Epidemiology Quiz

Lectures 1-5

Why do we need epidemiology?

• Identify trends in the public’s health (increase in colorectal cancer)

• Provide advance warning of possible threats to public health (event based surveillance, sentinel surveillance, social media monitoring, wastewater testing)

• Keep people healthy, prevent illness (screening, prevention, awareness, vaccination)

• Maximize societal benefit, minimize burden (focus on prevention over treatment, cost effective prevention programs)

• Reduce health disparities (build trust locally, widely promote all programs, tailoring programs and policies based on local data)

Early Epidemiologists - Hippocrates

• Rejected the idea that illness was religious or supernatural, breaking with ancestral tradition

• Recognized association of disease with place

• Believed disease was a result of imbalance of the body’s “humors”

• Creator of the Hippocratic Oath, modern version still used today

Early Epidemiologists - John Graunt

• English statistician

• “Founder of epidemiology”

• Examined number and causes of death to identify variations in death according to sex, residence, season, and age

Miasma

• Diseases caused by environmental factors such as contaminated water, foul air, and poor hygienic conditions

John Snow

• Founder of epidemiology

• Large cholera outbreak of London

• At the time, water was distributed by two main suppliers

• Traced the outbreak to one of the suppliers

Germ Theory - Louis Pasteur

• Discovered that microorganisms could cause disease

• Also studied fermentation and discovered pasteurization

Germ Theory - Robert Koch

• Established 4 postulates for providing a microorganism causes disease

  • Be present in all cases of disease

  • Be isolated from disease patient

  • Cause disease when introduced to healthy host

  • Be isolated again from new host

Defining a Population

• Person, place, and time

  • Who are you interested in?

  • Where are they?

  • During what timeframe?

• Be specific

Person - Characteristics to consider

• Age

• Sex/ Gender

• Marital Status

• Race/ ethnicity

• Nativity and migration (where are ppl from)

• Religion

• SES

Age and Health Outcomes

• Human biological clock

• Waning of immune system —> increased susceptibility

• Life cycle and behavioral phenomena

• Personal behavior and risk-taking influence disease/mortality (ex: homicide among youth)

• Delay b/w exposure and subsequent disease (ex: exposure to carcinogen is usually a lot earlier than cancer diagnosis)

• Older individuals have had greater opportunity for adverse exposures (ex: higher rate of adult cancer than pediatric)

Sex/Gender and Health Outcomes

• All-cause and age-specific mortality rates higher for men than women

• Male

  • Hearing imparement, CVD, smoking-related illnesses, chronic illness severity

• Female

  • Pain, asthma/lung difficulties, depression

• US Life Expectancy

  • Males: 74.8, Females: 80.2

Race/Ethnicity and Health Outcomes

• Differences in mortality by race/ethnicity

  • Black Americans = 72.8 years

  • White Americans = 77.5 years

  • Asian Americans = 84.5 years (longest lifespan)

  • AIAN American = 67.9 years (shortest lifespan)

• Differences are likely multifactorial

  • Stress, health behaviors, systemic racism

Place

• Disease, health behaviors, mortality show great variation globally

  • Climate, cultural factors, dietary habits (Mediterranean diet is very good), healthcare access, etc

• Also, within-country variation (by state, region, county)

  • NYC vs upstate

• Urban vs rural differences due to crowding, pollution, poverty, healthcare access, violence, food access (food deserts)

• Always be specific

Time

• Periodic changes in frequency of diseases and health conditions over time (months, years)

  • Birth rates: highest August and September

  • Heart disease mortality: winter

  • Influenza: December - February

  • Homicide: summer

• Related to changes in lifestyle, seasonal climatic changes (have more defined illness seasons in places with seasons), virulence of infectious agent (less exercise in winter so immune system weakens), cultural shift (more public health awareness nowadays)

Stationary vs Dynamic Populations

• Stationary/fixed

  • Membership is based on an event and is permanent

  • Can’t become part of that community

  • Ex: 9/11 survivors

• Dynamic/ open

  • Membership is based on a condition and is transitory

    • Ex: NYC residents, NYU community

  • Steady state: # of people leaving population = # of people entering population

Exposures and Outcomes

• An exposure is the hypothesized cause of an outcome

  • Virus, bacteria, genetics, smoking, sun exposure, diet, income, education

• An outcome is your health event of interest

  • Death, covid, lung cancer, overdose, car accidents, depression

• Can’t be the same thing

• Define both before beginning study

• Be specific aka operationalizing your exposure and outcome

• Consider subjective vs objective measures

• Ex: Exposure = flu virus, Outcome = flu illness

Outcome vs Results

• Outcome

  • Defined before a study starts

  • The thing you are interested in studying

• Result

  • The finding of your study

  • Cannot define before study starts bc it’s the purpose of the study

• Ex: I want to see if lack of sleep caused depression.

  • Outcome: Depression

  • Result: I found that lack of sleep increases the risk of depression

Subjective vs Objective

• Subjective

  • Self report (surveys, questionnaires)

  • No defined scale, could be lying

• Objective

  • Medical charts - weight, bp, height

  • Blood tests

  • MRIs

  • Validates scales

Measurement Types

• Binary

  • 2 options

• Continuous

  • Any numerical value within a range

  • Age, height, income, GPA

• Categorical

  • No order

  • Race, marital status, job category, eye color

• Ordinal

  • Categories with an order

  • Low, medium, high

Three Key Factors of Epidemiology - Epidemiology Triad Model 

Host, Agent, Environment 

Hosts 

• Can increase or decrease the chance for disease or severity (the organism, human, carrying the disease) 

• Factors 

  • Personal traits (more social vs isolating) 

  • Behaviors 

  • Genetics (predisposition, sickle cell) 

  • Immunologic factors (high allergies in US) 

Agents

• Necessary for disease to occur (the cause of disease itself) 

• Factors: 

  • Biological (fungus) 

  • Physical (allow to replicate/mutate fast) 

  • Chemical (how are they distributed - ex: aerosol (covid) 

Environment

• Contributes to the disease process

• Factors: 

  • External conditions (snowing outside —> more ppl indoors in the gym, grocery stores, malls) 

  • Physical, biologic, or social factors

Epi Triad and Disease 

• Triad should be in homeostasis for patterns of disease to be stable 

  • No longer balances = disease occurs 

• Ex: no more vaccination requirement in Florida —> host is disrupted

Spectrum of Disease 

• Susceptibility 

• Subclinical disease 

  • Individual is infected but clinical symptoms are absent 

  • Might be able to detect early illness through screening 

• Clinical disease 

  • Symptoms present 

  • Time of diagnosis

• Recovery, disability, or death 

Incubation Period

• Stage of subclinical disease 

• Time from exposure to onset of symptoms 

Disease Transmission 

• Direct (person to person) 

  • Droplets (covid, flu)

• Indirect

  • Waterborne (cholera)

  • Foodborne (salmonella, norovirus)

  • Vectorborne (malaria) 

    • Animals are intermediaries 

  • Vehicle borne (via fomite) 

    • Inanimate objects 

    • Door handles 

Chain of Infection

• Agent - disease producing factor

• Source/Reservoir - environment/habitat where a pathogen can live and multiply

Portal of Exit - pathogen leaves reservoir

• Mode of Transmission - how agent moves from reservoir to host

Portal of Entry - opening where pathogen may enter

• Host - person at risk

Ex of Chain of Infection 

Agent - bubonic plague 

Source - rats 

Exit portal - flea bite 

Mode of Transmission - Vector (flea) 

Portal of Entry - flea bite on skin 

Host - person bitten 

Stopping an Outbreak/Disease Pattern

• Must break the chain of transission

• Upstream —> agent 

  • ex: get rid of mosquitos 

• Downstream —> host 

  • ex: vaccines 

Defining Disease Patterns

• Endemic 

  • Illness consistently present in an area 

  • ex: lyme disease upstate 

• Epidemic (larger area) 

  • Cases are higher than expected and derived from a common exposure 

  • Opioid epidemic 

  • Influenza 

• Outbreak (smaller area) 

  • Cases are higher than expected and derived from a common exposure 

  • Norovirus outbreak at a wedding 

• Pandemic 

  • Worldwide epidemic on at least 3 continents 

  • Covid-19 2020

• Exception: for some severe illnesses (plague, anthrax, cholera in the US) or previously eradicated illnesses (smallpox) just one case can be an epidemic 

Herd/Population Immunity 

• Indirect protection from an infectious disease that happens when a population is immune either through vaccination or immunity developed through previous infection

Morbidity

• Measuring states of disease

2 major types - prevalence and incidence

Counts + limitation

• How many cases/deaths do you have? 

• Simplest and most commonly performed quantitative measure in epi 

• Limited bc no timeframe, needs context 

Prevalence

• Total cases of a disease in a population over a specified time period 

• Expressed as a percentage or number of cases per unit size of population 

• Indicates the burden of disease 

• Once someone is an existing case, they remain one unless they recover or die 

Prevalence = (# total cases) / (total population over a time period) 

Types of Prevalence

• Point Prevalence 

  • At one/short point in time 

  • ex: prevalence on Sept 1, 2024 

• Period Prevalence 

  • Over period of time 

  • ex: prevalence from 2020-2024 

• Cumulative Lifetime Prevalence 

  • Over a lifetime 

  • ex: prevalence of people ever diagnosed with disease X 

Incidence

• Number of new cases of a disease in a population at risk over a specified time period

• Expressed as a percentage or number of cases per unit size of population at risk 

• Indicates the risk of disease

• In other words, probability someone who is at risk of disease will get it

Incidence = (# new cases) / *(total pop at risk over a time period) 

What does “at risk” mean? 

• Someone who is susceptible to the disease

  • This will differ depending on specific disease you are interested in

• Reasons someone may not be at risk

  • Immune

    • Vaccinated, type of disease where you cannot be reinfected after having it (chickenpox)

    • Note that if you can get reinfected then would still be at risk even if you already had it (covid, common cold)

• Already have disease

  • If you have HIV, no longer at risk of developing it

• Cannot develop disease

  • Can only develop uterine cancer if you have a uterus 

Prevalence vs. Incidence

Prevalence

• Purpose

  • Measure burden

• Numerator

  • Old AND new cases

• Denominator

  • Total population

Incidence

• Purpose

  • Measure risk

• Numerator

  • New cases ONLY

• Denominator

  • Total population at risk

Types of Incidence

• Incidence / Cumulative Incidence *

  • Assumed everyone at risk in population is at risk the same amount of time

  • Likely not a true assumption – people are diagnosed at different times,  people come in/out of populations

• Incidence rate

  • Accounts for time at risk

  • People who are diagnosed early get less time “at risk” than those diagnosed late or not at all

  • People who come into population late or leave early get less time “at risk” than someone in population the whole time

Epidemiologists Bathtub

Mortality

• Mortality

  • Measuring death from diseases

• Many different types

  • Crude mortality

  • Cause-specific mortality

  • Case-fatality

  • Infant mortality

Crude Mortality

• Number of total deaths in a population over a specified period of time

• Expressed as number of deaths per unit size  of population

Crude Mortality = (# total deaths) / total population over a time period 

Crude Mortality Limitations

• It does not account for systematic factors that could impact mortality like AGE

• Imagine…

  • Population A has a crude mortality of 90 deaths per 100 people

  • Population B has a crude mortality of 5 deaths per 100 people

  • At first glace Population A seems like a big issue, but what if everyone in that population happen to be 100 years or older?

Adjusted Mortality

• Therefore, crude mortality is not a good measure when comparing populations

  • Solution? Adjustments!

• Summary measures that use a statistic procedure to remove the effect of different population compositions

  • Age-adjusted mortality

  • Sex-adjusted mortality

• Allows us to compare across populations

Other Common Mortality Measures

• Cause-specific mortality

  • Number of deaths in total population caused by specific disease/health outcome

• Infant mortality

  • Number of deaths among infants in population

• Case fatality

  • Number of deaths among those with specific disease

Surveillance

• “The continued watchfulness over the distribution and trends of incidence [of a disease] through the systematic collection, consolidation, and evaluation of morbidity and mortality reports and other relevant data.” Alexander Langmuir 

Why Collect Surveillance Data?

• Assess population health status

• Define objectives, priorities, and strategies

• Target interventions

• Evaluate interventions, effectiveness of control efforts

• Generate research hypotheses

• Monitor temporal trends

• Detect outbreaks

Surveillance Informs Control and Prevention

Some Questions Surveillance Data Can Answer

• What is the infant mortality rate?

• Has there been an increase in unsafe sex among MSMs?

• Are smoking rates declining in teens?

• What factors are associated with West Nile Virus?

• Is flu season here? Is it more severe than usual?

• What neighborhood has the highest rates of overdose?

Surveillance Examples

National HIV Behavioral Surveillance (NHBS)

• In 2000, CDC and external experts designed a national plan for HIV prevention

• One of the 4 goals

  • “Strengthen the national capacity to monitor the HIV epidemic to better direct and evaluate prevention efforts.”

• In 2002, state & local government funded to develop NHBS

• Findings used to enhance understanding of HIV risk and testing in high-risk groups and to develop and evaluate HIV prevention programs serving them

COVID-19 Surveillance

• CDC worked with state and local departments of health, academic, and commercial partners

• Goal was to understand the extent of SARS-CoV-2 infection and track trends

• Used serology testing and surveys to monitor past infection (antibody)

• Geographic surveys, community level surveys, and smaller-scale surveys (of specific populations)

Steps in Conducting Surveillance

1) Identify, define, and measure health problem of interest and objective of surveillance program

2) Collect and compile data about the problem

3) Analyze and interpret these data

4) Share data with those responsible for control

• Take steps to control problem, as needed

5) Monitor and evaluate usefulness of surveillance program

Criteria for Selecting Health Problems for Surveillance

• Public health importance

  • Incidence, prevalence, severity, consequences/mortality, socioeconomic impact, communicability, public perception, international requirements

• Ability to prevent, control, treat the problem

• Capacity of health system to implement control measures

  • Speed of response, economics, availability of resources (including availability of data), ability to ensure people get treatment they need

Data Sources

• Various data sources

  • Individuals, environment, healthcare providers / institutions

• Collected continuously, periodically, or defined period

• Data can be:

  • Primary – environmental monitoring, notifications, registries (including vital statistics)

  • Secondary – medical records, school records, other administrative data, surveys

Common Attributes of Surveillance Systems

•Simple

•Stable

•Acceptable (to data providers)

•Standardized, uniform, high-quality data

•Timely (in reporting events)

•Representative (of all areas)

•Sensitive (to outbreaks and changes in trends)

•Flexible (to changing surveillance needs)

Data Sources: Examples

•Reportable disease notifications

•Statistics from vital registration system (births, deaths)

•Surveys of general population

•Disease registries

•Insurance data

•Clinical data sources

•School health programs

•Census data

•Economic data

•Reports from health organizations (e.g., CDC, WHO)

Active vs. Passive Surveillance

• Active

  • Health department contacts healthcare providers or laboratories about conditions to identify cases

  • Advantage – Useful when need to identify all cases

  • Disadvantage – Requires more resources (expensive)

• Passive

  • Health departments rely on healthcare provider or laboratories to report cases of disease

  • Advantage – Efficient, requires limited resources

  • Disadvantage – Incomplete due to underreporting

Reportable Disease Notifications

• Reporting of certain diseases/health conditions as specified by law, regulation or agreement

• Typically made to local or state health agencies

• Types

  • Communicable diseases (COVID-19, HIV, HAV, HBV, HCV, Influenza)

  • Chemical and physical hazards (e.g., lead poisoning, firearm injury)

  • Adverse drug events

  • Elevated blood levels of lead

National Electronic Disease Surveillance System (NEDSS)*

• Electronic public health surveillance system that regularly provides data to the CDC on notifiable diseases

  • Each state has an electronic system where this is information is aggregated

  • Reporting is regulated by states and it technically voluntary

    • All 50 states and the District of Columbia participate

• Prior to COVID-19, not all states met the technological requirements to report using this system

Reportable Disease Notifications: Limitations

• Possible incompleteness of population coverage

  • I.e. - Asymptomatic persons would not seek treatment

•Failure of physician to fill out required forms (burden on provider, incomplete forms)

•Unwillingness to report cases that carry social stigma

•Can take time (especially if passive surveillance)

Registries

•Centralized database for documenting or tracking health events

•Certain registries are required by law

•Reported by health care providers, health care facilities, morticians/funeral directors, patients

• Examples

  • Vital statistics (births, deaths)

  • Immunizations

  • Disease specific

  • Exposure specific (9/11)

  • age, place of death, residence, sex, job, marital status, cause of death, etc

Registry—SEER

• The SEER (Surveillance, Epidemiology, and End Results) program collects and distributes cancer incidence and survival data from several different cancer registries

  • Runs within NIH through the National Cancer Institute (NCI)

Mortality Data Limitations

• Cause of death is unclear

  • Example: Someone with various chronic conditions

•Lack of standardization of diagnostic criteria

•Errors in coding

•Changes in coding (ICD codes can change over time)

•Stigma associated with certain diseases, e.g., AIDS, suicide, may lead to inaccurate reporting

Surveys

• Systematic, structured method of gathering information to quantitatively describe population

  • Census (entire population) vs. sample of population

•Typically conducted at one point in time, but some are done repeatedly

•Many are population-based

• Uses

  • Understand burden of health problem (prevalence!)

  • Conduct surveillance and examine trends

  • Estimate and target needed resources

Survey—NHANES

• program of studies designed to assess the health and nutritional status of adults and children in the United States

• Each year, it examines a nationally representative sample of ~5,000 persons.

  • Participants are in counties across the country, 15 are visited each year.

• The visit includes:

  • An interview: demographic, socioeconomic, dietary, and health-related questions.

  • An examination: medical, dental, and physiological measurements, as well as laboratory tests administered by highly trained medical personnel

Surveys: Limitations

•Time delay between data collection vs. publication

•People may not complete the surveys (non-response)

•Relies on self-reported data which may be inaccurate

•Large population-base surveys are expensive and resource intensive

•Can be difficult to reach smaller sub-populations

•Some populations excluded (people experiencing homelessness, those in institutional settings like jail/prison)

Syndromic Surveillance

• Method that uses health-related data BEFORE a diagnosis

• Thresholds are set and higher than expected thresholds “signal” there is a sufficient probability that a public health response should be considered

• Operationalized to target potential cases

• Today, it is mostly considered as bioterrorism surveillance

• Identify clusters of illness (symptoms) early, prior to a confirmed diagnosis and before it is officially reported to a public health agency

  • This allows for earlier mobilization of response to reduce morbidity and mortality

• Uses real health data, in real time, to provide immediate data for analysis and feedback to potential outbreak investigators

Syndromic Surveillance & Bioterrorism

• Bioterrorism events often initially present with non-specific clinic manifestations

  • Pathogens that may have been altered may result in atypical clinical symptoms

  • For rare diseases, some physicians may never have seen a case

    • Think smallpox, diphtheria, even measles

• It is possible that careful monitoring of specific symptoms can provide public health with early evidence of attacks

  • Spikes in ED complaints of nausea, neurological symptoms, respiratory issues, etc.

Examples of Syndromic Surveillance Sources

• Pre-diagnostic/chief complaint

  • Most common, typically comes from EDs

• Over-the-counter transactions

  • Drug stores

  • Grocery store purchases with OTC medicine purchases

•911 calls

•Ambulance dispatch data

•Absenteeism data

•ED discharge summaries

•Prescriptions (pharmacies)

Example: Syndromic Surveillance

What do we do with data after collecting it

• Analyze by person, time, and place

• Interpret data

  • Why might prevalence or incidence have increased or decreased over time

• Disseminate data and interpretations to healthcare providers, public health professionals, community

  • MMWRs, surveillance reports (NYCDOHMH)

• Evaluate and improve surveillance systems

  • Useful? What resources are needed?

HIV Outbreak in Scott County, IN

• Passive surveillance of HIV to Active helps identify more cases 

• Notified CDC in 2015, 11 years after first diagnosis

• Key Findings

  • •Cases primarily among people who injected drugs

    •Syringe exchange programs not permitted by law

    •Limited HIV awareness

    •No outpatient HIV care in the community

    •Insufficient substance use treatment

    •Few individuals were employed or had insurance

• Using the data

What is an outbreak investigation?

•Methodology to investigate an unusual number of related illnesses or an unusual geographic clustering of illnesses

• Typically outbreak investigations are for infectious diseases

• Investigations into chronic disease occurrence, such as geographically related cancers are typically called cluster studies

• Notable outbreak investigations

  • John Snow—Broad Street Pump

  • Ignaz Semmelweis—Childbed Fever

Ignaz Semmelweis - Childbed Fever

Found that doctors and med students were not washing their hands compared to midwives so their unit had higher mortality rate of childbed fever. Implemented a rule about handwashing and rates decreased. But faced backlash so his findings weren’t published for a long time.

Why’s it important to investigate outbreaks? 

Who is involved in outbreak investigations?

Local

• Local Health dept 

• Local businesses 

• Hospitals 

• Outpatient Providers 

State 

• State Health dept 

• state water agency 

• state food agency 

Federal 

• CDC 

• FDA 

• EPA 

What are the steps to investigate an outbreak?

1. Prepare for Field Work

• Listed first, but often occurs concurrently with or right after the “second” step (establish existence of an outbreak) 

• Scientific and Investigative Issues

  • Lab supplies

  • Lab capability

  • PPE

• Management Issues

  • Coordination

  • Within agency

  • With external agencies

  • Logistics

  • Responsibilities of agencies

2. Establish Existence of an Outbreak

• Considerations for non-outbreak/epidemic situations:

  • Severity of the illness

  • Potential for spread

  • Availability of control measures

  • Political considerations

  • Public relations

  • Available resources

3. Verify the Diagnosis

• Review Clinical Findings and Lab Results

• Summarize Clinical Picture with Frequency Tables

4. Construct a Case Definition

• A case definition is a standard set of criteria for deciding whether an individual should be classified as having the health condition of interest

• Includes clinical criteria and restrictions by time, place, and person.

  • Clinical criteria should be based on simple and objective measures such as:

    • Fever ≥ 40°C (101°F)

    • Three or more loose bowel movements per day

    • Myalgia (muscle pain) severe enough to limit the patient’s usual activities

• Common mistake: do not limit the case definition to the exposure/risk factor you are investigating!!

Broad vs Narrow Case Definition 

• Broad Case Definitions

  • Fever ≥ 40°C (101°F), cough

  • Likely to result in a high number of “cases”

  • Identification of patrons at an event where foodborne illness is suspected (helps increase number of samples)

  • Highly contagious or highly deadly illnesses where strict quarantine is needed to prevent spread

  • Higher likelihood of catching non-cases, less likely to miss cases

• Narrow Case Definitions

  • Fever ≥ 40°C (101°F), cough, lampshade rash

  • Likely to result in lower number of “cases”

  • Diseases where the symptoms are well documented

  • Situations where “false positives” are of concern

  • Closing restaurants, removing a product from the market

  • Higher likelihood of missing cases, less likely of catching non-cases

• Which is better?

  • Depends on what you are investigating

5. Find Cases and Record Information

•Active Surveillance

•Passive Surveillance

• Information usually collected using a line list

  • identifying info 

  • demographics 

  • clinical info (symptoms, dates) 

  • risk factor info 

  • reporter info 

6. Descriptive Epidemiology

• Describe the key characteristics of ill individuals

• Helps infer the population at risk

• Person, place, time 

7. Develop Hypotheses

• This step generally starts with the initial notification that there may be an outbreak

• What do you know?

  • What is the normal reservoir?

  • How is it transmitted?

  • What are the common vehicles?

  • What are the risk factors?

• Make sure to consider bioterrorism if warranted

Epidemiologic clues to bioterrorism

•One case of an uncommon agent (smallpox)

•Atypical strain of an agent

•Higher morbidity or mortality; ineffective usual therapy

•Unusual seasonal or geographic distribution

•Spike in a typically stable endemic disease (plague)

•Atypical transmission

•Exposure limited to one ventilation source (building)

•Many similarly ill persons seeking treatment in the same area at the same time

8: Evaluate Hypotheses Epidemiologically

• Retrospective Cohort Studies

  • Attack rates

  • Relative Risk

  • Attributable Risk

  • Chi-squared Test (Statistical Significance)

  • Confidence Intervals

• Case-Control Studies

  • Odds ratios

  • Most important consideration is picking controls

Example: Attendees at a wedding who developed gastroenteritis would be cases and attendees who attended but did not get ill would be the ideal control

9. Reconsider, Refine, Re-evaluate Hypotheses

•Unable to develop hypotheses with descriptive epi

•Evaluation of hypotheses did not yield any clues (step 8)

•Hypotheses may need to be narrowed based on findings

10. Compare and Reconcile with Samples

• Patient Samples

  • Norovirus

  • Salmonella

  • E. coli

  • Measles

• Environmental Samples

  • Food

  • Surface

  • Faucets

  • Cooling Towers

11. Implement Control & Prevention Measures

• Eliminate Source

  • Cleaning

  • Discarding food

• Mitigate Potential for Spread

  • Isolation of cases

  • Quarantine of contacts

  • Preventative antivirals for contacts

  • Masking

12. Initiate or Maintain Surveillance

• Typically, surveillance begins immediately once a potential outbreak is reported

• Maintenance of surveillance

  • High index of suspicion for cases meeting the case definition

  • Consider the outbreak ongoing until two incubation periods have passed with no cases

Example: Hepatitis A has an incubation period of 15-50 days.  Therefore, to declare the outbreak over, you must not have any outbreak related cases for 100 days.

13. Communicate Findings

• General Public (News)

• Key Stakeholders (local authorities, state agencies, CDC, FDA, EPA) 

• Contributions to Literature (CDC MMWR, journal publication, White Paper)