diseases that transmit directly from infected animals to humans
* '''Cyclozoonoses''': Diseases requiring both animals and humans for completion of the parasite lifecycle
* '''Metazoonoses''': Diseases transmitted to humans via insect or animal vectors

The '''zoonotic agent''' (the pathogen causing the disease) may originate from:
* Vertebrate animals (mammals, birds, reptiles, amphibians, fish)
* Invertebrate animals (insects, arachnids, mollusks)
* Environmental sources associated with animal populations

Humans in this context are considered ''accidental hosts'' or ''dead-end hosts'' for many zoonotic pathogens, meaning the pathogen cannot complete its lifecycle in humans alone and typically does not transmit person-to-person.

== Historical Context ==

Zoonotic diseases have shaped human history and civilization:

* '''Ancient period''': Plague, leprosy, and tuberculosis recognized as animal-associated diseases
* '''18th-19th centuries''': Edward Jenner's 1796 vaccination using cowpox (a zoonotic virus) demonstrated the principle of cross-species pathogen use for medical benefit
* '''20th century''': Recognition of zoonotic origins of influenza (1918 pandemic), HIV/AIDS (from non-human primates), and many others
* '''21st century''': High-profile zoonotic pandemics including SARS (2003), H1N1 influenza (2009), Ebola (2014-2016), Zika (2015-2016), and COVID-19 (2019-present)

The study of zoonoses became formalized with the concept of ''One Health'', recognizing the interconnection between animal health, human health, and environmental health.

== Classification ==

=== By Transmission Route ===

'''Direct transmission'''
* Bites and scratches (rabies from bats, raccoons)
* Contact with bodily fluids (Ebola, Marburg)
* Inhalation of aerosolized particles (tuberculosis from cattle, Q fever)
* Ingestion of contaminated food or water (salmonellosis, brucellosis)
* Skin contact (anthrax, ringworm)

'''Vector-mediated transmission'''
* Arthropod vectors: mosquitoes (dengue, yellow fever, Zika), ticks (Lyme disease, tick-borne encephalitis), fleas (plague), flies (sleeping sickness)
* Animal intermediate hosts: snails (schistosomiasis), crustaceans (paragonimiasis)

'''Environmental transmission'''
* Spores from soil or animal excreta (coccidioidomycosis, histoplasmosis)
* Contaminated environmental surfaces

=== By Epidemiological Pattern ===

'''Sporadic zoonoses'''
* Occur irregularly and unpredictably
* Examples: rabies, anthrax, most vector-borne diseases in non-endemic areas

'''Endemic zoonoses'''
* Maintained in animal populations with consistent human cases
* Examples: brucellosis in livestock regions, toxoplasmosis worldwide

'''Epidemic zoonoses'''
* Sudden increase in cases, often following animal-human contact changes or environmental disruption
* Examples: SARS, COVID-19, Nipah virus outbreaks

== Examples of Important Zoonotic Diseases ==

{| class="wikitable sortable"
|-
! Disease !! Zoonotic Agent !! Primary Animal Source !! Transmission Route !! Geographic Distribution
|-
| [[Rabies]] || Lyssavirus || Bats, dogs, raccoons, skunks || Bite wounds, saliva contact || Worldwide
|-
| [[Influenza]] || Influenza A virus || Birds, pigs || Respiratory droplets || Worldwide, seasonal
|-
| [[Tuberculosis]] || ''Mycobacterium bovis'' || Cattle, wildlife || Respiratory, ingestion || Worldwide
|-
| [[Brucellosis]] || ''Brucella'' species || Cattle, sheep, goats, pigs || Contact with tissues, unpasteurized dairy || Mediterranean, Middle East, parts of Asia
|-
| [[Plague]] || ''Yersinia pestis'' || Rodents || Flea bites || Central Asia, parts of Africa
|-
| [[Salmonellosis]] || ''Salmonella'' species || Poultry, reptiles, amphibians || Ingestion of contaminated food || Worldwide
|-
| [[Lyme disease]] || ''Borrelia burgdorferi'' || Deer, small mammals || Tick bite || Northern Hemisphere temperate regions
|-
| [[Dengue fever]] || Dengue virus || Monkeys, mosquitoes || Mosquito bite (''Aedes'' species) || Tropical/subtropical regions
|-
| [[Ebola virus disease]] || Ebola virus || Fruit bats, primates || Contact with blood/body fluids || Sub-Saharan Africa
|-
| [[Toxoplasmosis]] || ''Toxoplasma gondii'' || Cats, other animals || Ingestion of oocysts || Worldwide
|-
| [[Avian influenza]] || Influenza A virus (H5N1, H7N9, etc.) || Birds || Contact with infected birds || Worldwide in wild birds
|-
| [[Nipah virus infection]] || Nipah virus || Fruit bats, pigs || Contact with secretions, respiratory || Southeast Asia
|-
| [[HIV/AIDS]] || HIV || Non-human primates (SIV) || Contact with blood/body fluids || Worldwide, originated in Central Africa
|-
| [[SARS]] || SARS-CoV || Civets, bats || Respiratory droplets || Primarily East/Southeast Asia
|-
| [[COVID-19]] || SARS-CoV-2 || Bats (proposed), possibly intermediate hosts || Respiratory droplets || Worldwide pandemic
|}

== Risk Factors for Zoonotic Disease Transmission ==

=== Animal-Related Factors ===

* '''Pathogen prevalence in animal populations''': Higher infection rates in reservoir species increase transmission risk
* '''Animal reservoir size''': Larger populations support sustained pathogen maintenance
* '''Animal behavior''': Nocturnal animals reduce incidental human contact; animals entering human habitats increase risk
* '''Wildlife-livestock interface''': Contact between wild and domestic animals facilitates spillover
* '''Animal trade and transport''': Movement of infected animals to new geographic areas

=== Human Behavioral Factors ===

* '''Occupational exposure''': Hunters, farmers, veterinarians, laboratory workers, wildlife handlers
* '''Food practices''': Hunting and consuming bushmeat, consuming raw/undercooked meat, consuming unpasteurized dairy
* '''Pet ownership''': Close contact with companion animals
* '''Ecological practices''': Deforestation, habitat destruction that brings humans and wildlife into contact
* '''Travel and trade''': Movement to regions with endemic zoonoses or importation of infected animals/products

=== Environmental and Social Factors ===

* '''Ecological disruption''': Land-use changes, deforestation increase wildlife contact
* '''Climate change''': Alters animal distributions and breeding cycles, expanding vector ranges
* '''Socioeconomic status''': Limited access to healthcare, sanitation, and preventive measures
* '''Urbanization''': Dense human populations in proximity to wildlife reservoirs
* '''Armed conflict''': Disrupts disease surveillance and containment systems

== Mechanisms of Spillover ==

Zoonotic disease emergence typically involves several steps:

# '''Exposure''': Humans contact infected animals or contaminated environments
# '''Infection''': Pathogen infects human host cells
# '''Establishment''': Pathogen replicates and establishes infection
# '''Transmission''': Infected human transmits to other humans (not required for all zoonoses)
# '''Adaptation''': Pathogen evolves to become more suited to human hosts (occurs over multiple generations)

The [[basic reproduction number]] (R₀) in animals versus humans determines whether a zoonotic disease can sustain human-to-human transmission chains and cause epidemics.

== Public Health Significance ==

=== Disease Burden ===

* Approximately 60% of known human infectious diseases are zoonotic
* Approximately 75% of emerging infectious diseases have animal origins
* Annual economic losses from zoonotic disease and zoonotic animal disease exceed $220 billion USD globally<ref>Burns, A., van Panhuis, W., & Sazawal, S. (2016). The hidden pandemic of animal diseases. Chatham House Review.</ref>

=== Pandemic Risk ===

Zoonotic pathogens account for most pandemic threats:
* 1918 H1N1 influenza pandemic: ~50-100 million deaths
* 2009 H1N1 influenza pandemic: ~100,000-400,000 deaths
* HIV/AIDS pandemic: >40 million deaths to date
* COVID-19 pandemic: >7 million deaths (official count, likely underestimated)

=== Healthcare Burden ===

* Increased hospitalizations and healthcare costs
* Potential for rapid spread in healthcare settings
* Challenges to diagnostic and treatment infrastructure

== Prevention and Control ==

=== Individual-Level Measures ===

* '''Hand hygiene''': Regular handwashing, especially after animal contact
* '''Food safety''': Thorough cooking of meat, pasteurization of dairy
* '''Protective equipment''': Appropriate use of gloves, masks, and clothing in occupational settings
* '''Vaccination''': Where available (rabies post-exposure prophylaxis, avian influenza vaccines)
* '''Vector control''': Insect repellents, bed nets, permethrin-treated clothing against arthropods
* '''Animal precautions''': Avoiding wild animals, safe handling of pets

=== Community-Level Measures ===

* '''Disease surveillance''': Early detection of zoonotic disease cases and animal disease outbreaks
* '''Contact tracing''': Identification of exposed individuals
* '''Isolation and quarantine''': Separating infected individuals
* '''Vector control programs''': Mosquito spraying, tick control in endemic areas
* '''Public education''': Communication about zoonotic risks and prevention

=== Population-Level Measures ===

* '''One Health approach''': Coordinated efforts across human health, animal health, and environmental sectors
* '''Wildlife management''': Population control of reservoir species where appropriate
* '''Habitat modification''': Environmental management to reduce human-wildlife contact
* '''Biosecurity''': Controls on animal movement and trade
* '''Regulatory frameworks''': Laws governing bushmeat trade, pet trade, animal agriculture practices
* '''Climate adaptation''': Strategies to address climate-driven changes in disease distribution

=== Healthcare System Preparedness ===

* '''Diagnostic capacity''': Ability to rapidly identify zoonotic pathogens
* '''Infection control''': Protocols for managing zoonotic disease patients
* '''Supply stockpiles''': Maintenance of personal protective equipment and medical countermeasures
* '''Healthcare worker training''': Recognition and management of zoonotic diseases
* '''Research and development''': Investment in vaccines, therapeutics, and diagnostics

== Emerging Zoonotic Diseases ==

=== Factors Driving Emergence ===

* '''Ecological changes''': Habitat loss, climate change, agricultural intensification
* '''Demographic change''': Growing human population, urbanization
* '''Behavioral change''': Consumption of novel animal foods, increased wildlife trade
* '''Technological change''': Increased travel and connectivity facilitates spread
* '''Pathogen evolution''': Natural mutation and selection of viral and bacterial populations

=== Notable Emerging Zoonoses ===

* '''Henipavirus infections''' (Nipah, Hendra): Spillover from fruit bats through intermediate animal hosts
* '''Filovirus infections''' (Ebola, Marburg): Fruit bat-to-human spillover with secondary transmission
* '''SARS and COVID-19''': Suspected bat coronavirus origin with potential intermediate hosts
* '''Antimicrobial-resistant bacteria''': Including methicillin-resistant ''Staphylococcus aureus'' (MRSA) from livestock
* '''Novel arboviruses''': Zika, chikungunya expanding into new regions due to vector range expansion

== The One Health Approach ==

The '''One Health''' framework recognizes that human health, animal health, plant health, and environmental health are interconnected. This integrated approach to zoonotic disease:

* Involves collaboration between human medicine, veterinary medicine, and environmental sciences
* Addresses shared infectious disease threats at the human-animal-environment interface
* Promotes cross-sector communication and coordinated surveillance
* Advocates for sustainable food production and wildlife management
* Seeks to balance economic development with disease prevention

Organizations implementing One Health include [[World Health Organization|WHO]], [[Food and Agriculture Organization|FAO]], [[World Organisation for Animal Health|OIE]], [[United Nations Environment Programme|UNEP]], and national governments.

== Controversies and Debates ==

=== Origins of Pandemic Pathogens ===

The animal origin of certain pathogens, particularly SARS-CoV-2, remains contested. Key debates include:

* Evidence for and against animal spillover origins
* The role of intermediate hosts in spillover events
* The potential for laboratory accidents in pathogen transmission
* Geographic location of initial spillover events

=== Balancing Wildlife Conservation and Disease Control ===

* Tension between protection of endangered species and disease control measures
* Questions about when and how to manage wildlife populations
* Ethics of culling animals to prevent disease spillover

=== Animal Agriculture and Zoonotic Disease ===

* Factory farming practices and antimicrobial resistance
* Regulation of livestock production systems
* Trade-offs between food security and disease prevention

== See Also ==

* [[Pandemic|Pandemics]]
* [[Emerging infectious disease]]
* [[Vector-borne disease]]
* [[Reservoir (epidemiology)]]
* [[One Health]]
* [[Spillover (disease)]]
* [[Wildlife disease]]
* [[Antimicrobial resistance]]
* [[Food safety]]
* [[Public health]]

== References ==

<references />

== Further Reading ==

* Jones, B. A., Grace, D., Kock, R., et al. (2013). Zoonosis emergence linked to agricultural intensification and environmental change. ''Proceedings of the National Academy of Sciences'', 110(21), 8399-8404.
* Morse, S. S., Mazet, J. A., Woolhouse, M., et al. (2012). Prediction and prevention of the next pandemic zoonosis. ''The Lancet'', 380(9857), 1956-1965.
* Woolhouse, M. E., & Gowtage-Sequeria, S. (2005). Host range and emerging and reemerging pathogens. ''Emerging Infectious Diseases'', 11(12), 1842-1847.
* World Health Organization. (2020). Antimicrobial resistance: Global Report on Surveillance. WHO Publications.

== External Links ==

* [https://www.who.int/news-room/fact-sheets/detail/zoonoses WHO Fact Sheet on Zoonoses]
* [https://www.cdc.gov/onehealth/index.html CDC One Health Page]
* [https://www.oie.int/ World Organisation for Animal Health]

{{Categories|Epidemiology|Infectious diseases|Public health|Zoonosis|One Health}}

__NOTOC__
__FORCETOC__

Stage 2: Systematically collecting information

2026-05-18T12:39:21Z

Bosmana fem: /* Checklist 2: Basic disease information/determinants */

Identify basic facts about the disease and the aetiological agent from a standard reference text (ideally less than five years old). Examples include infectious disease textbooks such as: Heymann; Mandell; Topley and Wilson; Fields Virology (see references). There will be other key reference texts, including previous outbreaks and incidents,depending on the country and the disease. Sources on evidence-based medicine (see Appendix 3) are useful for checking what has already been done and to ensure that work is not repeated. Expertise on choosing reliablesources of information, such as bibliographic databases, websites and/or grey literature sources and advice on access to the full texts are usually available within Member States’ institution libraries.

=Checklist 2: Basic disease information/determinants=
* Occurrence: time, place and person
** Geographical distribution: is disease endemic in country?
** If not, what are routes of introduction, e.g. food/bird/animal/human?
** Seasonal/temporal trends
* [[Reservoir for infectious agents|Reservoir]] (if [[Zoonosis|zoonotic]], which species affected – will animals be symptomatic?)
* Susceptibility: are specific risk groups at increased risk of exposure/infection, e.g.:
** specific age groups (e.g. children, elderly);
** occupational groups;
** travellers;
** those with impaired immunity, e.g. immunosupression/chronic disease; pregnant women;
** others, e.g. as a result of specific recreational or other activities.
* Infectiousness
** Mode of transmission
** Incubation period
** Period of communicability
** Length of asymptomatic infection
** Reproductive rate
* Clinical presentation and outcome
** Disease severity: morbidity; mortality; case fatality
** Complications/sequelae
** Are specific risk groups at increased risk of severe disease/complications (consider children, elderly, those with immunosupression/chronic disease, pregnant women, occupational/recreational risks)
* Laboratory investigation and diagnosis
** Laboratory tests available
** Test specifications (sensitivity, specificity, PPV, quality assurance) and limitations (crossreactivity, biosafety concern)
* Treatment and control measures
** Treatment (efficacy?)
** Prophylaxis (vaccination/other)
** Other control measures (e.g. quarantine, withdrawal of food product, culling animals)
* Previous outbreaks/incidents
** Novel transmission routes

Basic disease information from standard textbooks should be supplemented by searching published and grey literature (including outbreak reports and surveillance data, guidelines, disease fact sheets, etc.). “A literature search should be a well-thought-out and organized search for all relevant literature published on a topic and is the most effective and efficient way to locate sound evidence on a subject.” (see http://www.nursingtimes.net/nursing-practice/217252.article). When time and resources are limited, a preliminary literature search should be undertaken to identify the key literature in the subject area. However, there will inevitably be a trade-off between time and sensitivity. Particular attention should be given to filtering the results,i.e. choice of subjects, timeframe, and restricting to ‘review’ articles – most citation databases offer the facility to filter searches in this way. A trained information specialist or librarian can help to identify the best way to use these options in databases and retrieve the appropriate records according to the questions. There are also sites with tutorials and guides providing help with the literature search, such as the London School of Hygiene & Tropical Medicine Library (see http://www.lshtm.ac.uk/library/help/help.html for further information). It should be acknowledged that a comprehensive systematic review will not be possible in the early stages of a rapid risk assessment; however, the need for such a review should be considered later when time and resources permit.

=Published literature=
The key steps in an effective literature search include:

* Clearly defining the question(s) and the type of information needed (e.g. type of studies searching for, any geographical/ethic/age limits)
* Database(s) to be searched – Pubmed/Medline is universally available and access to Cochrane Library may also be free depending on the country agreement (http://www.thecochranelibrary.com/view/0/FreeAccess.html). There are a range of citation databases that may also be used including Scopus, Web of Science, Google Scholar. For other databases, such Embase, which is specific to health, a subscription is needed. These databases vary in accessibility, geographical coverage, range and type of content (e.g. coverage of low-impact journals and conference proceedings). Ideally, more than one database should be searched and the results of each compared, however this is rarely practical in view of time restraints. It may be better to become proficient in using one database so that when an incident occurs a rapid literature search can be conducted. For further information see http://www.lshtm.ac.uk/library/help/choosingdbs.pdf.
* Selection of search terms – text words and/or MeSH headings (best to use both if time permits).
* Compiling search strategy and running the search – including use of Boolean operators (AND/OR).
* Documenting search strategy and results.
Full articles should be used wherever possible rather than abstracts.

Further resources for effective literature searching are listed in the references (e.g. http://www.lshtm.ac.uk/library/help/help.html#resources). Member States public health services will often have their own resources and guides to doing literature searches.

=Grey literature=
These include key electronic publications such as ProMED and websites of national and international public health bodies (for outbreak reports and disease information). A list of suggested sources is included in Appendix 3. It will not be practical (or relevant) to search all of these in the early stages of a rapid risk assessment; however, as a minimum, the following should be searched:

* Electronic publications, e.g., ProMED and WHO Disease Outbreak News, for outbreak reports.
* Key websites of the relevant national and international public health bodies to identify further disease information, guidelines, surveillance information, etc.
* Additional outbreak reports may be available on the IHR and EWRS websites (restricted access) and can be identified through the relevant IHR NFP and EWRS NCP.

=References=
Entire text copied from:
* European Centre for Disease Prevention and Control. Operational guidance on rapid risk assessment methodology. Stockholm: ECDC; 2011. ISBN 978-92-9193-306-8 doi 10.2900/57509

[[Category:Rapid Risk Assessment]]

Stage 2: Systematically collecting information

2026-05-18T12:38:40Z

Bosmana fem: /* Checklist 2: Basic disease information/determinants */

Identify basic facts about the disease and the aetiological agent from a standard reference text (ideally less than five years old). Examples include infectious disease textbooks such as: Heymann; Mandell; Topley and Wilson; Fields Virology (see references). There will be other key reference texts, including previous outbreaks and incidents,depending on the country and the disease. Sources on evidence-based medicine (see Appendix 3) are useful for checking what has already been done and to ensure that work is not repeated. Expertise on choosing reliablesources of information, such as bibliographic databases, websites and/or grey literature sources and advice on access to the full texts are usually available within Member States’ institution libraries.

=Checklist 2: Basic disease information/determinants=
* Occurrence: time, place and person
** Geographical distribution: is disease endemic in country?
** If not, what are routes of introduction, e.g. food/bird/animal/human?
** Seasonal/temporal trends
* [[Reservoir for infectious agents|Reservoir]] (if zoonotic, which species affected – will animals be symptomatic?)
* Susceptibility: are specific risk groups at increased risk of exposure/infection, e.g.:
** specific age groups (e.g. children, elderly);
** occupational groups;
** travellers;
** those with impaired immunity, e.g. immunosupression/chronic disease; pregnant women;
** others, e.g. as a result of specific recreational or other activities.
* Infectiousness
** Mode of transmission
** Incubation period
** Period of communicability
** Length of asymptomatic infection
** Reproductive rate
* Clinical presentation and outcome
** Disease severity: morbidity; mortality; case fatality
** Complications/sequelae
** Are specific risk groups at increased risk of severe disease/complications (consider children, elderly, those with immunosupression/chronic disease, pregnant women, occupational/recreational risks)
* Laboratory investigation and diagnosis
** Laboratory tests available
** Test specifications (sensitivity, specificity, PPV, quality assurance) and limitations (crossreactivity, biosafety concern)
* Treatment and control measures
** Treatment (efficacy?)
** Prophylaxis (vaccination/other)
** Other control measures (e.g. quarantine, withdrawal of food product, culling animals)
* Previous outbreaks/incidents
** Novel transmission routes

Basic disease information from standard textbooks should be supplemented by searching published and grey literature (including outbreak reports and surveillance data, guidelines, disease fact sheets, etc.). “A literature search should be a well-thought-out and organized search for all relevant literature published on a topic and is the most effective and efficient way to locate sound evidence on a subject.” (see http://www.nursingtimes.net/nursing-practice/217252.article). When time and resources are limited, a preliminary literature search should be undertaken to identify the key literature in the subject area. However, there will inevitably be a trade-off between time and sensitivity. Particular attention should be given to filtering the results,i.e. choice of subjects, timeframe, and restricting to ‘review’ articles – most citation databases offer the facility to filter searches in this way. A trained information specialist or librarian can help to identify the best way to use these options in databases and retrieve the appropriate records according to the questions. There are also sites with tutorials and guides providing help with the literature search, such as the London School of Hygiene & Tropical Medicine Library (see http://www.lshtm.ac.uk/library/help/help.html for further information). It should be acknowledged that a comprehensive systematic review will not be possible in the early stages of a rapid risk assessment; however, the need for such a review should be considered later when time and resources permit.

=Published literature=
The key steps in an effective literature search include:

* Clearly defining the question(s) and the type of information needed (e.g. type of studies searching for, any geographical/ethic/age limits)
* Database(s) to be searched – Pubmed/Medline is universally available and access to Cochrane Library may also be free depending on the country agreement (http://www.thecochranelibrary.com/view/0/FreeAccess.html). There are a range of citation databases that may also be used including Scopus, Web of Science, Google Scholar. For other databases, such Embase, which is specific to health, a subscription is needed. These databases vary in accessibility, geographical coverage, range and type of content (e.g. coverage of low-impact journals and conference proceedings). Ideally, more than one database should be searched and the results of each compared, however this is rarely practical in view of time restraints. It may be better to become proficient in using one database so that when an incident occurs a rapid literature search can be conducted. For further information see http://www.lshtm.ac.uk/library/help/choosingdbs.pdf.
* Selection of search terms – text words and/or MeSH headings (best to use both if time permits).
* Compiling search strategy and running the search – including use of Boolean operators (AND/OR).
* Documenting search strategy and results.
Full articles should be used wherever possible rather than abstracts.

Further resources for effective literature searching are listed in the references (e.g. http://www.lshtm.ac.uk/library/help/help.html#resources). Member States public health services will often have their own resources and guides to doing literature searches.

=Grey literature=
These include key electronic publications such as ProMED and websites of national and international public health bodies (for outbreak reports and disease information). A list of suggested sources is included in Appendix 3. It will not be practical (or relevant) to search all of these in the early stages of a rapid risk assessment; however, as a minimum, the following should be searched:

* Electronic publications, e.g., ProMED and WHO Disease Outbreak News, for outbreak reports.
* Key websites of the relevant national and international public health bodies to identify further disease information, guidelines, surveillance information, etc.
* Additional outbreak reports may be available on the IHR and EWRS websites (restricted access) and can be identified through the relevant IHR NFP and EWRS NCP.

=References=
Entire text copied from:
* European Centre for Disease Prevention and Control. Operational guidance on rapid risk assessment methodology. Stockholm: ECDC; 2011. ISBN 978-92-9193-306-8 doi 10.2900/57509

[[Category:Rapid Risk Assessment]]

Stage 2: Systematically collecting information

2026-05-18T12:38:20Z

Bosmana fem:

Identify basic facts about the disease and the aetiological agent from a standard reference text (ideally less than five years old). Examples include infectious disease textbooks such as: Heymann; Mandell; Topley and Wilson; Fields Virology (see references). There will be other key reference texts, including previous outbreaks and incidents,depending on the country and the disease. Sources on evidence-based medicine (see Appendix 3) are useful for checking what has already been done and to ensure that work is not repeated. Expertise on choosing reliablesources of information, such as bibliographic databases, websites and/or grey literature sources and advice on access to the full texts are usually available within Member States’ institution libraries.

=Checklist 2: Basic disease information/determinants=
* Occurrence: time, place and person
** Geographical distribution: is disease endemic in country?
** If not, what are routes of introduction, e.g. food/bird/animal/human?
** Seasonal/temporal trends
* [[Reservoir|Reservoir for infectious agents]] (if zoonotic, which species affected – will animals be symptomatic?)
* Susceptibility: are specific risk groups at increased risk of exposure/infection, e.g.:
** specific age groups (e.g. children, elderly);
** occupational groups;
** travellers;
** those with impaired immunity, e.g. immunosupression/chronic disease; pregnant women;
** others, e.g. as a result of specific recreational or other activities.
* Infectiousness
** Mode of transmission
** Incubation period
** Period of communicability
** Length of asymptomatic infection
** Reproductive rate
* Clinical presentation and outcome
** Disease severity: morbidity; mortality; case fatality
** Complications/sequelae
** Are specific risk groups at increased risk of severe disease/complications (consider children, elderly, those with immunosupression/chronic disease, pregnant women, occupational/recreational risks)
* Laboratory investigation and diagnosis
** Laboratory tests available
** Test specifications (sensitivity, specificity, PPV, quality assurance) and limitations (crossreactivity, biosafety concern)
* Treatment and control measures
** Treatment (efficacy?)
** Prophylaxis (vaccination/other)
** Other control measures (e.g. quarantine, withdrawal of food product, culling animals)
* Previous outbreaks/incidents
** Novel transmission routes

Basic disease information from standard textbooks should be supplemented by searching published and grey literature (including outbreak reports and surveillance data, guidelines, disease fact sheets, etc.). “A literature search should be a well-thought-out and organized search for all relevant literature published on a topic and is the most effective and efficient way to locate sound evidence on a subject.” (see http://www.nursingtimes.net/nursing-practice/217252.article). When time and resources are limited, a preliminary literature search should be undertaken to identify the key literature in the subject area. However, there will inevitably be a trade-off between time and sensitivity. Particular attention should be given to filtering the results,i.e. choice of subjects, timeframe, and restricting to ‘review’ articles – most citation databases offer the facility to filter searches in this way. A trained information specialist or librarian can help to identify the best way to use these options in databases and retrieve the appropriate records according to the questions. There are also sites with tutorials and guides providing help with the literature search, such as the London School of Hygiene & Tropical Medicine Library (see http://www.lshtm.ac.uk/library/help/help.html for further information). It should be acknowledged that a comprehensive systematic review will not be possible in the early stages of a rapid risk assessment; however, the need for such a review should be considered later when time and resources permit.

=Published literature=
The key steps in an effective literature search include:

* Clearly defining the question(s) and the type of information needed (e.g. type of studies searching for, any geographical/ethic/age limits)
* Database(s) to be searched – Pubmed/Medline is universally available and access to Cochrane Library may also be free depending on the country agreement (http://www.thecochranelibrary.com/view/0/FreeAccess.html). There are a range of citation databases that may also be used including Scopus, Web of Science, Google Scholar. For other databases, such Embase, which is specific to health, a subscription is needed. These databases vary in accessibility, geographical coverage, range and type of content (e.g. coverage of low-impact journals and conference proceedings). Ideally, more than one database should be searched and the results of each compared, however this is rarely practical in view of time restraints. It may be better to become proficient in using one database so that when an incident occurs a rapid literature search can be conducted. For further information see http://www.lshtm.ac.uk/library/help/choosingdbs.pdf.
* Selection of search terms – text words and/or MeSH headings (best to use both if time permits).
* Compiling search strategy and running the search – including use of Boolean operators (AND/OR).
* Documenting search strategy and results.
Full articles should be used wherever possible rather than abstracts.

Further resources for effective literature searching are listed in the references (e.g. http://www.lshtm.ac.uk/library/help/help.html#resources). Member States public health services will often have their own resources and guides to doing literature searches.

=Grey literature=
These include key electronic publications such as ProMED and websites of national and international public health bodies (for outbreak reports and disease information). A list of suggested sources is included in Appendix 3. It will not be practical (or relevant) to search all of these in the early stages of a rapid risk assessment; however, as a minimum, the following should be searched:

* Electronic publications, e.g., ProMED and WHO Disease Outbreak News, for outbreak reports.
* Key websites of the relevant national and international public health bodies to identify further disease information, guidelines, surveillance information, etc.
* Additional outbreak reports may be available on the IHR and EWRS websites (restricted access) and can be identified through the relevant IHR NFP and EWRS NCP.

=References=
Entire text copied from:
* European Centre for Disease Prevention and Control. Operational guidance on rapid risk assessment methodology. Stockholm: ECDC; 2011. ISBN 978-92-9193-306-8 doi 10.2900/57509

[[Category:Rapid Risk Assessment]]

Transmissibility

2026-05-18T12:25:19Z

Bosmana fem: /* Infectiousness / Transmissibility */

= Infectiousness / Transmissibility =
==Definition==
Infectiousness (or transmissibility) refers to the ability of a pathogen or infectious agent to spread from an infected person to others. It quantifies how readily a disease is transmitted within a population and is a fundamental parameter in epidemiological modeling and disease control strategy.
Infectiousness is distinct from pathogenicity (the capacity to cause disease) and virulence (the severity of disease caused). A highly infectious disease may be mild, while a severe disease may be poorly transmitted.
==Key Parameters==
===Basic Reproduction Number (R₀)===
The basic reproduction number (R₀, pronounced "R naught") is one of the most important epidemiological metrics. It represents the average number of secondary infections that result from a single infected individual in a completely susceptible population, assuming no interventions or behavioral changes.
====Mathematical Definition====
R₀ is calculated as:
R₀ = β × c × d
Where:
* β = transmissibility (probability of transmission per contact)
* c = contact rate (average number of contacts per unit time)
* d = duration of infectiousness (average time a person remains contagious)
Interpretation
* R₀ < 1: The infection will die out naturally; each infected person infects, on average, fewer than one other person
* R₀ = 1: Endemic equilibrium; the infection sustains itself at a stable level
* R₀ > 1: The infection will spread; each infected person infects more than one other person on average
* Larger R₀ values indicate more readily spreading infections and greater transmission potential
====Examples of R₀ Values====
Disease R₀ Range Transmission
Seasonal influenza 0.9–2.0 Moderate
COVID-19 (original strain) 2.0–3.0 Moderate to high
COVID-19 (Delta variant) 5–8 High
COVID-19 (Omicron variant) 8–12 Very high
Chickenpox 10–12 Very high
Measles 12–18 Extremely high
Smallpox 5–7 High
Polio 5–7 High
HIV/AIDS 0.5–3 Low to moderate

===Effective Reproduction Number (Rₑ or Rₜ)===
The effective reproduction number (Rₑ or Rₜ, where t indicates time) represents the average number of secondary infections caused by a single infected individual in the current population at a specific time, accounting for immunity, interventions, behavioral changes, and non-susceptible individuals.
====Mathematical Definition====
Rₑ(t) = R₀ × s(t)
Where:
* R₀ = basic reproduction number
* s(t) = proportion of the population that is susceptible at time t
====Key Differences from R₀====
Aspect R₀ Rₑ
Population Completely susceptible Current population state
Immunity Not accounted for Accounts for existing immunity
Interventions Assumes none Includes interventions, vaccines, behavior changes
Time dependence Constant, intrinsic to pathogen Changes over time and with circumstances
Practical use Theoretical benchmark Operational monitoring during outbreaks

===Epidemiological Significance===
* Rₑ < 1: The outbreak is declining or under control
* Rₑ = 1: Steady-state transmission (cases neither increasing nor decreasing)
* Rₑ > 1: The outbreak is accelerating or expanding
Because Rₑ incorporates real-world conditions, it is the more practically useful metric for monitoring epidemics and evaluating intervention effectiveness.
==Factors Affecting Transmissibility==
====Pathogen Characteristics====
* Virus/bacteria shedding rate: How much pathogen is released by infected individuals
* Genetic mutations: Changes can increase transmissibility (e.g., variant emergence)
* Stability in environment: How long the pathogen survives outside hosts
* Infectious period: The window during which a person can transmit
* Asymptomatic transmission: Capacity to spread before or without causing symptoms
====Population and Environmental Factors====
* Contact patterns: Density of population, social structures, frequency of interactions
* Hygiene and sanitation: Hand washing, water quality, sanitation infrastructure
* Climate and seasonality: Temperature, humidity, seasonal behavior changes
* Vaccination coverage: Proportion of population immune through vaccination
* Prior infection: Natural immunity from previous exposure
* Age structure: Different age groups may have different contact patterns and susceptibility
* Healthcare access: Early detection and isolation reduce transmission
====Behavioral Factors====
* Mobility and travel: Movement patterns spread pathogens across regions
* Social distancing: Reduces contact rates significantly
* Mask usage: Reduces transmissibility through respiratory droplets
* Isolation of sick individuals: Removes infectious people from contact with others
* Risk perception: Individual behavior changes based on perceived threat
==Measurement and Estimation==
===Direct Estimation===
In an early epidemic with complete contact tracing data:
R₀ ≈ (average number of secondary cases per infected individual)
====Statistical Methods====
* Generation time approach: Uses the serial interval and growth rate
* Contact tracing data: Tracks who infected whom
* Maximum likelihood estimation: Fits epidemic models to observed data
* Bayesian methods: Incorporates uncertainty and prior knowledge
===Real-Time Estimation===
Rₑ is estimated during epidemics using:
* Case incidence data: Number of confirmed cases over time
* Serial interval: Average time between case generations
* Smoothing techniques: Account for reporting delays and data variability
* Multiple methods: Cross-checking with different approaches improves reliability
====Common Estimation Methods====
* Wallinga-Teunis method
* Cori method
* Time-dependent methods using exponential growth rates
==Herd Immunity Threshold==
The herd immunity threshold is the proportion of the population that must be immune (through vaccination or prior infection) to prevent sustained transmission.
Herd Immunity Threshold = 1 - (1/R₀)
==Examples==
Disease R₀ Herd Immunity Threshold
Seasonal flu 1.3 23%
COVID-19 (original) 2.5 60%
COVID-19 (Delta) 6.5 85%
Measles 15 95%
Polio 6 83%

When vaccination coverage exceeds this threshold, the disease cannot sustain itself in the population, protecting even those not vaccinated (indirect protection).
==Practical Applications==
Outbreak Response
* Early assessment: Initial R₀ estimates inform intervention intensity
* Real-time monitoring: Tracking Rₑ determines if control measures are working
* Resource allocation: Higher R values indicate need for more aggressive response
Vaccination Strategy
* Coverage targets: Herd immunity threshold guides vaccination campaigns
* Booster decisions: Changing Rₑ indicates when immunity-boosting measures are needed
* Variant concerns: Increased R values prompt vaccine updates
Public Health Planning
* Hospital capacity: Higher R values predict more cases and healthcare burden
* Containment feasibility: R₀ < 1 suggests containment is possible; large R₀ suggests mitigation focus
* Intervention selection: Different interventions target different components of the R formula
Disease Modeling
* Epidemic projections: R values predict outbreak trajectory
* Intervention scenarios: Modeling shows how different measures affect R
* Long-term planning: Estimates inform pandemic preparedness
==Limitations and Considerations==
Assumptions and Challenges
* Homogeneous mixing: Actual populations have heterogeneous contact patterns (some people have many contacts, others few)
* Temporal variation: Contact patterns and susceptibility change over time
* Data quality: Relies on accurate case counts, testing, and reporting
* Reporting delays: Case reporting lags affect real-time estimates
* Uncertainty: Confidence intervals often wide during uncertainty
Heterogeneity
Real transmission is not uniformly random:
* Super-spreaders: Some individuals transmit to many more than average
* Superspreading events: Specific circumstances produce disproportionate transmission
* Spatial clustering: Transmission follows geographic and social networks
* Age and risk stratification: Transmission varies by age group and risk profile
Methodological Issues
* Estimation uncertainty: Different methods may yield different R values
* Generational overlap: Serial intervals difficult to estimate early in epidemics
* Incomplete data: Asymptomatic and undetected cases complicate estimates
* Non-stationarity: Changing conditions violate constant R assumptions
==Historical Context and Evolution==
The concept of R₀ emerged from mathematical epidemiology in the early 20th century, with major developments by:
* William Hamer (1906): Formulated the "mass action principle"
* Anderson and May (1980s): Developed comprehensive R₀ theory in population dynamics
* Modern applications: Real-time R estimation became standard during COVID-19 pandemic

The pandemic demonstrated both the utility and challenges of R-based monitoring, spurring improvements in methodology and real-time estimation techniques.

See Also
* [[Epidemic Curves and Growth Rate]]
* [[Serial Interval and Generation Time]]
* [[Vaccination and Immunization]]
* [[Contact Tracing]]
* [[Mathematical Epidemiology]]
* [[Pathogenicity and Virulence]]

==References==
* Keeling, M. J., & Rohani, P. (2008). Modeling infectious diseases. Princeton University Press.
* Wallinga, J., & Teunis, P. (2004). Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. American Journal of Epidemiology, 160(6), 509-516.
* Fraser, C. (2007). Estimating individual and household reproduction numbers in an emerging epidemic. PLoS Medicine, 4(7), e300.
* Thompson, R. N., Stockwin, J. E., van Gaalen, R. D., et al. (2019). Improved inference of time-varying reproduction numbers during outbreaks. Epidemics, 29, 100356.

Last Updated: May 2026

Status: Complete

Difficulty Level: Intermediate to Advanced

Transmissibility

2026-05-18T12:22:49Z

Bosmana fem: /* References */

= Infectiousness / Transmissibility =
==Definition==
Infectiousness (or transmissibility) refers to the ability of a pathogen or infectious agent to spread from an infected person to others. It quantifies how readily a disease is transmitted within a population and is a fundamental parameter in epidemiological modeling and disease control strategy.
Infectiousness is distinct from pathogenicity (the capacity to cause disease) and virulence (the severity of disease caused). A highly infectious disease may be mild, while a severe disease may be poorly transmitted.
==Key Parameters==
===Basic Reproduction Number (R₀)===
The basic reproduction number (R₀, pronounced "R naught") is one of the most important epidemiological metrics. It represents the average number of secondary infections that result from a single infected individual in a completely susceptible population, assuming no interventions or behavioral changes.
====Mathematical Definition====
R₀ is calculated as:
R₀ = β × c × d
Where:
· β = transmissibility (probability of transmission per contact)
· c = contact rate (average number of contacts per unit time)
· d = duration of infectiousness (average time a person remains contagious)
Interpretation
· R₀ < 1: The infection will die out naturally; each infected person infects, on average, fewer than one other person
· R₀ = 1: Endemic equilibrium; the infection sustains itself at a stable level
· R₀ > 1: The infection will spread; each infected person infects more than one other person on average
· Larger R₀ values indicate more readily spreading infections and greater transmission potential
====Examples of R₀ Values====
Disease R₀ Range Transmission
Seasonal influenza 0.9–2.0 Moderate
COVID-19 (original strain) 2.0–3.0 Moderate to high
COVID-19 (Delta variant) 5–8 High
COVID-19 (Omicron variant) 8–12 Very high
Chickenpox 10–12 Very high
Measles 12–18 Extremely high
Smallpox 5–7 High
Polio 5–7 High
HIV/AIDS 0.5–3 Low to moderate

===Effective Reproduction Number (Rₑ or Rₜ)===
The effective reproduction number (Rₑ or Rₜ, where t indicates time) represents the average number of secondary infections caused by a single infected individual in the current population at a specific time, accounting for immunity, interventions, behavioral changes, and non-susceptible individuals.
====Mathematical Definition====
Rₑ(t) = R₀ × s(t)
Where:
· R₀ = basic reproduction number
· s(t) = proportion of the population that is susceptible at time t
====Key Differences from R₀====
Aspect R₀ Rₑ
Population Completely susceptible Current population state
Immunity Not accounted for Accounts for existing immunity
Interventions Assumes none Includes interventions, vaccines, behavior changes
Time dependence Constant, intrinsic to pathogen Changes over time and with circumstances
Practical use Theoretical benchmark Operational monitoring during outbreaks

===Epidemiological Significance===
· Rₑ < 1: The outbreak is declining or under control
· Rₑ = 1: Steady-state transmission (cases neither increasing nor decreasing)
· Rₑ > 1: The outbreak is accelerating or expanding
Because Rₑ incorporates real-world conditions, it is the more practically useful metric for monitoring epidemics and evaluating intervention effectiveness.
==Factors Affecting Transmissibility==
====Pathogen Characteristics====
· Virus/bacteria shedding rate: How much pathogen is released by infected individuals
· Genetic mutations: Changes can increase transmissibility (e.g., variant emergence)
· Stability in environment: How long the pathogen survives outside hosts
· Infectious period: The window during which a person can transmit
· Asymptomatic transmission: Capacity to spread before or without causing symptoms
====Population and Environmental Factors====
· Contact patterns: Density of population, social structures, frequency of interactions
· Hygiene and sanitation: Hand washing, water quality, sanitation infrastructure
· Climate and seasonality: Temperature, humidity, seasonal behavior changes
· Vaccination coverage: Proportion of population immune through vaccination
· Prior infection: Natural immunity from previous exposure
· Age structure: Different age groups may have different contact patterns and susceptibility
· Healthcare access: Early detection and isolation reduce transmission
====Behavioral Factors====
· Mobility and travel: Movement patterns spread pathogens across regions
· Social distancing: Reduces contact rates significantly
· Mask usage: Reduces transmissibility through respiratory droplets
· Isolation of sick individuals: Removes infectious people from contact with others
· Risk perception: Individual behavior changes based on perceived threat
==Measurement and Estimation==
===Direct Estimation===
In an early epidemic with complete contact tracing data:
R₀ ≈ (average number of secondary cases per infected individual)
====Statistical Methods====
· Generation time approach: Uses the serial interval and growth rate
· Contact tracing data: Tracks who infected whom
· Maximum likelihood estimation: Fits epidemic models to observed data
· Bayesian methods: Incorporates uncertainty and prior knowledge
===Real-Time Estimation===
Rₑ is estimated during epidemics using:
· Case incidence data: Number of confirmed cases over time
· Serial interval: Average time between case generations
· Smoothing techniques: Account for reporting delays and data variability
· Multiple methods: Cross-checking with different approaches improves reliability
====Common Estimation Methods====
· Wallinga-Teunis method
· Cori method
· Time-dependent methods using exponential growth rates
==Herd Immunity Threshold==
The herd immunity threshold is the proportion of the population that must be immune (through vaccination or prior infection) to prevent sustained transmission.
Herd Immunity Threshold = 1 - (1/R₀)
==Examples==
Disease R₀ Herd Immunity Threshold
Seasonal flu 1.3 23%
COVID-19 (original) 2.5 60%
COVID-19 (Delta) 6.5 85%
Measles 15 95%
Polio 6 83%

When vaccination coverage exceeds this threshold, the disease cannot sustain itself in the population, protecting even those not vaccinated (indirect protection).
==Practical Applications==
Outbreak Response
* Early assessment: Initial R₀ estimates inform intervention intensity
* Real-time monitoring: Tracking Rₑ determines if control measures are working
* Resource allocation: Higher R values indicate need for more aggressive response
Vaccination Strategy
* Coverage targets: Herd immunity threshold guides vaccination campaigns
* Booster decisions: Changing Rₑ indicates when immunity-boosting measures are needed
* Variant concerns: Increased R values prompt vaccine updates
Public Health Planning
* Hospital capacity: Higher R values predict more cases and healthcare burden
* Containment feasibility: R₀ < 1 suggests containment is possible; large R₀ suggests mitigation focus
* Intervention selection: Different interventions target different components of the R formula
Disease Modeling
* Epidemic projections: R values predict outbreak trajectory
* Intervention scenarios: Modeling shows how different measures affect R
* Long-term planning: Estimates inform pandemic preparedness
==Limitations and Considerations==
Assumptions and Challenges
* Homogeneous mixing: Actual populations have heterogeneous contact patterns (some people have many contacts, others few)
* Temporal variation: Contact patterns and susceptibility change over time
* Data quality: Relies on accurate case counts, testing, and reporting
* Reporting delays: Case reporting lags affect real-time estimates
* Uncertainty: Confidence intervals often wide during uncertainty
Heterogeneity
Real transmission is not uniformly random:
* Super-spreaders: Some individuals transmit to many more than average
* Superspreading events: Specific circumstances produce disproportionate transmission
* Spatial clustering: Transmission follows geographic and social networks
* Age and risk stratification: Transmission varies by age group and risk profile
Methodological Issues
* Estimation uncertainty: Different methods may yield different R values
* Generational overlap: Serial intervals difficult to estimate early in epidemics
* Incomplete data: Asymptomatic and undetected cases complicate estimates
* Non-stationarity: Changing conditions violate constant R assumptions
==Historical Context and Evolution==
The concept of R₀ emerged from mathematical epidemiology in the early 20th century, with major developments by:
* William Hamer (1906): Formulated the "mass action principle"
* Anderson and May (1980s): Developed comprehensive R₀ theory in population dynamics
* Modern applications: Real-time R estimation became standard during COVID-19 pandemic

The pandemic demonstrated both the utility and challenges of R-based monitoring, spurring improvements in methodology and real-time estimation techniques.

See Also
* [[Epidemic Curves and Growth Rate]]
* [[Serial Interval and Generation Time]]
* [[Vaccination and Immunization]]
* [[Contact Tracing]]
* [[Mathematical Epidemiology]]
* [[Pathogenicity and Virulence]]

==References==
* Keeling, M. J., & Rohani, P. (2008). Modeling infectious diseases. Princeton University Press.
* Wallinga, J., & Teunis, P. (2004). Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. American Journal of Epidemiology, 160(6), 509-516.
* Fraser, C. (2007). Estimating individual and household reproduction numbers in an emerging epidemic. PLoS Medicine, 4(7), e300.
* Thompson, R. N., Stockwin, J. E., van Gaalen, R. D., et al. (2019). Improved inference of time-varying reproduction numbers during outbreaks. Epidemics, 29, 100356.

Last Updated: May 2026

Status: Complete

Difficulty Level: Intermediate to Advanced

Transmissibility

2026-05-18T12:22:22Z

Bosmana fem: /* Examples */

= Infectiousness / Transmissibility =
==Definition==
Infectiousness (or transmissibility) refers to the ability of a pathogen or infectious agent to spread from an infected person to others. It quantifies how readily a disease is transmitted within a population and is a fundamental parameter in epidemiological modeling and disease control strategy.
Infectiousness is distinct from pathogenicity (the capacity to cause disease) and virulence (the severity of disease caused). A highly infectious disease may be mild, while a severe disease may be poorly transmitted.
==Key Parameters==
===Basic Reproduction Number (R₀)===
The basic reproduction number (R₀, pronounced "R naught") is one of the most important epidemiological metrics. It represents the average number of secondary infections that result from a single infected individual in a completely susceptible population, assuming no interventions or behavioral changes.
====Mathematical Definition====
R₀ is calculated as:
R₀ = β × c × d
Where:
· β = transmissibility (probability of transmission per contact)
· c = contact rate (average number of contacts per unit time)
· d = duration of infectiousness (average time a person remains contagious)
Interpretation
· R₀ < 1: The infection will die out naturally; each infected person infects, on average, fewer than one other person
· R₀ = 1: Endemic equilibrium; the infection sustains itself at a stable level
· R₀ > 1: The infection will spread; each infected person infects more than one other person on average
· Larger R₀ values indicate more readily spreading infections and greater transmission potential
====Examples of R₀ Values====
Disease R₀ Range Transmission
Seasonal influenza 0.9–2.0 Moderate
COVID-19 (original strain) 2.0–3.0 Moderate to high
COVID-19 (Delta variant) 5–8 High
COVID-19 (Omicron variant) 8–12 Very high
Chickenpox 10–12 Very high
Measles 12–18 Extremely high
Smallpox 5–7 High
Polio 5–7 High
HIV/AIDS 0.5–3 Low to moderate

===Effective Reproduction Number (Rₑ or Rₜ)===
The effective reproduction number (Rₑ or Rₜ, where t indicates time) represents the average number of secondary infections caused by a single infected individual in the current population at a specific time, accounting for immunity, interventions, behavioral changes, and non-susceptible individuals.
====Mathematical Definition====
Rₑ(t) = R₀ × s(t)
Where:
· R₀ = basic reproduction number
· s(t) = proportion of the population that is susceptible at time t
====Key Differences from R₀====
Aspect R₀ Rₑ
Population Completely susceptible Current population state
Immunity Not accounted for Accounts for existing immunity
Interventions Assumes none Includes interventions, vaccines, behavior changes
Time dependence Constant, intrinsic to pathogen Changes over time and with circumstances
Practical use Theoretical benchmark Operational monitoring during outbreaks

===Epidemiological Significance===
· Rₑ < 1: The outbreak is declining or under control
· Rₑ = 1: Steady-state transmission (cases neither increasing nor decreasing)
· Rₑ > 1: The outbreak is accelerating or expanding
Because Rₑ incorporates real-world conditions, it is the more practically useful metric for monitoring epidemics and evaluating intervention effectiveness.
==Factors Affecting Transmissibility==
====Pathogen Characteristics====
· Virus/bacteria shedding rate: How much pathogen is released by infected individuals
· Genetic mutations: Changes can increase transmissibility (e.g., variant emergence)
· Stability in environment: How long the pathogen survives outside hosts
· Infectious period: The window during which a person can transmit
· Asymptomatic transmission: Capacity to spread before or without causing symptoms
====Population and Environmental Factors====
· Contact patterns: Density of population, social structures, frequency of interactions
· Hygiene and sanitation: Hand washing, water quality, sanitation infrastructure
· Climate and seasonality: Temperature, humidity, seasonal behavior changes
· Vaccination coverage: Proportion of population immune through vaccination
· Prior infection: Natural immunity from previous exposure
· Age structure: Different age groups may have different contact patterns and susceptibility
· Healthcare access: Early detection and isolation reduce transmission
====Behavioral Factors====
· Mobility and travel: Movement patterns spread pathogens across regions
· Social distancing: Reduces contact rates significantly
· Mask usage: Reduces transmissibility through respiratory droplets
· Isolation of sick individuals: Removes infectious people from contact with others
· Risk perception: Individual behavior changes based on perceived threat
==Measurement and Estimation==
===Direct Estimation===
In an early epidemic with complete contact tracing data:
R₀ ≈ (average number of secondary cases per infected individual)
====Statistical Methods====
· Generation time approach: Uses the serial interval and growth rate
· Contact tracing data: Tracks who infected whom
· Maximum likelihood estimation: Fits epidemic models to observed data
· Bayesian methods: Incorporates uncertainty and prior knowledge
===Real-Time Estimation===
Rₑ is estimated during epidemics using:
· Case incidence data: Number of confirmed cases over time
· Serial interval: Average time between case generations
· Smoothing techniques: Account for reporting delays and data variability
· Multiple methods: Cross-checking with different approaches improves reliability
====Common Estimation Methods====
· Wallinga-Teunis method
· Cori method
· Time-dependent methods using exponential growth rates
==Herd Immunity Threshold==
The herd immunity threshold is the proportion of the population that must be immune (through vaccination or prior infection) to prevent sustained transmission.
Herd Immunity Threshold = 1 - (1/R₀)
==Examples==
Disease R₀ Herd Immunity Threshold
Seasonal flu 1.3 23%
COVID-19 (original) 2.5 60%
COVID-19 (Delta) 6.5 85%
Measles 15 95%
Polio 6 83%

When vaccination coverage exceeds this threshold, the disease cannot sustain itself in the population, protecting even those not vaccinated (indirect protection).
==Practical Applications==
Outbreak Response
* Early assessment: Initial R₀ estimates inform intervention intensity
* Real-time monitoring: Tracking Rₑ determines if control measures are working
* Resource allocation: Higher R values indicate need for more aggressive response
Vaccination Strategy
* Coverage targets: Herd immunity threshold guides vaccination campaigns
* Booster decisions: Changing Rₑ indicates when immunity-boosting measures are needed
* Variant concerns: Increased R values prompt vaccine updates
Public Health Planning
* Hospital capacity: Higher R values predict more cases and healthcare burden
* Containment feasibility: R₀ < 1 suggests containment is possible; large R₀ suggests mitigation focus
* Intervention selection: Different interventions target different components of the R formula
Disease Modeling
* Epidemic projections: R values predict outbreak trajectory
* Intervention scenarios: Modeling shows how different measures affect R
* Long-term planning: Estimates inform pandemic preparedness
==Limitations and Considerations==
Assumptions and Challenges
* Homogeneous mixing: Actual populations have heterogeneous contact patterns (some people have many contacts, others few)
* Temporal variation: Contact patterns and susceptibility change over time
* Data quality: Relies on accurate case counts, testing, and reporting
* Reporting delays: Case reporting lags affect real-time estimates
* Uncertainty: Confidence intervals often wide during uncertainty
Heterogeneity
Real transmission is not uniformly random:
* Super-spreaders: Some individuals transmit to many more than average
* Superspreading events: Specific circumstances produce disproportionate transmission
* Spatial clustering: Transmission follows geographic and social networks
* Age and risk stratification: Transmission varies by age group and risk profile
Methodological Issues
* Estimation uncertainty: Different methods may yield different R values
* Generational overlap: Serial intervals difficult to estimate early in epidemics
* Incomplete data: Asymptomatic and undetected cases complicate estimates
* Non-stationarity: Changing conditions violate constant R assumptions
==Historical Context and Evolution==
The concept of R₀ emerged from mathematical epidemiology in the early 20th century, with major developments by:
* William Hamer (1906): Formulated the "mass action principle"
* Anderson and May (1980s): Developed comprehensive R₀ theory in population dynamics
* Modern applications: Real-time R estimation became standard during COVID-19 pandemic

The pandemic demonstrated both the utility and challenges of R-based monitoring, spurring improvements in methodology and real-time estimation techniques.

See Also
* [[Epidemic Curves and Growth Rate]]
* [[Serial Interval and Generation Time]]
* [[Vaccination and Immunization]]
* [[Contact Tracing]]
* [[Mathematical Epidemiology]]
* [[Pathogenicity and Virulence]]

==References==
· Keeling, M. J., & Rohani, P. (2008). Modeling infectious diseases. Princeton University Press.
· Wallinga, J., & Teunis, P. (2004). Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. American Journal of Epidemiology, 160(6), 509-516.
· Fraser, C. (2007). Estimating individual and household reproduction numbers in an emerging epidemic. PLoS Medicine, 4(7), e300.
· Thompson, R. N., Stockwin, J. E., van Gaalen, R. D., et al. (2019). Improved inference of time-varying reproduction numbers during outbreaks. Epidemics, 29, 100356.

Last Updated: May 2026

Status: Complete

Difficulty Level: Intermediate to Advanced

Transmissibility

2026-05-18T12:19:24Z

Bosmana fem: Created page with "= Infectiousness / Transmissibility = ==Definition== Infectiousness (or transmissibility) refers to the ability of a pathogen or infectious agent to spread from an infected person to others. It quantifies how readily a disease is transmitted within a population and is a fundamental parameter in epidemiological modeling and disease control strategy. Infectiousness is distinct from pathogenicity (the capacity to cause disease) and virulence (the severity of disease caused)..."

= Infectiousness / Transmissibility =
==Definition==
Infectiousness (or transmissibility) refers to the ability of a pathogen or infectious agent to spread from an infected person to others. It quantifies how readily a disease is transmitted within a population and is a fundamental parameter in epidemiological modeling and disease control strategy.
Infectiousness is distinct from pathogenicity (the capacity to cause disease) and virulence (the severity of disease caused). A highly infectious disease may be mild, while a severe disease may be poorly transmitted.
==Key Parameters==
===Basic Reproduction Number (R₀)===
The basic reproduction number (R₀, pronounced "R naught") is one of the most important epidemiological metrics. It represents the average number of secondary infections that result from a single infected individual in a completely susceptible population, assuming no interventions or behavioral changes.
====Mathematical Definition====
R₀ is calculated as:
R₀ = β × c × d
Where:
· β = transmissibility (probability of transmission per contact)
· c = contact rate (average number of contacts per unit time)
· d = duration of infectiousness (average time a person remains contagious)
Interpretation
· R₀ < 1: The infection will die out naturally; each infected person infects, on average, fewer than one other person
· R₀ = 1: Endemic equilibrium; the infection sustains itself at a stable level
· R₀ > 1: The infection will spread; each infected person infects more than one other person on average
· Larger R₀ values indicate more readily spreading infections and greater transmission potential
====Examples of R₀ Values====
Disease R₀ Range Transmission
Seasonal influenza 0.9–2.0 Moderate
COVID-19 (original strain) 2.0–3.0 Moderate to high
COVID-19 (Delta variant) 5–8 High
COVID-19 (Omicron variant) 8–12 Very high
Chickenpox 10–12 Very high
Measles 12–18 Extremely high
Smallpox 5–7 High
Polio 5–7 High
HIV/AIDS 0.5–3 Low to moderate

===Effective Reproduction Number (Rₑ or Rₜ)===
The effective reproduction number (Rₑ or Rₜ, where t indicates time) represents the average number of secondary infections caused by a single infected individual in the current population at a specific time, accounting for immunity, interventions, behavioral changes, and non-susceptible individuals.
====Mathematical Definition====
Rₑ(t) = R₀ × s(t)
Where:
· R₀ = basic reproduction number
· s(t) = proportion of the population that is susceptible at time t
====Key Differences from R₀====
Aspect R₀ Rₑ
Population Completely susceptible Current population state
Immunity Not accounted for Accounts for existing immunity
Interventions Assumes none Includes interventions, vaccines, behavior changes
Time dependence Constant, intrinsic to pathogen Changes over time and with circumstances
Practical use Theoretical benchmark Operational monitoring during outbreaks

===Epidemiological Significance===
· Rₑ < 1: The outbreak is declining or under control
· Rₑ = 1: Steady-state transmission (cases neither increasing nor decreasing)
· Rₑ > 1: The outbreak is accelerating or expanding
Because Rₑ incorporates real-world conditions, it is the more practically useful metric for monitoring epidemics and evaluating intervention effectiveness.
==Factors Affecting Transmissibility==
====Pathogen Characteristics====
· Virus/bacteria shedding rate: How much pathogen is released by infected individuals
· Genetic mutations: Changes can increase transmissibility (e.g., variant emergence)
· Stability in environment: How long the pathogen survives outside hosts
· Infectious period: The window during which a person can transmit
· Asymptomatic transmission: Capacity to spread before or without causing symptoms
====Population and Environmental Factors====
· Contact patterns: Density of population, social structures, frequency of interactions
· Hygiene and sanitation: Hand washing, water quality, sanitation infrastructure
· Climate and seasonality: Temperature, humidity, seasonal behavior changes
· Vaccination coverage: Proportion of population immune through vaccination
· Prior infection: Natural immunity from previous exposure
· Age structure: Different age groups may have different contact patterns and susceptibility
· Healthcare access: Early detection and isolation reduce transmission
====Behavioral Factors====
· Mobility and travel: Movement patterns spread pathogens across regions
· Social distancing: Reduces contact rates significantly
· Mask usage: Reduces transmissibility through respiratory droplets
· Isolation of sick individuals: Removes infectious people from contact with others
· Risk perception: Individual behavior changes based on perceived threat
==Measurement and Estimation==
===Direct Estimation===
In an early epidemic with complete contact tracing data:
R₀ ≈ (average number of secondary cases per infected individual)
====Statistical Methods====
· Generation time approach: Uses the serial interval and growth rate
· Contact tracing data: Tracks who infected whom
· Maximum likelihood estimation: Fits epidemic models to observed data
· Bayesian methods: Incorporates uncertainty and prior knowledge
===Real-Time Estimation===
Rₑ is estimated during epidemics using:
· Case incidence data: Number of confirmed cases over time
· Serial interval: Average time between case generations
· Smoothing techniques: Account for reporting delays and data variability
· Multiple methods: Cross-checking with different approaches improves reliability
====Common Estimation Methods====
· Wallinga-Teunis method
· Cori method
· Time-dependent methods using exponential growth rates
==Herd Immunity Threshold==
The herd immunity threshold is the proportion of the population that must be immune (through vaccination or prior infection) to prevent sustained transmission.
Herd Immunity Threshold = 1 - (1/R₀)
==Examples==
Disease R₀ Herd Immunity Threshold
Seasonal flu 1.3 23%
COVID-19 (original) 2.5 60%
COVID-19 (Delta) 6.5 85%
Measles 15 95%
Polio 6 83%

When vaccination coverage exceeds this threshold, the disease cannot sustain itself in the population, protecting even those not vaccinated (indirect protection).
Practical Applications
Outbreak Response
· Early assessment: Initial R₀ estimates inform intervention intensity
· Real-time monitoring: Tracking Rₑ determines if control measures are working
· Resource allocation: Higher R values indicate need for more aggressive response
Vaccination Strategy
· Coverage targets: Herd immunity threshold guides vaccination campaigns
· Booster decisions: Changing Rₑ indicates when immunity-boosting measures are needed
· Variant concerns: Increased R values prompt vaccine updates
Public Health Planning
· Hospital capacity: Higher R values predict more cases and healthcare burden
· Containment feasibility: R₀ < 1 suggests containment is possible; large R₀ suggests mitigation focus
· Intervention selection: Different interventions target different components of the R formula
Disease Modeling
· Epidemic projections: R values predict outbreak trajectory
· Intervention scenarios: Modeling shows how different measures affect R
· Long-term planning: Estimates inform pandemic preparedness
Limitations and Considerations
Assumptions and Challenges
· Homogeneous mixing: Actual populations have heterogeneous contact patterns (some people have many contacts, others few)
· Temporal variation: Contact patterns and susceptibility change over time
· Data quality: Relies on accurate case counts, testing, and reporting
· Reporting delays: Case reporting lags affect real-time estimates
· Uncertainty: Confidence intervals often wide during uncertainty
Heterogeneity
Real transmission is not uniformly random:
· Super-spreaders: Some individuals transmit to many more than average
· Superspreading events: Specific circumstances produce disproportionate transmission
· Spatial clustering: Transmission follows geographic and social networks
· Age and risk stratification: Transmission varies by age group and risk profile
Methodological Issues
· Estimation uncertainty: Different methods may yield different R values
· Generational overlap: Serial intervals difficult to estimate early in epidemics
· Incomplete data: Asymptomatic and undetected cases complicate estimates
· Non-stationarity: Changing conditions violate constant R assumptions
Historical Context and Evolution
The concept of R₀ emerged from mathematical epidemiology in the early 20th century, with major developments by:
· William Hamer (1906): Formulated the "mass action principle"
· Anderson and May (1980s): Developed comprehensive R₀ theory in population dynamics
· Modern applications: Real-time R estimation became standard during COVID-19 pandemic
The pandemic demonstrated both the utility and challenges of R-based monitoring, spurring improvements in methodology and real-time estimation techniques.
See Also
· [[Epidemic Curves and Growth Rate]]
· [[Serial Interval and Generation Time]]
· [[Vaccination and Immunization]]
· [[Contact Tracing]]
· [[Mathematical Epidemiology]]
· [[Pathogenicity and Virulence]]
==References==
· Keeling, M. J., & Rohani, P. (2008). Modeling infectious diseases. Princeton University Press.
· Wallinga, J., & Teunis, P. (2004). Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. American Journal of Epidemiology, 160(6), 509-516.
· Fraser, C. (2007). Estimating individual and household reproduction numbers in an emerging epidemic. PLoS Medicine, 4(7), e300.
· Thompson, R. N., Stockwin, J. E., van Gaalen, R. D., et al. (2019). Improved inference of time-varying reproduction numbers during outbreaks. Epidemics, 29, 100356.

Last Updated: May 2026

Status: Complete

Difficulty Level: Intermediate to Advanced

Formal Risk Assessment

2026-05-18T12:16:09Z

Bosmana fem: /* Defining the impact */

Formal risk assessment is the process of a systematic review of evidence that defines or estimates risk in the population. The purpose is to guide risk management (e.g., preventive and control measures). The concept of 'risk' is here defined as the [impact of the event * probability of occurrence]

The differences with [[Rapid Risk Assessment|Rapid Risk Assessments]] are:

* the timeline in which the assessment takes place: instead of 24-28 hours, a formal risk assessment may take weeks or even months
* the focus of the risk scenarios: instead of focusing on the immediate problem, the focus includes possible future evolutions of the hazard under assessment
Health risks may arise in the population (emerging or newly identified diseases) or may already exist (yet with changing epidemiological patterns or changes in risk factors). It is important to know that risks do not remain static; formal risk assessments consider development scenarios.

=Problem formulation=
The scope of the assessment is the starting point. This could be the risk of the introduction of a disease agent or the risk of the spread of a disease. It could cover threats to one sector (health) or many (e.g., agriculture, food, security). Once the scope is defined, the problem is formulated, with related objectives of the assessment (SMART). This will also clarify what sectors of society are affected by the problem.

=Constituting a Formal Risk assessment group=
Many hazards that are assessed are cross-cutting through different sectors and disciplines in health. Therefore the team needs to reflect on this multidisciplinary and multisectoral aspect. The group should be large enough to cover all areas and include representatives from health and other relevant sectors. Additional experts will be contacted to provide expert input.

=Defining the probability of occurrence: an example=
When defining the risk of introducing West Nile Virus in a country, the first issue to address is estimating the probability of introducing the virus in the population. This includes studying migratory routes of birds that could carry the virus and the presence of vectors (Aedes albopictus) in the country. Specifically, the interest will be in the frequency of occurrence of specific migratory birds in the country (this can be informed via veterinarians and wildlife societies) and the presence and future spread of the mosquito (this can be informed by environmental specialists and entomologists and in addition specialists in climate change).

Socio-economic factors (e.g., agricultural development, technological development, movement of people) also need to be considered.

=Defining the impact=
The scope of the risk assessment also defines the scope of the impact to describe: only health or also other areas such as economics, travel, agriculture, security etc. The impact depends on various hazard factors.

Hazard factors for infectious agents are:

* Infectiousness / [[Transmissibility]]
* Mode of transmission
* Pathogenicity
* Severity of illness
* Outcomes of illness
* Dose-response effects
In addition, there will be several host factors that are relevant to describe:

* Susceptibility
* Vulnerability
* ..... (complement)

Characteristics of the system are essential to describe: the structure and capacity of the health care system, laboratory capacity, treatment capacity, prevention and control capacity. For bloodborne diseases (such as WNV), it is important to describe the blood donation process, options, and costs for screening (depending on the scenario of the establishment of the disease in the country)

=Modeling the risks=
Scenario tree modelling describes the chain of events leading to possible risks. This requires describing a tree of events (each event step will be a relevant condition for the final risk). Then for each of these steps, the probability is assigned. Finally, a sensitivity analysis is performed.

=Mapping the risks=
It is usually relevant for risk managers to have a visual representation of risks according to the geographical region of a country (map). This will help to set priorities for risk reduction strategies. Risk maps can also aggregate information from different factors considered predictors of an event's probability.

=Sources of data=
Probability and impact need to be as much as possible fact and evidence-based. Surveillance data (human, animal, environmental) are useful, as are specific surveys and published research. If information is missing from those sources, then expert opinion can be recruited to complement the required information.

[[Category:Assessing the burden of disease and risk assessment]]

Formal Risk Assessment

2026-05-18T12:15:52Z

Bosmana fem:

Formal risk assessment is the process of a systematic review of evidence that defines or estimates risk in the population. The purpose is to guide risk management (e.g., preventive and control measures). The concept of 'risk' is here defined as the [impact of the event * probability of occurrence]

The differences with [[Rapid Risk Assessment|Rapid Risk Assessments]] are:

* the timeline in which the assessment takes place: instead of 24-28 hours, a formal risk assessment may take weeks or even months
* the focus of the risk scenarios: instead of focusing on the immediate problem, the focus includes possible future evolutions of the hazard under assessment
Health risks may arise in the population (emerging or newly identified diseases) or may already exist (yet with changing epidemiological patterns or changes in risk factors). It is important to know that risks do not remain static; formal risk assessments consider development scenarios.

=Problem formulation=
The scope of the assessment is the starting point. This could be the risk of the introduction of a disease agent or the risk of the spread of a disease. It could cover threats to one sector (health) or many (e.g., agriculture, food, security). Once the scope is defined, the problem is formulated, with related objectives of the assessment (SMART). This will also clarify what sectors of society are affected by the problem.

=Constituting a Formal Risk assessment group=
Many hazards that are assessed are cross-cutting through different sectors and disciplines in health. Therefore the team needs to reflect on this multidisciplinary and multisectoral aspect. The group should be large enough to cover all areas and include representatives from health and other relevant sectors. Additional experts will be contacted to provide expert input.

=Defining the probability of occurrence: an example=
When defining the risk of introducing West Nile Virus in a country, the first issue to address is estimating the probability of introducing the virus in the population. This includes studying migratory routes of birds that could carry the virus and the presence of vectors (Aedes albopictus) in the country. Specifically, the interest will be in the frequency of occurrence of specific migratory birds in the country (this can be informed via veterinarians and wildlife societies) and the presence and future spread of the mosquito (this can be informed by environmental specialists and entomologists and in addition specialists in climate change).

Socio-economic factors (e.g., agricultural development, technological development, movement of people) also need to be considered.

=Defining the impact=
The scope of the risk assessment also defines the scope of the impact to describe: only health or also other areas such as economics, travel, agriculture, security etc. The impact depends on various hazard factors.

Hazard factors for infectious agents are:

* Infectiousness / [Transmissibility]
* Mode of transmission
* Pathogenicity
* Severity of illness
* Outcomes of illness
* Dose-response effects
In addition, there will be several host factors that are relevant to describe:

* Susceptibility
* Vulnerability
* ..... (complement)

Characteristics of the system are essential to describe: the structure and capacity of the health care system, laboratory capacity, treatment capacity, prevention and control capacity. For bloodborne diseases (such as WNV), it is important to describe the blood donation process, options, and costs for screening (depending on the scenario of the establishment of the disease in the country)

=Modeling the risks=
Scenario tree modelling describes the chain of events leading to possible risks. This requires describing a tree of events (each event step will be a relevant condition for the final risk). Then for each of these steps, the probability is assigned. Finally, a sensitivity analysis is performed.

=Mapping the risks=
It is usually relevant for risk managers to have a visual representation of risks according to the geographical region of a country (map). This will help to set priorities for risk reduction strategies. Risk maps can also aggregate information from different factors considered predictors of an event's probability.

=Sources of data=
Probability and impact need to be as much as possible fact and evidence-based. Surveillance data (human, animal, environmental) are useful, as are specific surveys and published research. If information is missing from those sources, then expert opinion can be recruited to complement the required information.

[[Category:Assessing the burden of disease and risk assessment]]

MediaWiki:Common.css

2026-05-11T21:22:06Z

Bosmana fem:

/* CSS placed here will be applied to all skins */
/* ============================================================
Transmissible brand styling for FemWiki
------------------------------------------------------------
Paste this into MediaWiki:Common.css
URL: https://femwiki.org/index.php?title=MediaWiki:Common.css
(You must be logged in as a sysop / administrator to edit it.)
============================================================ */

/* ---------- Fonts ----------
Rubik is loaded from Google Fonts (free).
Avenir is proprietary; Nunito Sans is the free fallback.
To swap in real Avenir later, replace the @import line below
with your Adobe Fonts kit URL and update the body font-family. */
@import url('https://fonts.googleapis.com/css2?family=Rubik:wght@500;700&family=Nunito+Sans:wght@500;700;800&display=swap');

/* ---------- Brand tokens ---------- */
:root {
--tm-blue: #2780ad; /* core blue */
--tm-blue-dark: #1f6588; /* hover / visited */
--tm-green: #62c465; /* core green */
--tm-green-soft: #c6ebbe; /* extended */
--tm-bg-pale: #e6f7ff; /* core pale blue background */
--tm-bg-mint: #e4fde1; /* extended pale green */
--tm-accent-blue: #b2e2ff; /* extended light blue */
--tm-accent-yel: #f4f2b5; /* extended pale yellow */
--tm-ink: #2a2d34; /* core near-black for text */
--tm-grey: #70747a; /* extended grey */
}

/* ---------- Base typography ---------- */
body,
.mw-body,
#mw-content-text,
#bodyContent {
font-family: 'Avenir Next', 'Avenir', 'Nunito Sans', system-ui, -apple-system, sans-serif;
font-weight: 500;
color: var(--tm-ink);
background-color: white;
line-height: 1.55;
}

/* Strong/bold elements use the heavier Avenir/Nunito weight */
strong, b, .subtitle {
font-weight: 800;
}

/* All headings: Rubik */
h1, h2, h3, h4, h5, h6,
#firstHeading,
.mw-body h1,
.mw-body h2,
.mw-body h3,
.mw-heading {
font-family: 'Rubik', system-ui, sans-serif;
font-weight: 500;
color: var(--tm-ink);
}

#firstHeading,
.mw-body h1 {
border-bottom: 2px solid var(--tm-blue);
padding-bottom: 0.3em;
margin-bottom: 0.6em;
}

.mw-body h2 {
border-bottom: 1px solid var(--tm-green-soft);
padding-bottom: 0.2em;
}

/* ---------- Page chrome (Vector legacy) ---------- */
/* The strip behind the top bar */
#mw-page-base {
background: var(--tm-bg-pale);
background-image: none;
}

/* The dark strip immediately above the content */
#mw-head-base {
background-color: var(--tm-blue);
border-bottom: 3px solid var(--tm-green);
}

#mw-head {
background-color: var(--tm-blue);
}

/* Personal tools (login / your username, top right) */
#p-personal ul li a,
#p-personal ul li a:link {
color: white;
}
#p-personal ul li a:hover {
color: var(--tm-bg-pale);
text-decoration: underline;
}

/* Page action tabs: Read / Edit / View history etc. */
.vector-menu-tabs,
.vectorTabs {
background: transparent;
}
.vector-menu-tabs li,
.vector-menu-tabs li a,
.vectorTabs li a {
background-image: none;
background-color: transparent;
color: white;
}
.vector-menu-tabs li.selected,
.vectorTabs li.selected {
background-color: white;
}
.vector-menu-tabs li.selected a,
.vectorTabs li.selected a {
color: var(--tm-blue-dark);
}
.vector-menu-tabs li a:hover {
color: var(--tm-bg-pale);
}

/* ---------- Sidebar ---------- */
#mw-panel {
background: transparent;
}
#mw-panel .portal h3,
#mw-panel .vector-menu-heading {
font-family: 'Rubik', system-ui, sans-serif;
color: var(--tm-blue-dark);
font-size: 0.85em;
text-transform: uppercase;
letter-spacing: 0.04em;
font-weight: 500;
}
#mw-panel .portal .body a,
#mw-panel .vector-menu-content a {
color: var(--tm-blue);
}
#mw-panel .portal .body a:hover {
color: var(--tm-blue-dark);
text-decoration: underline;
}

/* ---------- Content area ---------- */
#content,
.mw-body {
border: 1px solid var(--tm-accent-blue);
background-color: white;
border-radius: 4px;
}

/* ---------- Readable body text (Wikipedia-matched, 15px) ---------- */
.mw-body,
.mw-body-content,
#mw-content-text,
#bodyContent {
font-size: 15px;
line-height: 1.6;
}

/* Keep the page title proportionally sized */
#firstHeading,
.mw-body h1 {
font-size: 1.8em;
}

.mw-body h2 { font-size: 1.5em; }
.mw-body h3 { font-size: 1.2em; }
.mw-body h4 { font-size: 1.1em; }

/* ---------- Links ---------- */
a,
a:link {
color: var(--tm-blue);
text-decoration: none;
}
a:visited {
color: var(--tm-blue-dark);
}
a:hover {
color: var(--tm-blue-dark);
text-decoration: underline;
}
/* Red (missing-page) links stay red — MediaWiki convention */
a.new,
a.new:visited {
color: #b32424;
}

/* External links */
a.external,
a.extiw {
color: var(--tm-blue);
}

/* ---------- Search box ---------- */
#searchInput,
#simpleSearch input[type="search"] {
border: 1px solid var(--tm-accent-blue);
border-radius: 3px;
padding: 0.35em 0.6em;
font-family: inherit;
}
#searchInput:focus {
border-color: var(--tm-blue);
outline: 2px solid var(--tm-accent-blue);
outline-offset: 0;
}
#searchButton,
#mw-searchButton {
background-color: var(--tm-blue);
}

/* ---------- Buttons & form submits ---------- */
.mw-ui-button,
input[type="submit"],
input[type="button"],
button.cdx-button,
button.mw-ui-button {
background-color: var(--tm-blue);
color: white;
border: 1px solid var(--tm-blue-dark);
border-radius: 3px;
padding: 0.45em 0.95em;
font-family: 'Avenir Next', 'Nunito Sans', sans-serif;
font-weight: 700;
cursor: pointer;
}
.mw-ui-button:hover,
input[type="submit"]:hover,
input[type="button"]:hover {
background-color: var(--tm-blue-dark);
color: white;
}
.mw-ui-button.mw-ui-progressive,
.cdx-button--action-progressive {
background-color: var(--tm-green);
border-color: #4ea552;
color: white;
}
.mw-ui-button.mw-ui-progressive:hover {
background-color: #4ea552;
}

/* ---------- Tables ---------- */
table.wikitable {
border: 1px solid var(--tm-accent-blue);
background-color: white;
border-collapse: collapse;
}
table.wikitable > tr > th,
table.wikitable > * > tr > th {
background-color: var(--tm-bg-pale);
color: var(--tm-ink);
border: 1px solid var(--tm-accent-blue);
font-family: 'Rubik', sans-serif;
font-weight: 500;
}
table.wikitable > tr > td,
table.wikitable > * > tr > td {
border: 1px solid var(--tm-accent-blue);
}

/* ---------- TOC, infoboxes, callouts ---------- */
.toc,
.toccolours {
background-color: var(--tm-bg-pale);
border: 1px solid var(--tm-accent-blue);
border-radius: 4px;
}
.toctitle h2 {
font-family: 'Rubik', sans-serif;
color: var(--tm-blue-dark);
border: none;
}

/* MediaWiki standard message boxes */
.warningbox,
.mw-message-box-warning {
background-color: var(--tm-accent-yel);
border: 1px solid #c4c269;
color: var(--tm-ink);
}
.successbox,
.mw-message-box-success {
background-color: var(--tm-bg-mint);
border: 1px solid var(--tm-green);
color: var(--tm-ink);
}
.messagebox,
.mw-message-box-notice {
background-color: var(--tm-bg-pale);
border: 1px solid var(--tm-accent-blue);
color: var(--tm-ink);
}
.errorbox,
.mw-message-box-error {
background-color: #fde7e7;
border: 1px solid #d33;
color: var(--tm-ink);
}

/* ---------- Code blocks ---------- */
code,
pre,
.mw-code,
tt {
background-color: var(--tm-bg-pale);
border: 1px solid var(--tm-accent-blue);
border-radius: 3px;
font-family: 'JetBrains Mono', 'SF Mono', 'Consolas', 'Courier New', monospace;
padding: 0.1em 0.3em;
}
pre,
.mw-code {
padding: 0.8em 1em;
overflow-x: auto;
}

/* ---------- Blockquotes ---------- */
blockquote {
border-left: 4px solid var(--tm-green);
background-color: var(--tm-bg-pale);
padding: 0.6em 1em;
margin: 1em 0;
color: var(--tm-ink);
font-style: italic;
}

/* ---------- Footer ---------- */
#footer,
#mw-footer {
background: var(--tm-bg-pale);
border-top: 3px solid var(--tm-green);
color: var(--tm-grey);
font-size: 0.85em;
}
#footer a,
#mw-footer a {
color: var(--tm-blue-dark);
}

/* ---------- Category navigation at page bottom ---------- */
#catlinks,
.catlinks {
background-color: var(--tm-bg-mint);
border: 1px solid var(--tm-green-soft);
border-radius: 4px;
padding: 0.4em 0.6em;
}

/* ---------- "Powered by MediaWiki" — keep, just tone ---------- */
#footer-poweredbyico img,
#footer-copyrightico img {
opacity: 0.7;
}

MediaWiki:Common.css

2026-05-11T21:16:11Z

Bosmana fem: Created page with "/* CSS placed here will be applied to all skins */ /* ============================================================ Transmissible brand styling for FemWiki ------------------------------------------------------------ Paste this into MediaWiki:Common.css URL: https://femwiki.org/index.php?title=MediaWiki:Common.css (You must be logged in as a sysop / administrator to edit it.) ============================================================ */ /* ----------..."

/* CSS placed here will be applied to all skins */
/* ============================================================
Transmissible brand styling for FemWiki
------------------------------------------------------------
Paste this into MediaWiki:Common.css
URL: https://femwiki.org/index.php?title=MediaWiki:Common.css
(You must be logged in as a sysop / administrator to edit it.)
============================================================ */

/* ---------- Fonts ----------
Rubik is loaded from Google Fonts (free).
Avenir is proprietary; Nunito Sans is the free fallback.
To swap in real Avenir later, replace the @import line below
with your Adobe Fonts kit URL and update the body font-family. */
@import url('https://fonts.googleapis.com/css2?family=Rubik:wght@500;700&family=Nunito+Sans:wght@500;700;800&display=swap');

/* ---------- Brand tokens ---------- */
:root {
--tm-blue: #2780ad; /* core blue */
--tm-blue-dark: #1f6588; /* hover / visited */
--tm-green: #62c465; /* core green */
--tm-green-soft: #c6ebbe; /* extended */
--tm-bg-pale: #e6f7ff; /* core pale blue background */
--tm-bg-mint: #e4fde1; /* extended pale green */
--tm-accent-blue: #b2e2ff; /* extended light blue */
--tm-accent-yel: #f4f2b5; /* extended pale yellow */
--tm-ink: #2a2d34; /* core near-black for text */
--tm-grey: #70747a; /* extended grey */
}

/* ---------- Base typography ---------- */
body,
.mw-body,
#mw-content-text,
#bodyContent {
font-family: 'Avenir Next', 'Avenir', 'Nunito Sans', system-ui, -apple-system, sans-serif;
font-weight: 500;
color: var(--tm-ink);
background-color: white;
line-height: 1.55;
}

/* Strong/bold elements use the heavier Avenir/Nunito weight */
strong, b, .subtitle {
font-weight: 800;
}

/* All headings: Rubik */
h1, h2, h3, h4, h5, h6,
#firstHeading,
.mw-body h1,
.mw-body h2,
.mw-body h3,
.mw-heading {
font-family: 'Rubik', system-ui, sans-serif;
font-weight: 500;
color: var(--tm-ink);
}

#firstHeading,
.mw-body h1 {
border-bottom: 2px solid var(--tm-blue);
padding-bottom: 0.3em;
margin-bottom: 0.6em;
}

.mw-body h2 {
border-bottom: 1px solid var(--tm-green-soft);
padding-bottom: 0.2em;
}

/* ---------- Page chrome (Vector legacy) ---------- */
/* The strip behind the top bar */
#mw-page-base {
background: var(--tm-bg-pale);
background-image: none;
}

/* The dark strip immediately above the content */
#mw-head-base {
background-color: var(--tm-blue);
border-bottom: 3px solid var(--tm-green);
}

#mw-head {
background-color: var(--tm-blue);
}

/* Personal tools (login / your username, top right) */
#p-personal ul li a,
#p-personal ul li a:link {
color: white;
}
#p-personal ul li a:hover {
color: var(--tm-bg-pale);
text-decoration: underline;
}

/* Page action tabs: Read / Edit / View history etc. */
.vector-menu-tabs,
.vectorTabs {
background: transparent;
}
.vector-menu-tabs li,
.vector-menu-tabs li a,
.vectorTabs li a {
background-image: none;
background-color: transparent;
color: white;
}
.vector-menu-tabs li.selected,
.vectorTabs li.selected {
background-color: white;
}
.vector-menu-tabs li.selected a,
.vectorTabs li.selected a {
color: var(--tm-blue-dark);
}
.vector-menu-tabs li a:hover {
color: var(--tm-bg-pale);
}

/* ---------- Sidebar ---------- */
#mw-panel {
background: transparent;
}
#mw-panel .portal h3,
#mw-panel .vector-menu-heading {
font-family: 'Rubik', system-ui, sans-serif;
color: var(--tm-blue-dark);
font-size: 0.85em;
text-transform: uppercase;
letter-spacing: 0.04em;
font-weight: 500;
}
#mw-panel .portal .body a,
#mw-panel .vector-menu-content a {
color: var(--tm-blue);
}
#mw-panel .portal .body a:hover {
color: var(--tm-blue-dark);
text-decoration: underline;
}

/* ---------- Content area ---------- */
#content,
.mw-body {
border: 1px solid var(--tm-accent-blue);
background-color: white;
border-radius: 4px;
}

/* ---------- Links ---------- */
a,
a:link {
color: var(--tm-blue);
text-decoration: none;
}
a:visited {
color: var(--tm-blue-dark);
}
a:hover {
color: var(--tm-blue-dark);
text-decoration: underline;
}
/* Red (missing-page) links stay red — MediaWiki convention */
a.new,
a.new:visited {
color: #b32424;
}

/* External links */
a.external,
a.extiw {
color: var(--tm-blue);
}

/* ---------- Search box ---------- */
#searchInput,
#simpleSearch input[type="search"] {
border: 1px solid var(--tm-accent-blue);
border-radius: 3px;
padding: 0.35em 0.6em;
font-family: inherit;
}
#searchInput:focus {
border-color: var(--tm-blue);
outline: 2px solid var(--tm-accent-blue);
outline-offset: 0;
}
#searchButton,
#mw-searchButton {
background-color: var(--tm-blue);
}

/* ---------- Buttons & form submits ---------- */
.mw-ui-button,
input[type="submit"],
input[type="button"],
button.cdx-button,
button.mw-ui-button {
background-color: var(--tm-blue);
color: white;
border: 1px solid var(--tm-blue-dark);
border-radius: 3px;
padding: 0.45em 0.95em;
font-family: 'Avenir Next', 'Nunito Sans', sans-serif;
font-weight: 700;
cursor: pointer;
}
.mw-ui-button:hover,
input[type="submit"]:hover,
input[type="button"]:hover {
background-color: var(--tm-blue-dark);
color: white;
}
.mw-ui-button.mw-ui-progressive,
.cdx-button--action-progressive {
background-color: var(--tm-green);
border-color: #4ea552;
color: white;
}
.mw-ui-button.mw-ui-progressive:hover {
background-color: #4ea552;
}

/* ---------- Tables ---------- */
table.wikitable {
border: 1px solid var(--tm-accent-blue);
background-color: white;
border-collapse: collapse;
}
table.wikitable > tr > th,
table.wikitable > * > tr > th {
background-color: var(--tm-bg-pale);
color: var(--tm-ink);
border: 1px solid var(--tm-accent-blue);
font-family: 'Rubik', sans-serif;
font-weight: 500;
}
table.wikitable > tr > td,
table.wikitable > * > tr > td {
border: 1px solid var(--tm-accent-blue);
}

/* ---------- TOC, infoboxes, callouts ---------- */
.toc,
.toccolours {
background-color: var(--tm-bg-pale);
border: 1px solid var(--tm-accent-blue);
border-radius: 4px;
}
.toctitle h2 {
font-family: 'Rubik', sans-serif;
color: var(--tm-blue-dark);
border: none;
}

/* MediaWiki standard message boxes */
.warningbox,
.mw-message-box-warning {
background-color: var(--tm-accent-yel);
border: 1px solid #c4c269;
color: var(--tm-ink);
}
.successbox,
.mw-message-box-success {
background-color: var(--tm-bg-mint);
border: 1px solid var(--tm-green);
color: var(--tm-ink);
}
.messagebox,
.mw-message-box-notice {
background-color: var(--tm-bg-pale);
border: 1px solid var(--tm-accent-blue);
color: var(--tm-ink);
}
.errorbox,
.mw-message-box-error {
background-color: #fde7e7;
border: 1px solid #d33;
color: var(--tm-ink);
}

/* ---------- Code blocks ---------- */
code,
pre,
.mw-code,
tt {
background-color: var(--tm-bg-pale);
border: 1px solid var(--tm-accent-blue);
border-radius: 3px;
font-family: 'JetBrains Mono', 'SF Mono', 'Consolas', 'Courier New', monospace;
padding: 0.1em 0.3em;
}
pre,
.mw-code {
padding: 0.8em 1em;
overflow-x: auto;
}

/* ---------- Blockquotes ---------- */
blockquote {
border-left: 4px solid var(--tm-green);
background-color: var(--tm-bg-pale);
padding: 0.6em 1em;
margin: 1em 0;
color: var(--tm-ink);
font-style: italic;
}

/* ---------- Footer ---------- */
#footer,
#mw-footer {
background: var(--tm-bg-pale);
border-top: 3px solid var(--tm-green);
color: var(--tm-grey);
font-size: 0.85em;
}
#footer a,
#mw-footer a {
color: var(--tm-blue-dark);
}

/* ---------- Category navigation at page bottom ---------- */
#catlinks,
.catlinks {
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Analytical Study Designs

2026-05-11T19:52:21Z

Bosmana fem:

Where descriptive epidemiology describes occurrence of disease (or of its determinants) within a population, the analytical epidemiology aims to gain knowledge on the quality and the amount of influence that determinants have on the occurrence of disease. The usual way to gain this knowledge is by group comparisons. Such a comparison starts from one or more hypotheses about how the determinant may influence occurrence of disease.

For example, the hypothesis may be "people who have eaten home preserved green olives in restaurant X in August 2006 have an increased risk of developing botulism than those who have not eaten such olives".

We can test this hypothesis in an analytical epidemiological study where the risk of developing botulism is studied in 2 comparable populations; one group consists of people that have visited restaurant X in August 2006 and who did eat green home preserved olives. The other group consists of guests of restaurant X in August 2006 that have not eaten those olives. In both groups the risk of developing botulism is measured (by counting botulism cases that occurred in each group within 30 days after visiting the restaurant). Then those two risks are compared to see if they are significantly different.

==Observational studies==
In the above example of a simple analytical epidemiological study, a traditional cohort study design was chosen. Another group of traditional study designs that belongs to analytical epidemiology are case control studies. Other less traditional analytical study designs include case-case studies and case-cross over design. In each of these analytical studies, observations in one group in the population are compared to another group (also called 'reference group'). Choosing the appropriate reference group is one of the challenging aspects of analytical epidemiology.

The examples above belong to the category of 'observational studies' in analytical epidemiology. In such studies, the investigator observes systematically how exposure and outcome are distributed in the populations, and the comparison of those observations is made.

==Experiments==
Another category of analytical studies are 'experimental studies', for example in which the investigator is able to randomly assign exposure to individuals from a particular population after which the occurrence of disease is measured in exposed and unexposed groups. Such experiments are called 'randomised controlled trials (RCT)' and are usually considered the gold standard in analytical epidemiology since the amount of bias is usually very limited. However RCT are not an option if the exposure is known to be very dangerous to humans, in which case it would not be ethical to conduct a RCT. In our example above, it is very clear that a RCT would be completely unacceptable (i.e. deciding randomly which guest should eat green home preserved olives, and then to count botulism cases among exposed and unexposed).

Therefore in Field Epidemiology we are usually left with observational study designs, to observe the 'experiments that nature has created for us'. This often creates challenges in finding appropriate comparison groups. This challenge may also be seen as what defines Field Epidemiology: "Experiments of Nature are our core business"

==EPIET Lectures:==
Case Control and Cohort Studies

Choice of a reference group

Alternative study designs

==FEM PAGE CONTRIBUTORS 2007==
;Editor
:Arnold Bosman
;Contributors
:Vladimir Prikazsky
:Arnold Bosman

[[Category:Types of Study]]

Software for epidemiologists

2025-09-07T16:51:49Z

Bosmana fem:

==Statistical packages==
Statistical software is one of the most important software for epdiemiologists. For a detailed review see the list of [https://web.archive.org/web/20161220110100/http://en.wikipedia.org/wiki/Comparison_of_statistical_packages comparison of statistical packages from Wikipedia]. The most important ones are listed here:

* [https://web.archive.org/web/20161220110100/http://www.r-project.org/ R] = Open source statistics package
* [https://web.archive.org/web/20161220110100/http://www.stata.com/ Stata] = statistic software
* [https://web.archive.org/web/20161220110100/http://www-01.ibm.com/software/analytics/spss/ SPSS] =statistic software
* [https://web.archive.org/web/20161220110100/http://www.graphpad.com/scientific-software/prism/ GraphPad Prism] = statistic software focused on graphs
* [https://web.archive.org/web/20161220110100/http://www.openepi.com/v37/Menu/OE_Menu.htm Openepi] = Open Source Epidemiologic Statistics for Public Health
* [https://web.archive.org/web/20161220110100/http://www.atlasti.com/index.html Atlasti] = Qualitative statistics software
* [https://web.archive.org/web/20161220110100/http://surveillance.cancer.gov/joinpoint/ Joinpoint] = analysis of trends using joinpoint models
* [https://web.archive.org/web/20161220110100/http://www.palisade.com/risk/ Atrisk] = risk analysis using Monte Carlo simulation
* Quick guide for experienced users of other statistical packages (e.g., SAS, SPSS, Stata) who would like to transition to R. http://www.statmethods.net/index.html

==GIS software==
Geographical information systems are used for displaying and analysing geographic data. For a detailed list see the [https://web.archive.org/web/20161220110100/http://en.wikipedia.org/wiki/List_of_geographic_information_systems_software list of GIS-software from Wikipedia]. The most important ones are listed here

* [https://web.archive.org/web/20161220110100/http://www.esri.com/software/arcgis/ Acrgis] = frequently used program
* [https://web.archive.org/web/20161220110100/http://www.qgis.org/en/site/ Quantum GIS] = Open source GIS program
* [https://web.archive.org/web/20161220110100/http://www.gfk-regiograph.com/en/homepage.html Regiograph] = business mapping software

==Data entry software==
* [https://web.archive.org/web/20161220110100/http://www.epidata.dk/ Epidata] = data collection instrument released by the non-profit organisation "The EpiData Association"
* [https://www.who.int/tools/godata Go.Data]

==Field Epidemiology Packages==
* [https://web.archive.org/web/20161220110100/http://wwwn.cdc.gov/epiinfo/ Epiinfo] = The very first all-round Field Epidemiology Support Software by CDC<Ref>See also: The Rise and Fall of Epi Info https://fieldepi.eu/the-rise-and-fall-of-epi-info/</ref>
* [https://sormas.org/ SORMAS]

== Social network analysis==
* [https://web.archive.org/web/20161220110100/http://www.cytoscape.org/ Cytoscape] = Open source social network analysis tool

== Online questionnaires software==
* [https://web.archive.org/web/20161220110100/https://www.limesurvey.org/en/ Limesurvey] = Open source online questionnaire tool
* [https://web.archive.org/web/20161220110100/http://www.classapps.com/SelectSurveyNETOverview.asp Select survey]
* [https://web.archive.org/web/20161220110100/https://www.surveymonkey.com/ Survey monkey]
* [https://web.archive.org/web/20161220110100/https://portal.voozanoo.net/ Voozanoo]

==FEM PAGE CONTRIBUTORS 2007==
; Editor
: Arnold Bosman
; Contributors
: Vladimir Prikazsky
: Jakob Schumacher
: Arnold Bosman

=References=
</references>

[[Category:Public Health Informatics]]

Primary prevention

2025-07-08T08:13:08Z

Bosmana fem:

Within the framework of [[Field Epidemiology|field epidemiology]], primary prevention plays a vital role in averting the onset of communicable diseases and reducing their overall impact on public health.<ref>Centers for Disease Control and Prevention. (2012). Principles of Epidemiology in Public Health Practice, 3rd ed. Lesson 3: Measures of Risk. https://www.cdc.gov/csels/dsepd/ss1978/lesson3/section2.html</ref><ref>This text was written by ChatGPT4.0 on 26 March 2023 and reviewed by Arnold Bosman.</ref>

Key strategies in primary prevention include [[Vaccination|immunisation]] programs, which protect populations from infectious agents such as [[Measles|measles]], polio, and influenza through vaccination.<ref>World Health Organisation. (2023). Immunisation coverage. https://www.who.int/news-room/fact-sheets/detail/immunization-coverage</ref>
Health education and promotion campaigns—such as handwashing initiatives and safe food handling practices—encourage behaviours that reduce the risk of disease [[Transmission routes|transmission]].

[[Vector Borne|Vector control measures]], including the use of insecticide-treated bed nets and environmental source reduction, help limit the spread of vector-borne diseases like malaria and dengue fever. Environmental interventions, such as improving access to clean water and sanitation, also play a critical role by reducing exposure to disease-causing pathogens.

Through these proactive efforts, field epidemiologists contribute to building resilient communities and establishing a strong foundation for communicable disease prevention.

== References ==
<references />

[[Category:Prevention]]

Category:Prevention

2025-07-08T08:11:13Z

Bosmana fem:

In the realm of communicable disease control, prevention strategies are classified into three distinct categories: [[Primary prevention|primary]], [[Secondary prevention|secondary]], and [[Tertiary prevention|tertiary]] prevention.
* Primary prevention focuses on preemptive measures to avoid the onset of disease, targeting healthy individuals through methods such as vaccinations, health education, and promoting hygienic practices.
* Secondary prevention aims to identify and treat the disease in its early stages to halt its progression, typically utilizing screening programs and early diagnosis interventions.
* On the other hand, tertiary prevention targets individuals with an established disease, seeking to minimize complications and improve the quality of life through rehabilitation, long-term care, and chronic disease management.

Collectively, these prevention strategies form a comprehensive approach to controlling communicable diseases, reducing morbidity, and mitigating the overall impact on public health.

=References=
* This text was written by ChatGPT4.0 on 26 March 2023 and reviewed by Arnold Bosman.

[[Category:Public Health Interventions]]

Category:Prevention

2025-07-08T08:10:35Z

Bosmana fem:

In the realm of communicable disease control, prevention strategies are classified into three distinct categories: [[Primary prevention|primary]], [[Secondary prevention|secondary]], and [[Tertiary prevention|tertiary]] prevention.
* Primary prevention focuses on preemptive measures to avoid the onset of disease, targeting healthy individuals through methods such as vaccinations, health education, and promoting hygienic practices.
* Secondary prevention aims to identify and treat the disease in its early stages to halt its progression, typically utilizing screening programs and early diagnosis interventions.
* On the other hand, tertiary prevention targets individuals with an established disease, seeking to minimize complications and improve the quality of life through rehabilitation, long-term care, and chronic disease management. Collectively, these prevention strategies form a comprehensive approach to controlling communicable diseases, reducing morbidity, and mitigating the overall impact on public health.

=References=
* This text was written by ChatGPT4.0 on 26 March 2023 and reviewed by Arnold Bosman.

[[Category:Public Health Interventions]]

Category:Assessing the burden of disease and risk assessment

2025-07-08T08:08:37Z

Bosmana fem:

Effective disease [[Prevention|prevention]] and control depends on several factors that must be present and work together in the community. It all starts with the ability to detect threats to the population's health. A threat can be seen as an undesirable situation that has not yet occurred but may happen unless protective measures are taken. The ability to detect a disease threat implies that we already have basic knowledge about the 'normal occurrence (or burden)' of this disease in the population.

Assessing the disease burden requires a public health workforce competent to collect, analyze and interpret health data from "your" population and the healthcare system infrastructure that allows access to relevant data. [[Field Epidemiology]] methods are central in assessing disease burden.

Detect health threats requires (in addition to the above) continuous monitoring of the burden of disease information of 'your own' and surrounding populations, trends in risk behaviour, characteristics of pathogens (e.g., development of antimicrobial resistance) plus competent staff responsible for the continuous collection, analysis, and interpretation of information. Detecting health threats is sometimes referred to as [[epidemic intelligence]].

Once health threats have been detected and validated, information must be shared with "those who need to know" in the health system. This usually requires translating specific epidemiology jargon into a format used by policy and decision-makers to decide on [[Public Health Interventions|public health interventions]] (preventive and control measures).

This part of this FEMWiki addresses methods that can be used to assess the population's health status and detect and assess health threats. Methods for [[Surveillance principles|Surveillance]], [[Formal Risk Assessment|Risk Assessment]], and [[Outbreak Investigations]] will be described in this section.

[[Public Health Interventions|Interventions]] (public health measures, policy-making, and decision-taking) are discussed in another part of the FEMWIKI. Communication is yet another topic.

<div style="display: inline-block; width: 25%; vertical-align: top; border: 1px solid #000; background-color: #d7effc; padding: 10px; margin: 5px;">
'''FEM PAGE CONTRIBUTORS 2007'''
;Editors
:Arnold Bosman
;FEM Contributors
:Patty Kostkova
</div>
[[Category:Root]]

Category:Assessing the burden of disease and risk assessment

2025-07-08T08:07:45Z

Bosmana fem:

Effective disease [[Category:Prevention|prevention]] and control depends on several factors that must be present and work together in the community. It all starts with the ability to detect threats to the population's health. A threat can be seen as an undesirable situation that has not yet occurred but may happen unless protective measures are taken. The ability to detect a disease threat implies that we already have basic knowledge about the 'normal occurrence (or burden)' of this disease in the population.

Assessing the disease burden requires a public health workforce competent to collect, analyze and interpret health data from "your" population and the healthcare system infrastructure that allows access to relevant data. [[Field Epidemiology]] methods are central in assessing disease burden.

Detect health threats requires (in addition to the above) continuous monitoring of the burden of disease information of 'your own' and surrounding populations, trends in risk behaviour, characteristics of pathogens (e.g., development of antimicrobial resistance) plus competent staff responsible for the continuous collection, analysis, and interpretation of information. Detecting health threats is sometimes referred to as [[epidemic intelligence]].

Once health threats have been detected and validated, information must be shared with "those who need to know" in the health system. This usually requires translating specific epidemiology jargon into a format used by policy and decision-makers to decide on [[Public Health Interventions|public health interventions]] (preventive and control measures).

This part of this FEMWiki addresses methods that can be used to assess the population's health status and detect and assess health threats. Methods for [[Surveillance principles|Surveillance]], [[Formal Risk Assessment|Risk Assessment]], and [[Outbreak Investigations]] will be described in this section.

[[Public Health Interventions|Interventions]] (public health measures, policy-making, and decision-taking) are discussed in another part of the FEMWIKI. Communication is yet another topic.

<div style="display: inline-block; width: 25%; vertical-align: top; border: 1px solid #000; background-color: #d7effc; padding: 10px; margin: 5px;">
'''FEM PAGE CONTRIBUTORS 2007'''
;Editors
:Arnold Bosman
;FEM Contributors
:Patty Kostkova
</div>
[[Category:Root]]

FEM-WIKI

2025-07-08T07:59:05Z

Bosmana fem: /* Field Epidemiology Manual */

= '''Field Epidemiology Manual''' =

The Field Epidemiology Manual was originally developed in [https://web.archive.org/web/20050315063110/http://www.epiet.org/index.html 2004-2005 by EPIET] as the Field Epidemiology Manual, where facilitators and scientific coordinators contributed chapters. This informal reference book was transformed in 2007 into a WIKI format by the [http://ecdc.europa.eu ECDC] and the City eHealth Research Centre (CeRC - City University, London) <Ref>KOSTKOVA, Patty; SZOMSZOR, Martin. The FEM Wiki Project: A Conversion of a Training Resource for Field Epidemiologists into a Collaborative Web 2.0 Portal. In: Electronic Healthcare: Third International Conference, eHealth 2010, Casablanca, Morocco, December 13-15, 2010, Revised Selected Papers 3. Springer Berlin Heidelberg, 2012. p. 119-126.</ref>, <Ref>KOSTKOVA, Patty; PRIKAZSKY, Vladimir; BOSMAN, Arnold. FEMwiki: Crowdsourcing Semantic Taxonomy and Wiki Input Todomain Experts While Keeping Editorial Control: Mission Possible! In: Proceedings of the 5th International Conference on Digital Health 2015. 2015. p. 27-34.</ref> to support the European Programme for Intervention Epidemiology Training ([https://www.ecdc.europa.eu/en/epiet-euphem EPIET]). Trainers, supervisors, scientific coordinators, and facilitators created draft chapters using the lectures they delivered during the EPIET introductory course. The philosophy of sharing and building knowledge (in particular training materials) led to creation of a collaborative information space for the epidemiological training community - The FEM Wiki.

Eventually, the ECDC decommissioned the FEM Wiki in 2022 and archived the last version as a [https://eva.ecdc.europa.eu/mod/resource/view.php?id=23002 PDF]. Since FEM Wiki content was developed under [https://creativecommons.org/licenses/by-nc-sa/3.0/ Creative Commons], the Dutch Public Health Learning Support Company [https://Transmissible.eu Transmissible] decided to reinstall the Field Epidemiology manual as it was intended: a professional collaborative platform.

The FEMWiki aims to create a library of training materials for field epidemiologists.

FEM Wiki is an open information-sharing platform for all professionals and the lay public interested in public health. It is hosted and funded by ECDC. Platform users provide the content of FEM Wiki and do not necessarily represent the official opinion of Transmissible BV. By contributing content to FEMWIKI, users agree to the conditions described under [https://creativecommons.org/licenses/by-nc-sa/3.0/ Creative Commons] and FEM Wiki users’ [[FEM Users code of conduct|code of conduct]].

Though this platform does not allow as many community activities besides maintaining the Field Epidemiology Manual, we have created an [[Talk:FEM-WIKI|open marketplace where users can discuss and exchange views]]: click on the 'Discussion' tab above. The FEMWIKI is organised into five main volumes. Below is a portal with links to each volume's main articles.

<div style="display: inline-block; width: 30%; vertical-align: top; border: 1px solid #000; padding: 10px; margin: 5px;">
'''Methods Portal'''
<categorytree mode="all">Assessing the burden of disease and risk assessment</categorytree>
<categorytree mode="all">Statistical Concepts</categorytree>
</div>
<div style="display: inline-block; width: 30%; vertical-align: top; border: 1px solid #000; padding: 10px; margin: 5px;">
'''Public Health Portal'''
<categorytree mode="all">Introduction to Public Health and basic concepts</categorytree>
<categorytree mode="all">General Communication</categorytree>
</div>
<div style="display: inline-block; width: 30%; vertical-align: top; border: 1px solid #000; padding: 10px; margin: 5px;">
'''Infection Control'''
<categorytree mode="all">Infection control and hospital hygiene</categorytree>
</div>

=References=
<References/>

=FEMWiki Needs You!=
YES, you should contribute!
If you are a Field Epidemiologist who loves to manage and share knowledge, then you are the one FEMWiki needs. [https://fieldepi.eu/fem-editor/ Request an account here], and we will be delighted to include more Field Epidemiologists in the FEM-editor crew!
[[File:Aunt WIKI needs you2.jpg]]

Category:Public Health Law

2025-06-03T06:47:46Z

Bosmana fem:

Among the tools that governments have to influence the population's health, public health law is critical to reduce illness and preventing premature death. Public health law examines the government's authority at various jurisdictional levels to improve the general population's health within societal limits and norms.

Public health staff may encounter various legal and policy decisions during their public health work, including:

The need to apply isolation or quarantine measures in order to reduce public health threats
Closing schools and other public gatherings, temporarily or for longer periods to limit the spread of communicable diseases
The authority to mandate vaccinations for minors or autonomous adults, including health care workers
Licensing, credentialing and privileging health practitioners from abroad
Inter-disciplinary and inter-departmental management of resources, including personnel, vaccines, shelter etc
Concerns over the liability of public health practitioners during emergencies
Public health measures must be applied to contain and control events with potential health impact.

Public health law typically has three major areas of practice: police power, disease and injury prevention, and the law of populations.

==Police Power==
These areas perpetuate are employed by governmental agencies. Bioterrorism is a growing focus of this practice area in some jurisdictions, yet also more day to day law enforcement situations such as closing down restaurants or production facilities, giving fines to health care facilities and impounding suspected sources of infections fall under this area.

A specific part of this area deals with questions of case management of highly infectious individuals: what are the legal possibilities for restricting movement and treatment against the will of the individual? Public Health Doctors that encounter such situations do well to get in touch with public health law experts for guidance.

==Disease and Injury Prevention==
This broader area of public health law applies legal tools to public health problems associated with disease and injury. Practitioners apply legislation, regulation, litigation (private enforcement), and international law to public health problems using the law as an instrument of public health. Litigation against tobacco companies in the United States provides an excellent example.

==Law of Populations==
Population-based legal analysis is the theoretical foundation of public health law. The law of populations is a relatively new theoretical framework in jurisprudence that seeks to analyze legal problems using the tools of epidemiology. Population-based legal analysis can be applied to traditional public health problems but also has application in environmental law, zoning, evidence, and complex tort.

==Communicable Diseases and Law Enforcement==
Many countries have specific national laws on the control of communicable diseases. Internationally, there are many regulations that address this topic. The International Health Regulations (2005) offer global regulations concerning people and goods, aimed to reduce spread of 'public health events of international concern'. This IHR is one of the most recent in short history of global international health laws, aimed to reduce global spread of diseases. The EU has adopted a specific set of regulations, addressing communicable disease prevention and control in the European Union.

<<<EDITORS WANTED FOR PUBLIC HEALTH LAW CHAPTERS >>>>>
=References=
1. Public Health Law, Wikipedia, accessed 28 January 2011.

2. Centers for Disease Control and Prevention's Public Health Law Program

3. The network for public health law; https://www.networkforphl.org/

[[Category:Introduction to Public Health and basic concepts]]

FEM-WIKI

2025-05-22T12:51:19Z

Bosmana fem: /* Field Epidemiology Manual */

= '''Field Epidemiology Manual''' =

The Field Epidemiology Manual was originally developed in 2004-2005 by EPIET as the Field Epidemiology Manual, where facilitators and scientific coordinators contributed chapters. This informal reference book was transformed in 2007 into a WIKI format by the [http://ecdc.europa.eu ECDC] and the City eHealth Research Centre (CeRC - City University, London) <Ref>KOSTKOVA, Patty; SZOMSZOR, Martin. The FEM Wiki Project: A Conversion of a Training Resource for Field Epidemiologists into a Collaborative Web 2.0 Portal. In: Electronic Healthcare: Third International Conference, eHealth 2010, Casablanca, Morocco, December 13-15, 2010, Revised Selected Papers 3. Springer Berlin Heidelberg, 2012. p. 119-126.</ref>, <Ref>KOSTKOVA, Patty; PRIKAZSKY, Vladimir; BOSMAN, Arnold. FEMwiki: Crowdsourcing Semantic Taxonomy and Wiki Input Todomain Experts While Keeping Editorial Control: Mission Possible! In: Proceedings of the 5th International Conference on Digital Health 2015. 2015. p. 27-34.</ref> to support the European Programme for Intervention Epidemiology Training ([https://www.ecdc.europa.eu/en/epiet-euphem EPIET]). Trainers, supervisors, scientific coordinators, and facilitators created draft chapters using the lectures they delivered during the EPIET introductory course. The philosophy of sharing and building knowledge (in particular training materials) led to creation of a collaborative information space for the epidemiological training community - The FEM Wiki.

Eventually, the ECDC decommissioned the FEM Wiki in 2022 and archived the last version as a [https://eva.ecdc.europa.eu/mod/resource/view.php?id=23002 PDF]. Since FEM Wiki content was developed under [https://creativecommons.org/licenses/by-nc-sa/3.0/ Creative Commons], the Dutch Public Health Learning Support Company [https://Transmissible.eu Transmissible] decided to reinstall the Field Epidemiology manual as it was intended: a professional collaborative platform.

The FEMWiki aims to create a library of training materials for field epidemiologists.

FEM Wiki is an open information-sharing platform for all professionals and the lay public interested in public health. It is hosted and funded by ECDC. Platform users provide the content of FEM Wiki and do not necessarily represent the official opinion of Transmissible BV. By contributing content to FEMWIKI, users agree to the conditions described under [https://creativecommons.org/licenses/by-nc-sa/3.0/ Creative Commons] and FEM Wiki users’ [[FEM Users code of conduct|code of conduct]].

Though this platform does not allow as many community activities besides maintaining the Field Epidemiology Manual, we have created an [[Talk:FEM-WIKI|open marketplace where users can discuss and exchange views]]: click on the 'Discussion' tab above. The FEMWIKI is organised into five main volumes. Below is a portal with links to each volume's main articles.

<div style="display: inline-block; width: 30%; vertical-align: top; border: 1px solid #000; padding: 10px; margin: 5px;">
'''Methods Portal'''
<categorytree mode="all">Assessing the burden of disease and risk assessment</categorytree>
<categorytree mode="all">Statistical Concepts</categorytree>
</div>
<div style="display: inline-block; width: 30%; vertical-align: top; border: 1px solid #000; padding: 10px; margin: 5px;">
'''Public Health Portal'''
<categorytree mode="all">Introduction to Public Health and basic concepts</categorytree>
<categorytree mode="all">General Communication</categorytree>
</div>
<div style="display: inline-block; width: 30%; vertical-align: top; border: 1px solid #000; padding: 10px; margin: 5px;">
'''Infection Control'''
<categorytree mode="all">Infection control and hospital hygiene</categorytree>
</div>

=References=
<References/>

=FEMWiki Needs You!=
YES, you should contribute!
If you are a Field Epidemiologist who loves to manage and share knowledge, then you are the one FEMWiki needs. [https://fieldepi.eu/fem-editor/ Request an account here], and we will be delighted to include more Field Epidemiologists in the FEM-editor crew!
[[File:Aunt WIKI needs you2.jpg]]

Category:Odds

2025-05-21T07:49:35Z

Bosmana fem: /* Absolute Number */

=Absolute Number=
Odds (no synonyms), are expressed as an absolute number.

The odds of an event ("odds", always plural) occurring is the probability (e.g. risk) that this event will occur divided by the probability that the event will not occur. It can also be expressed as the probability that an event will occur divided by "1 minus the probability that the event will occur"<ref>Porta, M. A Dictionary of Epidemiology, Fifth edition. Oxford University press, 2008.</ref>.

P
Odds of event = -----------
1 - P

Note that probabilities allow us to calculate the odds of an event: probabilities and odds are related, but they are not the same thing.

𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲 expresses the chance of an event happening out of the total number of possible outcomes. Probability is the likelihood that an event will occur, expressed as a value between 0 and 1 (e.g., a 20% chance is a probability of 0.2).

𝐎𝐝𝐝𝐬 express the ratio of the event happening to the event not happening. For a 20% chance (probability of 0.2), the odds are 0.25 (i.e., the odds of having the disease is 0.25).

[[File:Prob Vs Odds.png|400px|frameless|Visual representation about the differences between probabilities and odds]]
Visual representation about the differences between probabilities and odds

Odds is a probability measure that is popular in the world of gambling. If we compute the number of people putting money on one horse winning and the number of people putting money on the horse not winning (i.e., putting money on other horses), we can compute the odds of winning. For example, among 3100 persons gambling on horses, 100 persons put money on horse "A" to win, and 3000 do not (they bet on other horses). The odds of winning are then 1/30 (100/3100 divided by 3000/3100 which can be simplified as 100/3000 or 1 / 30). In fact, in gambling, the odds of not winning are preferred and expressed as a ratio X/1. In our example, 30/1, or in words, "thirty to one". This means that for every Euro that you bet, you will receive 30 if you win.

Since we illustrate the population under investigation in epidemiology with a two-by-two table, we will use a table to describe how to calculate odds. In the two-by-two table, the concept of exposure is also included. However, to calculate the odds of disease, it is not necessary to consider that in our population, some might have been exposed to a particular exposure and some not.

===Example 1===
{| class="wikitable"
! !! Developing the disease !! Not developing the disease !! Total
|-
| Exposed || a || b || a+b
|-
| Not exposed || c || d || c+d
|-
| Total || 30 || 70 || 100
|}
The table yields the following calculations:
[[File:0028.risk of dis.png-550x0.png|400px|frameless|left]]

[[File:2086.odds of dis.png-550x0.png|600px|frameless|left]]

Therefore to calculate the odds: divide the risk of getting the disease by the risk of not getting the disease. It is equal to the ratio of the number of people with the disease to the number of people without it in a particular population.

The odds is a measure rarely used in epidemiology. Most often the odds are used to express the odds ratio. A disease-odds ratio is the ratio of the odds of having the disease among the exposed and the odds of having the disease among the unexposed [1]. In other words, the odds ratio is the ratio of the odds of disease observed in 2 subsets of a population.

In you take again the table as an example, the disease-odds ratio will be equal to:

Odds of developing the disease among the exposed: a / b

Odds of developing the disease among the unexposed: c / d

Disease-odds ratio:
[[File:2330.or simple.png-550x0.png|400px|frameless|left]]

As you see by comparing example one, two and three, the risk and the odds approximate each other when the event is rare. When the event occurs frequently the odds overestimate the risk of disease.

For this reason, in many situations (when the disease is rare) the odds ratio can estimate the risk ratio.

===Example 2===

{| class="wikitable"
! !! Developing the disease !! Not developing the disease !! Total
|-
| Exposed || a || b || a+b
|-
| Not exposed || c || d || c+d
|-
| Total || 50 || 99 950 || 100 000
|}

Risk of disease = 50 / 100000 = 0.00050000

Odds of disease (50 / 100000) / 1 - (50/100000) = 0.00050025

When getting the disease is rare, the risk of disease approximates the odds of disease.

===Example 3===

{| class="wikitable"
! !! Developing the disease !! Not developing the disease !! Total
|-
| Exposed || a || b || a+b
|-
| Not exposed || c || d || c+d
|-
| Total || 59 || 950 || 1000
|}

Risk of disease = 50 / 1000 = 0.05000

Odds of disease (50 / 1000) / 1 - (50/1000) = 0.05263

=References=
<References/>

=Credits=
===FEM Editor 2007===
* Sabrina Bacci
===Original Authors===
* Alain Moren
* Marta Valenciano
* Arnold Bosman
===FEM Contributors===
* Arnold Bosman
* Naomi Boxall
* Vladimir Prikazsky
* Aileen Kitching
* Lisa Lazareck
* Sabrina Bacci

[[Category:Measures of Disease Occurrence]]

Category:Odds

2025-05-21T07:48:40Z

Bosmana fem: /* Absolute Number */

=Absolute Number=
Odds (no synonyms), are expressed as an absolute number.

The odds of an event ("odds", always plural) occurring is the probability (e.g. risk) that this event will occur divided by the probability that the event will not occur. It can also be expressed as the probability that an event will occur divided by "1 minus the probability that the event will occur"<ref>Porta, M. A Dictionary of Epidemiology, Fifth edition. Oxford University press, 2008.</ref>.

P
Odds of event = -----------
1 - P

Note that probabilities allow us to calculate the odds of an event: probabilities and odds are related, but they are not the same thing.

𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲 expresses the chance of an event happening out of the total number of possible outcomes. Probability is the likelihood that an event will occur, expressed as a value between 0 and 1 (e.g., a 20% chance is a probability of 0.2).

𝐎𝐝𝐝𝐬 express the ratio of the event happening to the event not happening. For a 20% chance (probability of 0.2), the odds are 0.25 (i.e., the odds of having the disease is 0.25).

[[File:Prob Vs Odds.png|400px|frameless|Visual representation about the differences between probabilities and odds]]

Odds is a probability measure that is popular in the world of gambling. If we compute the number of people putting money on one horse winning and the number of people putting money on the horse not winning (i.e., putting money on other horses), we can compute the odds of winning. For example, among 3100 persons gambling on horses, 100 persons put money on horse "A" to win, and 3000 do not (they bet on other horses). The odds of winning are then 1/30 (100/3100 divided by 3000/3100 which can be simplified as 100/3000 or 1 / 30). In fact, in gambling, the odds of not winning are preferred and expressed as a ratio X/1. In our example, 30/1, or in words, "thirty to one". This means that for every Euro that you bet, you will receive 30 if you win.

Since we illustrate the population under investigation in epidemiology with a two-by-two table, we will use a table to describe how to calculate odds. In the two-by-two table, the concept of exposure is also included. However, to calculate the odds of disease, it is not necessary to consider that in our population, some might have been exposed to a particular exposure and some not.

===Example 1===
{| class="wikitable"
! !! Developing the disease !! Not developing the disease !! Total
|-
| Exposed || a || b || a+b
|-
| Not exposed || c || d || c+d
|-
| Total || 30 || 70 || 100
|}
The table yields the following calculations:
[[File:0028.risk of dis.png-550x0.png|400px|frameless|left]]

[[File:2086.odds of dis.png-550x0.png|600px|frameless|left]]

Therefore to calculate the odds: divide the risk of getting the disease by the risk of not getting the disease. It is equal to the ratio of the number of people with the disease to the number of people without it in a particular population.

The odds is a measure rarely used in epidemiology. Most often the odds are used to express the odds ratio. A disease-odds ratio is the ratio of the odds of having the disease among the exposed and the odds of having the disease among the unexposed [1]. In other words, the odds ratio is the ratio of the odds of disease observed in 2 subsets of a population.

In you take again the table as an example, the disease-odds ratio will be equal to:

Odds of developing the disease among the exposed: a / b

Odds of developing the disease among the unexposed: c / d

Disease-odds ratio:
[[File:2330.or simple.png-550x0.png|400px|frameless|left]]

As you see by comparing example one, two and three, the risk and the odds approximate each other when the event is rare. When the event occurs frequently the odds overestimate the risk of disease.

For this reason, in many situations (when the disease is rare) the odds ratio can estimate the risk ratio.

===Example 2===

{| class="wikitable"
! !! Developing the disease !! Not developing the disease !! Total
|-
| Exposed || a || b || a+b
|-
| Not exposed || c || d || c+d
|-
| Total || 50 || 99 950 || 100 000
|}

Risk of disease = 50 / 100000 = 0.00050000

Odds of disease (50 / 100000) / 1 - (50/100000) = 0.00050025

When getting the disease is rare, the risk of disease approximates the odds of disease.

===Example 3===

{| class="wikitable"
! !! Developing the disease !! Not developing the disease !! Total
|-
| Exposed || a || b || a+b
|-
| Not exposed || c || d || c+d
|-
| Total || 59 || 950 || 1000
|}

Risk of disease = 50 / 1000 = 0.05000

Odds of disease (50 / 1000) / 1 - (50/1000) = 0.05263

=References=
<References/>

=Credits=
===FEM Editor 2007===
* Sabrina Bacci
===Original Authors===
* Alain Moren
* Marta Valenciano
* Arnold Bosman
===FEM Contributors===
* Arnold Bosman
* Naomi Boxall
* Vladimir Prikazsky
* Aileen Kitching
* Lisa Lazareck
* Sabrina Bacci

[[Category:Measures of Disease Occurrence]]

Category:Odds

2025-05-21T07:47:55Z

Bosmana fem: /* Absolute Number */

=Absolute Number=
Odds (no synonyms), are expressed as an absolute number.

The odds of an event ("odds", always plural) occurring is the probability (e.g. risk) that this event will occur divided by the probability that the event will not occur. It can also be expressed as the probability that an event will occur divided by "1 minus the probability that the event will occur"<ref>Porta, M. A Dictionary of Epidemiology, Fifth edition. Oxford University press, 2008.</ref>.

P
Odds of event = -----------
1 - P

Note that probabilities allow us to calculate the odds of an event: probabilities and odds are related, but they are not the same thing.

𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲 expresses the chance of an event happening out of the total number of possible outcomes. Probability is the likelihood that an event will occur, expressed as a value between 0 and 1 (e.g., a 20% chance is a probability of 0.2).

𝐎𝐝𝐝𝐬 express the ratio of the event happening to the event not happening. For a 20% chance (probability of 0.2), the odds are 0.25 (i.e., the odds of having the disease is 0.25).

[[File:Prob Vs Odds.png|600px|frame|Visual representation about the differences between probabilities and odds]]

Odds is a probability measure that is popular in the world of gambling. If we compute the number of people putting money on one horse winning and the number of people putting money on the horse not winning (i.e., putting money on other horses), we can compute the odds of winning. For example, among 3100 persons gambling on horses, 100 persons put money on horse "A" to win, and 3000 do not (they bet on other horses). The odds of winning are then 1/30 (100/3100 divided by 3000/3100 which can be simplified as 100/3000 or 1 / 30). In fact, in gambling, the odds of not winning are preferred and expressed as a ratio X/1. In our example, 30/1, or in words, "thirty to one". This means that for every Euro that you bet, you will receive 30 if you win.

Since we illustrate the population under investigation in epidemiology with a two-by-two table, we will use a table to describe how to calculate odds. In the two-by-two table, the concept of exposure is also included. However, to calculate the odds of disease, it is not necessary to consider that in our population, some might have been exposed to a particular exposure and some not.

===Example 1===
{| class="wikitable"
! !! Developing the disease !! Not developing the disease !! Total
|-
| Exposed || a || b || a+b
|-
| Not exposed || c || d || c+d
|-
| Total || 30 || 70 || 100
|}
The table yields the following calculations:
[[File:0028.risk of dis.png-550x0.png|400px|frameless|left]]

[[File:2086.odds of dis.png-550x0.png|600px|frameless|left]]

Therefore to calculate the odds: divide the risk of getting the disease by the risk of not getting the disease. It is equal to the ratio of the number of people with the disease to the number of people without it in a particular population.

The odds is a measure rarely used in epidemiology. Most often the odds are used to express the odds ratio. A disease-odds ratio is the ratio of the odds of having the disease among the exposed and the odds of having the disease among the unexposed [1]. In other words, the odds ratio is the ratio of the odds of disease observed in 2 subsets of a population.

In you take again the table as an example, the disease-odds ratio will be equal to:

Odds of developing the disease among the exposed: a / b

Odds of developing the disease among the unexposed: c / d

Disease-odds ratio:
[[File:2330.or simple.png-550x0.png|400px|frameless|left]]

As you see by comparing example one, two and three, the risk and the odds approximate each other when the event is rare. When the event occurs frequently the odds overestimate the risk of disease.

For this reason, in many situations (when the disease is rare) the odds ratio can estimate the risk ratio.

===Example 2===

{| class="wikitable"
! !! Developing the disease !! Not developing the disease !! Total
|-
| Exposed || a || b || a+b
|-
| Not exposed || c || d || c+d
|-
| Total || 50 || 99 950 || 100 000
|}

Risk of disease = 50 / 100000 = 0.00050000

Odds of disease (50 / 100000) / 1 - (50/100000) = 0.00050025

When getting the disease is rare, the risk of disease approximates the odds of disease.

===Example 3===

{| class="wikitable"
! !! Developing the disease !! Not developing the disease !! Total
|-
| Exposed || a || b || a+b
|-
| Not exposed || c || d || c+d
|-
| Total || 59 || 950 || 1000
|}

Risk of disease = 50 / 1000 = 0.05000

Odds of disease (50 / 1000) / 1 - (50/1000) = 0.05263

=References=
<References/>

=Credits=
===FEM Editor 2007===
* Sabrina Bacci
===Original Authors===
* Alain Moren
* Marta Valenciano
* Arnold Bosman
===FEM Contributors===
* Arnold Bosman
* Naomi Boxall
* Vladimir Prikazsky
* Aileen Kitching
* Lisa Lazareck
* Sabrina Bacci

[[Category:Measures of Disease Occurrence]]

Category:Odds

2025-05-21T07:46:43Z

Bosmana fem:

=Absolute Number=
Odds (no synonyms), are expressed as an absolute number.

The odds of an event ("odds", always plural) occurring is the probability (e.g. risk) that this event will occur divided by the probability that the event will not occur. It can also be expressed as the probability that an event will occur divided by "1 minus the probability that the event will occur"<ref>Porta, M. A Dictionary of Epidemiology, Fifth edition. Oxford University press, 2008.</ref>.

P
Odds of event = -----------
1 - P

Note that probabilities allow us to calculate the odds of an event: probabilities and odds are related, but they are not the same thing.

𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲 expresses the chance of an event happening out of the total number of possible outcomes. Probability is the likelihood that an event will occur, expressed as a value between 0 and 1 (e.g., a 20% chance is a probability of 0.2).

𝐎𝐝𝐝𝐬 express the ratio of the event happening to the event not happening. For a 20% chance (probability of 0.2), the odds are 0.25 (i.e., the odds of having the disease is 0.25).

<gallery>
Prob Vs Odds.png| Visual representation about the differences between probabilities and odds
</gallery>

Odds is a probability measure that is popular in the world of gambling. If we compute the number of people putting money on one horse winning and the number of people putting money on the horse not winning (i.e., putting money on other horses), we can compute the odds of winning. For example, among 3100 persons gambling on horses, 100 persons put money on horse "A" to win, and 3000 do not (they bet on other horses). The odds of winning are then 1/30 (100/3100 divided by 3000/3100 which can be simplified as 100/3000 or 1 / 30). In fact, in gambling, the odds of not winning are preferred and expressed as a ratio X/1. In our example, 30/1, or in words, "thirty to one". This means that for every Euro that you bet, you will receive 30 if you win.

Since we illustrate the population under investigation in epidemiology with a two-by-two table, we will use a table to describe how to calculate odds. In the two-by-two table, the concept of exposure is also included. However, to calculate the odds of disease, it is not necessary to consider that in our population, some might have been exposed to a particular exposure and some not.

===Example 1===
{| class="wikitable"
! !! Developing the disease !! Not developing the disease !! Total
|-
| Exposed || a || b || a+b
|-
| Not exposed || c || d || c+d
|-
| Total || 30 || 70 || 100
|}
The table yields the following calculations:
[[File:0028.risk of dis.png-550x0.png|400px|frameless|left]]

[[File:2086.odds of dis.png-550x0.png|600px|frameless|left]]

Therefore to calculate the odds: divide the risk of getting the disease by the risk of not getting the disease. It is equal to the ratio of the number of people with the disease to the number of people without it in a particular population.

The odds is a measure rarely used in epidemiology. Most often the odds are used to express the odds ratio. A disease-odds ratio is the ratio of the odds of having the disease among the exposed and the odds of having the disease among the unexposed [1]. In other words, the odds ratio is the ratio of the odds of disease observed in 2 subsets of a population.

In you take again the table as an example, the disease-odds ratio will be equal to:

Odds of developing the disease among the exposed: a / b

Odds of developing the disease among the unexposed: c / d

Disease-odds ratio:
[[File:2330.or simple.png-550x0.png|400px|frameless|left]]

As you see by comparing example one, two and three, the risk and the odds approximate each other when the event is rare. When the event occurs frequently the odds overestimate the risk of disease.

For this reason, in many situations (when the disease is rare) the odds ratio can estimate the risk ratio.

===Example 2===

{| class="wikitable"
! !! Developing the disease !! Not developing the disease !! Total
|-
| Exposed || a || b || a+b
|-
| Not exposed || c || d || c+d
|-
| Total || 50 || 99 950 || 100 000
|}

Risk of disease = 50 / 100000 = 0.00050000

Odds of disease (50 / 100000) / 1 - (50/100000) = 0.00050025

When getting the disease is rare, the risk of disease approximates the odds of disease.

===Example 3===

{| class="wikitable"
! !! Developing the disease !! Not developing the disease !! Total
|-
| Exposed || a || b || a+b
|-
| Not exposed || c || d || c+d
|-
| Total || 59 || 950 || 1000
|}

Risk of disease = 50 / 1000 = 0.05000

Odds of disease (50 / 1000) / 1 - (50/1000) = 0.05263

=References=
<References/>

=Credits=
===FEM Editor 2007===
* Sabrina Bacci
===Original Authors===
* Alain Moren
* Marta Valenciano
* Arnold Bosman
===FEM Contributors===
* Arnold Bosman
* Naomi Boxall
* Vladimir Prikazsky
* Aileen Kitching
* Lisa Lazareck
* Sabrina Bacci

[[Category:Measures of Disease Occurrence]]

Category:Odds

2025-05-21T07:46:09Z

Bosmana fem: /* Absolute Number */

=Absolute Number=
Odds (no synonyms), are expressed as an absolute number.

The odds of an event ("odds", always plural) occurring is the probability (e.g. risk) that this event will occur divided by the probability that the event will not occur. It can also be expressed as the probability that an event will occur divided by "1 minus the probability that the event will occur"<ref>Porta, M. A Dictionary of Epidemiology, Fifth edition. Oxford University press, 2008.</ref>.

P
Odds of event = -----------
1 - P

Note that probabilities allow us to calculate the odds of an event: probabilities and odds are related, but they are not the same thing.

𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲 expresses the chance of an event happening out of the total number of possible outcomes. Probability is the likelihood that an event will occur, expressed as a value between 0 and 1 (e.g., a 20% chance is a probability of 0.2).

𝐎𝐝𝐝𝐬 express the ratio of the event happening to the event not happening. For a 20% chance (probability of 0.2), the odds are 0.25 (i.e., the odds of having the disease is 0.25).

<gallery>
Prob Vs Odds.png| Visual representation about the differences between probabilities and odds
</gallery>

[[File:Prob Vs Odds.png|frame|Visual representation about the differences between probabilities and odds]]

Odds is a probability measure that is popular in the world of gambling. If we compute the number of people putting money on one horse winning and the number of people putting money on the horse not winning (i.e., putting money on other horses), we can compute the odds of winning. For example, among 3100 persons gambling on horses, 100 persons put money on horse "A" to win, and 3000 do not (they bet on other horses). The odds of winning are then 1/30 (100/3100 divided by 3000/3100 which can be simplified as 100/3000 or 1 / 30). In fact, in gambling, the odds of not winning are preferred and expressed as a ratio X/1. In our example, 30/1, or in words, "thirty to one". This means that for every Euro that you bet, you will receive 30 if you win.

Since we illustrate the population under investigation in epidemiology with a two-by-two table, we will use a table to describe how to calculate odds. In the two-by-two table, the concept of exposure is also included. However, to calculate the odds of disease, it is not necessary to consider that in our population, some might have been exposed to a particular exposure and some not.

===Example 1===
{| class="wikitable"
! !! Developing the disease !! Not developing the disease !! Total
|-
| Exposed || a || b || a+b
|-
| Not exposed || c || d || c+d
|-
| Total || 30 || 70 || 100
|}
The table yields the following calculations:
[[File:0028.risk of dis.png-550x0.png|400px|frameless|left]]

[[File:2086.odds of dis.png-550x0.png|600px|frameless|left]]

Therefore to calculate the odds: divide the risk of getting the disease by the risk of not getting the disease. It is equal to the ratio of the number of people with the disease to the number of people without it in a particular population.

The odds is a measure rarely used in epidemiology. Most often the odds are used to express the odds ratio. A disease-odds ratio is the ratio of the odds of having the disease among the exposed and the odds of having the disease among the unexposed [1]. In other words, the odds ratio is the ratio of the odds of disease observed in 2 subsets of a population.

In you take again the table as an example, the disease-odds ratio will be equal to:

Odds of developing the disease among the exposed: a / b

Odds of developing the disease among the unexposed: c / d

Disease-odds ratio:
[[File:2330.or simple.png-550x0.png|400px|frameless|left]]

As you see by comparing example one, two and three, the risk and the odds approximate each other when the event is rare. When the event occurs frequently the odds overestimate the risk of disease.

For this reason, in many situations (when the disease is rare) the odds ratio can estimate the risk ratio.

===Example 2===

{| class="wikitable"
! !! Developing the disease !! Not developing the disease !! Total
|-
| Exposed || a || b || a+b
|-
| Not exposed || c || d || c+d
|-
| Total || 50 || 99 950 || 100 000
|}

Risk of disease = 50 / 100000 = 0.00050000

Odds of disease (50 / 100000) / 1 - (50/100000) = 0.00050025

When getting the disease is rare, the risk of disease approximates the odds of disease.

===Example 3===

{| class="wikitable"
! !! Developing the disease !! Not developing the disease !! Total
|-
| Exposed || a || b || a+b
|-
| Not exposed || c || d || c+d
|-
| Total || 59 || 950 || 1000
|}

Risk of disease = 50 / 1000 = 0.05000

Odds of disease (50 / 1000) / 1 - (50/1000) = 0.05263

=References=
<References/>

=Credits=
===FEM Editor 2007===
* Sabrina Bacci
===Original Authors===
* Alain Moren
* Marta Valenciano
* Arnold Bosman
===FEM Contributors===
* Arnold Bosman
* Naomi Boxall
* Vladimir Prikazsky
* Aileen Kitching
* Lisa Lazareck
* Sabrina Bacci

[[Category:Measures of Disease Occurrence]]

Key definitions in infectious diseases epidemiology

2025-05-19T15:09:38Z

Bosmana fem:

Infectious disease epidemiology shares the same general conceptual framework as ‘non-infectious disease’ epidemiology. It seeks to understand the causes and distribution of infectious diseases in populations with the aim of controlling them. However, there are specific epidemiological concepts/terms that are mainly related to infectious diseases:

== Infection ==

'''Infection''' refers to the entry and multiplication of an infectious agent in the body of humans or animals, where it may or may not lead to clinical symptoms.

An infection can be:
* '''Endogenous''' – originating from the host’s own microbial flora (e.g. urinary tract infection from intestinal flora)
* '''Exogenous''' – acquired from the environment, other individuals, animals, or contaminated objects

=== Source of infection ===
The term '''source of infection''' refers to the origin from which a susceptible host acquires the infectious agent.
It can be:
* An infected human (symptomatic or asymptomatic)
* An infected animal (reservoir or vector)
* A contaminated environment (e.g. surfaces, food, water)

''Example: A cholera patient excreting Vibrio cholerae in stools contaminates drinking water. The patient is the source of infection, the water is the vehicle of transmission.''

== Contamination ==

'''Contamination''' refers to the presence of infectious agents on inanimate objects, surfaces, food, or water, without any multiplication or clinical infection in the object or material.

Contamination may or may not lead to infection in a host. Contaminated materials can act as:
* '''Vehicles''' (e.g. contaminated food, water)
* '''Fomites''' (e.g. door handles, bedding)
* '''Environmental reservoirs''' (e.g. soil, surfaces)

''Example: A septic tank overflow that pollutes a drinking water well is a source of contamination. People drinking from the well may become infected, depending on exposure and susceptibility.''

== Comparison table ==

{| class="wikitable"
! Aspect !! Infection !! Contamination
|-
| Definition || Multiplication of pathogens in a host || Presence of pathogens on a surface or substance
|-
| Host involvement || Occurs in living organisms (humans/animals) || Occurs on inanimate objects or materials
|-
| Clinical symptoms || May or may not occur || Never occurs (non-biological host)
|-
| Role in outbreaks || Direct cause of disease || May facilitate transmission, but not always infectious
|}

==Agent==
A disease agent is a biological agent that causes a disease. This can be a virus, bacterium, fungus, parasite, or other type of microorganism, as well as certain toxins and other substances that can cause disease. Disease agents can be transmitted from person to person, or through contact with contaminated surfaces, food, water, or other sources. They can cause a wide range of diseases, ranging from mild to severe, and can have serious consequences for individuals and communities. Some examples of disease agents include the influenza virus, which causes the flu; the bacterium Escherichia coli, which can cause food poisoning; and the parasite Plasmodium, which causes malaria.

==Chain of transmission==
The chain of transmission refers to the steps or stages through which a disease agent is transmitted from one person or host to another. It involves a series of events that occur in a specific order, starting with the source of the disease agent and ending with the infection of a new host. The chain of transmission can be broken at any point, which can help to prevent the spread of the disease.
There are several key elements in the chain of transmission:
# The source: This is the source of the disease agent, which can be a person, animal, or environment.
# The reservoir: This is the place where the disease agent can survive and multiply.
# The mode of transmission: This is the way in which the disease agent is transmitted from the source to the host. This can be through direct contact (such as through touching, kissing, or sexual contact), indirect contact (such as through contaminated objects or surfaces), or through the air (such as through coughing or sneezing).
# The host: This is the person or animal that becomes infected with the disease agent.
#The environment: This includes the physical, social, and cultural factors that can influence the transmission of the disease agent.

Breaking the chain of transmission involves interrupting one or more of these elements, which can help to prevent the spread of the disease. This can be achieved through measures such as vaccination, hygiene practices, quarantine, and other public health interventions.

==Contagiousness==
Contagiousness refers to the ability of a disease to be transmitted from one person or animal to another. A disease that is highly contagious can be transmitted easily and quickly, often with just brief contact or through the air. A disease that is less contagious may require more prolonged or close contact in order to be transmitted.

The contagiousness of a disease can depend on several factors, including the type of disease agent (such as a virus or bacterium), the mode of transmission (such as through direct or indirect contact, or through the air), and the susceptibility of the host (such as a person or animal). Some diseases, such as the common cold, are highly contagious and can be transmitted easily through contact with respiratory secretions or contaminated surfaces. Other diseases, such as HIV, are less contagious and require more specific modes of transmission, such as through sexual contact or sharing needles.

Understanding a disease's contagiousness is important in preventing its spread and controlling outbreaks. Public health measures, such as vaccination and hygiene practices, can help reduce a disease's contagiousness and prevent its transmission.

==Epidemic curve==
An epidemic curve is a graphical representation of the number of cases of a specific disease that occur over time. It is used to understand the spread and impact of an outbreak, as well as to identify patterns and trends in the data.

The epidemic curve is usually plotted on a graph, with the x-axis representing time (often in days or weeks) and the y-axis representing the number of cases. The shape of the curve can provide important information about the nature of the outbreak, such as the rate at which the disease is spreading and the population groups that are most affected.

There are several types of epidemic curves that can be used to represent different types of outbreaks:
# A linear epidemic curve shows a constant rate of disease transmission over time.
# An exponential epidemic curve shows a rapid increase in cases over time, often indicating a highly contagious disease.
# A log-linear epidemic curve shows a slowing of the rate of disease transmission over time, often indicating that public health interventions are having an effect.
# A logistic epidemic curve shows a slowing of the rate of disease transmission, followed by a plateau and then a decline, often indicating that the outbreak has reached its peak and is starting to decline.
Understanding the shape of the epidemic curve can help public health officials to identify the most effective interventions for controlling the outbreak and preventing further transmission of the disease.

==Generation time==
The time that elapses between the onset of symptoms in the primary case and the onset of symptoms in the secondary case. Generation time is a term used to describe the time it takes for a disease to be transmitted from one person to another or from one generation of hosts to the next. In the context of an epidemic, generation time can be an important factor in understanding the spread and impact of the disease and in identifying strategies for controlling the outbreak.

The generation time of a disease can vary depending on the specific disease agent and the characteristics of the host population. Some diseases have a relatively short generation time, meaning that they can be transmitted quickly and can lead to rapid outbreaks. Other diseases have a longer generation time, meaning they may take longer to spread and may have a slower impact on the population.

Understanding the generation time of a disease can help public health officials to identify the most effective interventions for controlling the outbreak and preventing further transmission of the disease. This can include measures such as vaccination, quarantine, and hygiene practices, as well as strategies for reducing the number of contacts between individuals in the population.

==Herd immunity==
Herd immunity, also known as community immunity, refers to the protective effect that occurs when a high proportion of a population is immune to a specific disease. This can be achieved through natural immunity (for example, by recovering from the disease) or through vaccination.

When a large proportion of a population is immune to a disease, it is more difficult for the disease to spread, because there are fewer individuals who are susceptible to infection. This can provide protection for those who are not immune, including those who are too young to be vaccinated, those who are unable to be vaccinated due to underlying health conditions, and those who have not yet had the opportunity to be vaccinated.

Herd immunity can be an important factor in controlling the spread of infectious diseases and preventing outbreaks. The level of immunity needed to achieve herd immunity varies depending on the specific disease and the characteristics of the population. Some diseases, such as measles, require a relatively high level of immunity (around 95%) in order to achieve herd immunity, while others, such as pertussis (whooping cough), require a lower level of immunity (around 80%).

It is important to maintain high vaccination rates in order to protect the population from infectious diseases and to maintain herd immunity. This can help to prevent outbreaks and protect those who are most vulnerable to serious illness or complications from the disease.

==Host==
A host is an individual or animal that is infected with a disease agent, such as a virus or bacterium. The host serves as a source of the disease, and can transmit the disease to other individuals or animals through the process of transmission.

The term "host" can also refer to the individual or animal that provides a habitat or environment for a particular disease agent. For example, a mosquito may be the host for a parasite that causes malaria, while a human may be the host for a virus that causes the flu.
A primary host is where a parasite reaches maturity or passes its sexual stage. A secondary host is where a parasite is in a larval or asexual stage.

Understanding the role of hosts in the transmission of disease is an important aspect of epidemiology, as it helps public health officials to identify the sources of outbreaks and to develop strategies for preventing the spread of the disease. This can include measures such as vaccination, quarantine, and hygiene practices, as well as strategies for reducing the number of contacts between individuals in the population.

==Incubation period==
The incubation period is the time between when an individual is exposed to a disease agent (such as a virus or bacterium) and when they develop symptoms of the disease. The incubation period can vary depending on the specific disease, as well as the individual's age, immune system, and other factors.

During the incubation period, the disease agent may be multiplying and spreading in the body, but the individual is not yet experiencing any symptoms. This can make it difficult to identify the source of the infection, as the infected individual may not realize that they are carrying the disease and may be unknowingly spreading it to others.

Understanding the incubation period of a disease is important in terms of preventing the spread of the disease and controlling outbreaks. For example, if the incubation period is relatively long, public health officials may need to implement quarantine measures for a longer period of time in order to prevent the transmission of the disease. If the incubation period is shorter, more immediate interventions may be necessary to control the spread of the disease.

==Index case==
First case of a disease to be identified at the start of an outbreak. The index case is the first patient that indicates the existence of an outbreak. It does not necessarily mean that it was the outbreak's first case. Earlier cases may be found and are labeled primary case, secondary case, tertiary case, etc.

==Primary case==
The primary case (or source case, or patient zero), is the first case of a disease in an outbreak or epidemic. This individual introduced the disease agent to the population and gave rise to the outbreak or epidemic.

==Latent period==
The latent period is the time between the initial infection and the onset of infectiousness. In other words, it is the period during which the pathogen replicates within the infected individual's body, but the individual is not yet contagious.

==Outbreak==
Term used in epidemiology to describe an occurrence of disease greater than would otherwise be expected at a particular time and place. It may affect a small and localized group or impact upon thousands of people across an entire continent. Two linked cases of a rare infectious disease may be sufficient to constitute an outbreak.

==Epidemic==
Generally, ‘epidemic’ refers to large outbreak. But the difference between ‘epidemic’ and ‘outbreak’ remains subjective. Some have proposed that an epidemic is an outbreak that affects a region in a country of a group of countries.

==Pandemic==
Outbreak of disease around the globe.

==Pathogen==
A pathogen is any agent that can cause disease in a host organism. Pathogens are typically infectious agents such as viruses, bacteria, fungi, or parasites, but they can also be non-infectious agents such as physical agents (e.g., radiation) or chemical agents (e.g., toxins).

Pathogens can cause a wide range of diseases in humans and other animals, ranging from mild infections to serious, life-threatening illnesses. Some common examples of pathogens include the bacteria that cause tuberculosis and salmonella, the viruses that cause influenza and HIV, and the fungi that cause athlete's foot and ringworm.

Pathogens are able to enter the body of a host in a variety of ways, including through contact with infected bodily fluids (such as blood or saliva), through inhalation of respiratory secretions, or through ingestion of contaminated food or water. Once inside the body, the pathogen may begin to replicate and cause damage to cells and tissues, leading to the development of symptoms of disease.

==Reproductive rate==
The reproductive rate of an infection refers to the number of new infections that are generated by each infected individual during the course of their illness. It is often expressed as the basic reproductive number, or R0 (pronounced "R-naught"), which is the average number of new infections produced by a single infected individual in a population that is fully susceptible to the infection.

The reproductive rate is an important concept in the field of epidemiology, as it helps to predict the spread and potential impact of an infectious disease within a population. A high reproductive rate can indicate that an infection is highly contagious and may spread rapidly through a population, while a low reproductive rate may suggest that the infection is less easily transmitted.

The reproductive rate of an infection can vary widely depending on a number of factors, including the mode of transmission (e.g., respiratory droplets, contact with bodily fluids), the severity of the disease, and the effectiveness of control measures (such as vaccination or isolation of infected individuals).

To calculate the reproductive rate of an infection, epidemiologists use a variety of statistical and mathematical models that take into account the number of new infections, the length of the infectious period, and the size of the population. These models can help to identify the key drivers of transmission and inform the development of strategies to control the spread of the infection.

==Transmission route==
A transmission route refers to the way in which a pathogen (such as a virus or bacterium) is transmitted from one host to another. Transmission routes can vary depending on the specific pathogen and the characteristics of the host population.

Some common transmission routes for infectious diseases include:

* '''Respiratory droplets''': Many respiratory infections, such as the common cold and influenza, are transmitted through respiratory droplets that are released into the air when an infected person talks, coughs, or sneezes. Other people can inhale these droplets and then become infected.
* '''Contact with bodily fluids''': Some infections, such as HIV and hepatitis B, are transmitted through contact with infected blood or other bodily fluids. This can occur through sexual contact, injection drug use, or accidental exposure to contaminated needles or other medical equipment.
* '''Food and water''': Some infections, such as norovirus and salmonella, are transmitted through contaminated food or water. These pathogens can be present in undercooked or raw food, or water contaminated with feces.
* '''Insects''': Some infections, such as malaria and West Nile virus, are transmitted through the bites of infected insects, such as mosquitoes or ticks.
* '''Fomites''': Fomites are inanimate objects that can become contaminated with pathogens and serve as a means of transmission. Examples of fomites include towels, bedding, and other household items that can harbor pathogens and transmit them to other people.

Understanding the transmission route of an infection is important in the control and prevention of the disease, as it can inform the development of strategies such as vaccination, isolation of infected individuals, and the implementation of infection control measures.

==Direct transmission==
Direct and immediate transfer of infectious agents to a susceptible host. This may be through direct contact such as touching, biting, kissing or sexual intercourse, or by the direct projection of droplet (droplet spread) spraying onto eyes, nose or mouth of other people. Droplet spread is usually limited to short distances, such as 1 meter or less.

==Vertical transmission==
A specific direct transmission is between mother and child during pregnancy or childbirth.

==Indirect transmission==
Transmission of infectious organisms from a source through objects (vehicles) or insects (vectors).

==Vehicle-borne==
Infectious agents can reach susceptible hosts through transport on inanimate objects (=fomites) such as toys, handkerchiefs, soiled clothes, bedding, medical instruments, food, water, blood products or any other substance that can be contaminated. Some vehicles allow multiplication of the infectious agent (e.g. salmonella in food), though this is not always the case.

==Vector-borne==
When insects transfer infectious agents to susceptible hosts, they act as 'vectors' of the infection.

==Airborne transmission==
Microbial aerosols are suspensions of particles (fluid or solid) in the air consisting partially or wholly of microorganisms. They may remain suspended in the air for prolonged periods of time (as opposed to droplets that are too large in diameter and fall to the ground relatively fast). This transmission route works particularly efficiently for viruses such as the measles virus.

==Reservoir==
a reservoir is a place or host where a pathogen (such as a virus or bacterium) can survive, grow, and reproduce. A reservoir can be either a living organism (such as a human, animal, or plant) or an inanimate object (such as soil or water).

The concept of a reservoir is important in epidemiology because it helps to understand how infectious diseases are transmitted and how they can be controlled. For example, if the reservoir for a particular pathogen is an animal, such as a rodent or a bird, understanding the habits and habitats of that animal can help to identify potential sources of infection and implement control measures to reduce the risk of transmission to humans.

Some common examples of reservoirs for infectious diseases include:
* Humans: Many infectious diseases, such as influenza and measles, have humans as their primary reservoir.
* Animals: Many infections, such as rabies and West Nile virus, are transmitted to humans from animals. The animal reservoir for these infections may be wild animals, domestic pets, or livestock.
* Water: Some infections, such as cholera and typhoid fever, are transmitted through contaminated water. In these cases, the water can act as a reservoir for the pathogen.
* Soil: Some infections, such as tetanus and anthrax, are transmitted through contact with contaminated soil. In these cases, the soil can act as a reservoir for the pathogen.

Understanding the reservoir for an infectious disease is important for developing control measures and designing effective interventions to prevent the spread of the infection.

==Susceptibility==
A susceptible individual (sometimes known simply as a susceptible) is a population member at risk of becoming infected by a disease.

==Source==
The source is the place or host from which the pathogen is transmitted to another host.

To understand the difference between a reservoir and a source, it can be helpful to think of a reservoir as a place where the pathogen is "stored" and a source as a place where the pathogen is "released." For example, a person with an infection may act as a reservoir for the pathogen (storing it within their body), while the respiratory secretions they release when they sneeze or cough may act as the source of the infection (releasing the pathogen into the environment).

In some cases, the reservoir and the source for an infectious disease may be the same. For example, if a person with an infection sneezes or coughs, they may both act as the reservoir (storing the pathogen within their body) and the source (releasing the pathogen into the environment). In other cases, the reservoir and the source may be different. For example, a mosquito may act as the source of an infection by transmitting the pathogen to a human through its bite, while the human may act as the reservoir for the pathogen.

There are several ways this source can infect people:

===Common source outbreaks===
Outbreaks, where all (or most) cases were infected by the same source, are called common source outbreaks.

===Point source outbreaks===
Common source outbreaks where the source has infected cases at one particular geographical location and during a short period of time. In such situations, the source is located 'at a single point in time and place'. These outbreaks have a typical bell-shaped epidemic curve that increases sharply, peaks, and then declines sharply, reflecting the normal distribution of the incubation period of the causative agent in humans. For this reason, the epidemic curve of a point source outbreak can help identify the moment of transmission (i.e., when all cases have been exposed to the source).

===Continuing common source outbreaks===
Outbreaks where all (or most) cases have been infected by the same source over a prolonged period of time. The shape of the epidemic curve does not increase that sharply, it does not peak, yet reaches a plateau sustained over time until the source is removed.

===Propagated outbreaks===
Outbreaks of communicable infectious disease (i.e. can be transmitted from person to person) for which there is no single, common source. The causative agent is propagated within the population through human contact patterns. The shape of the epidemic curve in propagated outbreaks can vary and depends on the contact pattern and the proportion of susceptible individuals.

[[Category:Field Epidemiology]]

Source of infection

2025-05-19T15:08:37Z

Bosmana fem:

We refer to the source of [[Basic concepts: Infection and Contamination|infection]] as the origin from which a [[Host tracing|host]] acquires the infection, either endogenous (i.e. originating from a person's own commensal microbial flora) or exogenous (i.e. an individual, animal or object in the external environment of the host). Usually, the source can be identified as an individual, animal or object in a specific place and at a specific time.

Thus, a person can be a source of infection, either for him/herself (endogenous) or to other people (directly through personal contact or indirectly, e.g. by contaminating food or beverages).

In addition to people, also animals can be sources of infection.

Objects may be sources of infection; food, water, air-conditioning systems, showers, medical instruments, recreational waters, door knobs, cotton handkerchiefs etc. Such contaminated objects are also referred to as fomites. Most man-made products that may be sources of infection must be produced while limiting the risk of contamination.

In most outbreak investigations, the principal objective is to identify the source of the infection. Interestingly enough, this sometimes leads to semantic problems: an identified 'source' (e.g. a chocolate cake) is usually contaminated by some other source (e.g. the baker of the cake or the eggs used). Tracing back such a 'chain of transmission' usually leads back to the reservoir. In some articles, the concept of 'source' and 'reservoir' are used as synonyms, though strictly speaking, they are not.

Inanimate sources of infection are sometimes referred to as 'vehicles of infection' (e.g. the chocolate cake) or 'fomites' (e.g. the cotton handkerchief). Inanimate sources (vehicles, fomites) are part of the indirect transmission route.

The source of infection should be distinguished from the source of contamination (e.g. overflow of a septic tank, contaminating a water supply).

=References:=
* David L. Heymann (editor). Control of Communicable Diseases Manual. APHA, 2008

[[Category:Public Health Interventions]]

Primary prevention

2025-05-17T17:36:18Z

Bosmana fem:

Within the framework of [[Field Epidemiology|field epidemiology]], primary prevention plays a vital role in averting the onset of communicable diseases and reducing their overall impact on public health.<ref>Centers for Disease Control and Prevention. (2012). Principles of Epidemiology in Public Health Practice, 3rd ed. Lesson 3: Measures of Risk. https://www.cdc.gov/csels/dsepd/ss1978/lesson3/section2.html</ref><ref>This text was written by ChatGPT4.0 on 26 March 2023 and reviewed by Arnold Bosman.</ref>

Key strategies in primary prevention include immunisation programs, which protect populations from infectious agents such as measles, polio, and influenza through vaccination.<ref>World Health Organisation. (2023). Immunisation coverage. https://www.who.int/news-room/fact-sheets/detail/immunization-coverage</ref>
Health education and promotion campaigns—such as handwashing initiatives and safe food handling practices—encourage behaviours that reduce the risk of disease transmission.

Vector control measures, including the use of insecticide-treated bed nets and environmental source reduction, help limit the spread of vector-borne diseases like malaria and dengue fever. Environmental interventions, such as improving access to clean water and sanitation, also play a critical role by reducing exposure to disease-causing pathogens.

Through these proactive efforts, field epidemiologists contribute to building resilient communities and establishing a strong foundation for communicable disease prevention.

== References ==
<references />

[[Category:Prevention]]

Secondary prevention

2025-05-17T17:30:24Z

Bosmana fem:

Secondary prevention<ref>This text was written by ChatGPT4.0 on 26 March 2023 and reviewed by Arnold Bosman</ref> focuses on the early detection and timely intervention of communicable diseases, aiming to halt or slow disease progression in its initial stages. In the context of [[Field Epidemiology|field epidemiology]], secondary prevention includes a range of strategies designed to identify infections before they become symptomatic or widely transmitted.

Screening programs for diseases such as tuberculosis or HIV are critical for identifying asymptomatic or pre-symptomatic individuals. Early diagnosis allows for prompt initiation of treatment, reducing the risk of complications and limiting further transmission within the community.

Another essential method is [[Contact tracing]], which involves identifying and monitoring individuals who have been exposed to a contagious disease—such as COVID-19—with the goal of ensuring timely testing, isolation, and treatment if necessary.

Field epidemiologists also contribute through [[Outbreak Investigations|outbreak investigations]] and ongoing surveillance. These systems help detect unusual patterns, emerging clusters, or sharp increases in incidence, allowing public health authorities to launch rapid containment and mitigation efforts.

In addition, targeted prophylactic interventions, such as post-exposure prophylaxis (PEP) for HIV or chemoprophylaxis for malaria, can prevent disease development in individuals who have been recently exposed to a pathogen.

Through these strategies, secondary prevention plays a vital role in curbing the spread of communicable diseases and minimizing their impact on individual and public health.

== References ==
<references />

[[Category:Prevention]]

Secondary prevention

2025-05-17T17:29:20Z

Bosmana fem:

Secondary prevention focuses on the early detection and timely intervention of communicable diseases, aiming to halt or slow disease progression in its initial stages. In the context of [[Field Epidemiology|field epidemiology]], secondary prevention includes a range of strategies designed to identify infections before they become symptomatic or widely transmitted.

Screening programs for diseases such as tuberculosis or HIV are critical for identifying asymptomatic or pre-symptomatic individuals. Early diagnosis allows for prompt initiation of treatment, reducing the risk of complications and limiting further transmission within the community.

Another essential method is [[Contact tracing]], which involves identifying and monitoring individuals who have been exposed to a contagious disease—such as COVID-19—with the goal of ensuring timely testing, isolation, and treatment if necessary.

Field epidemiologists also contribute through [[Outbreak Investigations|outbreak investigations]] and ongoing surveillance. These systems help detect unusual patterns, emerging clusters, or sharp increases in incidence, allowing public health authorities to launch rapid containment and mitigation efforts.

In addition, targeted prophylactic interventions, such as post-exposure prophylaxis (PEP) for HIV or chemoprophylaxis for malaria, can prevent disease development in individuals who have been recently exposed to a pathogen.

Through these strategies, secondary prevention plays a vital role in curbing the spread of communicable diseases and minimizing their impact on individual and public health.

== References ==
<references />

[[Category:Prevention]]

Tertiary prevention

2025-05-17T17:27:37Z

Bosmana fem:

Tertiary prevention aims to reduce the long-term impact of communicable diseases by alleviating complications, preventing disability, and improving quality of life for those already affected. In the context of field epidemiology, tertiary prevention goes beyond clinical care and includes strategic interventions that promote recovery, functionality, and psychosocial well-being. These efforts are often disease-specific and require collaboration across public health, clinical medicine, and social services.

For instance, in HIV/AIDS management, multidisciplinary rehabilitation programs play a vital role in addressing physical deconditioning, social stigma, mental health concerns, and vocational reintegration. These programs help individuals cope with the chronic nature of the disease and support their participation in society.<ref>Rebeiro, Peter F. (2021). "The Impact of HIV/AIDS on Quality of Life: A Global Perspective". ''Journal of the International AIDS Society''. https://doi.org/10.1002/jia2.25772</ref>

In tuberculosis (TB) control, adherence support strategies are central to tertiary prevention. The Directly Observed Treatment Short-course (DOTS) strategy, endorsed by the WHO, ensures completion of therapy, thus reducing the risk of relapse and drug resistance.<ref>World Health Organization. "The End TB Strategy". https://www.who.int/tb/strategy/en/</ref> Long-term support may also include rehabilitation from lung damage and re-integration into the workforce after extended treatment.

For viral hepatitis, particularly chronic hepatitis B and C, tertiary prevention encompasses patient education, regular monitoring of liver function, and antiviral therapies to prevent progression to cirrhosis or hepatocellular carcinoma. Psychosocial support is also essential to address stigma and facilitate lifestyle adjustments that reduce hepatic stress.<ref>EASL (2020). "EASL Clinical Practice Guidelines: Management of hepatitis C virus infection". ''Journal of Hepatology'', 73:1174–1211. https://doi.org/10.1016/j.jhep.2020.05.041</ref>

In the context of emerging infectious diseases such as COVID-19 or Ebola virus disease, tertiary prevention includes structured long-term follow-up to manage post-acute sequelae (e.g., “long COVID” or post-EVD syndrome), inform rehabilitation strategies, and guide future clinical preparedness.<ref>Carfi, Angelo et al. (2020). "Persistent Symptoms in Patients After Acute COVID-19". ''JAMA'', 324(6):603–605. https://doi.org/10.1001/jama.2020.12603</ref>

Field epidemiologists contribute to tertiary prevention by:
* Designing surveillance systems that capture long-term outcomes of infectious diseases.
* Coordinating with clinical and rehabilitation services to ensure comprehensive care.
* Conducting operational research to identify best practices for chronic disease management after infection.

These activities help minimize the burden of disease on individuals and society, strengthen health systems’ resilience, and close the loop between acute response and sustained recovery.

== References ==
<references />

[[Category:Prevention]]

Tertiary prevention

2025-05-17T17:19:20Z

Bosmana fem:

Tertiary prevention aims to reduce the long-term impact of communicable diseases by alleviating complications, preventing disability, and improving quality of life for those already affected. In the context of field epidemiology, tertiary prevention goes beyond clinical care and includes strategic interventions that promote recovery, functionality, and psychosocial well-being. These efforts are often disease-specific and require collaboration across public health, clinical medicine, and social services.

For instance, in HIV/AIDS management, multidisciplinary rehabilitation programs play a vital role in addressing physical deconditioning, social stigma, mental health concerns, and vocational reintegration. These programs help individuals cope with the chronic nature of the disease and support their participation in society {{Cite journal |last=Rebeiro |first=Peter F. |title=The Impact of HIV/AIDS on Quality of Life: A Global Perspective |journal=Journal of the International AIDS Society |year=2021 |doi=10.1002/jia2.25772}}.

In tuberculosis (TB) control, adherence support strategies are central to tertiary prevention. The Directly Observed Treatment Short-course (DOTS) strategy, endorsed by the WHO, ensures completion of therapy, thus reducing the risk of relapse and drug resistance {{Cite web |url=https://www.who.int/tb/strategy/en/ |title=The End TB Strategy |publisher=World Health Organization}}. Long-term support may also include rehabilitation from lung damage and re-integration into the workforce after extended treatment.

For viral hepatitis, particularly chronic hepatitis B and C, tertiary prevention encompasses patient education, regular monitoring of liver function, and antiviral therapies to prevent progression to cirrhosis or hepatocellular carcinoma. Psychosocial support is also essential to address stigma and facilitate lifestyle adjustments that reduce hepatic stress {{Cite journal |last=EASL |title=EASL Clinical Practice Guidelines: Management of hepatitis C virus infection |journal=Journal of Hepatology |year=2020 |volume=73 |pages=1174–1211 |doi=10.1016/j.jhep.2020.05.041}}.

In the context of emerging infectious diseases such as COVID-19 or Ebola virus disease, tertiary prevention includes structured long-term follow-up to manage post-acute sequelae (e.g., “long COVID” or post-EVD syndrome), inform rehabilitation strategies, and guide future clinical preparedness {{Cite journal |last=Carfi |first=Angelo |title=Persistent Symptoms in Patients After Acute COVID-19 |journal=JAMA |year=2020 |volume=324 |issue=6 |pages=603–605 |doi=10.1001/jama.2020.12603}}.

Field epidemiologists contribute to tertiary prevention by:

Designing surveillance systems that capture long-term outcomes of infectious diseases.

Coordinating with clinical and rehabilitation services to ensure comprehensive care.

Conducting operational research to identify best practices for chronic disease management after infection.

These activities help minimize the burden of disease on individuals and society, strengthen health systems’ resilience, and close the loop between acute response and sustained recovery.

== References ==
<references />

[[Category:Prevention]]

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Tertiary prevention

2025-05-17T16:29:15Z

Bosmana fem: /* Tertiary Prevention */

Tertiary prevention aims to reduce the long-term impact of communicable diseases by alleviating complications, preventing disability, and improving quality of life for those already affected. In the context of field epidemiology, tertiary prevention goes beyond clinical care and includes strategic interventions that promote recovery, functionality, and psychosocial well-being. These efforts are often disease-specific and require collaboration across public health, clinical medicine, and social services.

For instance, in HIV/AIDS management, multidisciplinary rehabilitation programs play a vital role in addressing physical deconditioning, social stigma, mental health concerns, and vocational reintegration. These programs help individuals cope with the chronic nature of the disease and support their participation in society {{Cite journal |last=Rebeiro |first=Peter F. |title=The Impact of HIV/AIDS on Quality of Life: A Global Perspective |journal=Journal of the International AIDS Society |year=2021 |doi=10.1002/jia2.25772}}.

In tuberculosis (TB) control, adherence support strategies are central to tertiary prevention. The Directly Observed Treatment Short-course (DOTS) strategy, endorsed by the WHO, ensures completion of therapy, thus reducing the risk of relapse and drug resistance {{Cite web |url=https://www.who.int/tb/strategy/en/ |title=The End TB Strategy |publisher=World Health Organization}}. Long-term support may also include rehabilitation from lung damage and re-integration into the workforce after extended treatment.

For viral hepatitis, particularly chronic hepatitis B and C, tertiary prevention encompasses patient education, regular monitoring of liver function, and antiviral therapies to prevent progression to cirrhosis or hepatocellular carcinoma. Psychosocial support is also essential to address stigma and facilitate lifestyle adjustments that reduce hepatic stress {{Cite journal |last=EASL |title=EASL Clinical Practice Guidelines: Management of hepatitis C virus infection |journal=Journal of Hepatology |year=2020 |volume=73 |pages=1174–1211 |doi=10.1016/j.jhep.2020.05.041}}.

In the context of emerging infectious diseases such as COVID-19 or Ebola virus disease, tertiary prevention includes structured long-term follow-up to manage post-acute sequelae (e.g., “long COVID” or post-EVD syndrome), inform rehabilitation strategies, and guide future clinical preparedness {{Cite journal |last=Carfi |first=Angelo |title=Persistent Symptoms in Patients After Acute COVID-19 |journal=JAMA |year=2020 |volume=324 |issue=6 |pages=603–605 |doi=10.1001/jama.2020.12603}}.

Field epidemiologists contribute to tertiary prevention by:

Designing surveillance systems that capture long-term outcomes of infectious diseases.

Coordinating with clinical and rehabilitation services to ensure comprehensive care.

Conducting operational research to identify best practices for chronic disease management after infection.

These activities help minimize the burden of disease on individuals and society, strengthen health systems’ resilience, and close the loop between acute response and sustained recovery.

[[Category:Prevention]]

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Template:Cite journal

2025-05-17T16:07:46Z

Bosmana fem: 1 revision imported

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{{documentation}}
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Template:Cite news

2025-05-17T16:07:46Z

Bosmana fem: 1 revision imported

<includeonly>{{#invoke:citation/CS1|citation
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{{documentation}}
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