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Outbreak Investigation. Best Practice/Methods Practical Reference Points. Learning Objectives. At the end of the session, the participants will be able to: List down the objectives of outbreak Describe steps in outbreak investigation

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outbreak investigation

Outbreak Investigation

Best Practice/Methods

Practical Reference Points

learning objectives
Learning Objectives
  • At the end of the session, the participants will be able to:
    • List down the objectives of outbreak
    • Describe steps in outbreak investigation
    • Explain the importance of conducting timely outbreak investigation
detecting an outbreak
Detecting an outbreak

Routine surveillance


Rumor verification

General public



Is this an


is it an outbreak
Is it an outbreak?
  • More than expected cases
    • Illustrates the importance of surveillance and timely analysis
  • Clustered in time, place or person
    • Pattern recognition
  • Concern from a HCW, school or media
    • Rumour verification
    • Encourage participation in the system
why investigate outbreaks
Why investigate outbreaks?
  • Stop the outbreak (new cases)
  • Increase our knowledge
  • Prevent new episodes
  • Evaluate the surveillance system
  • Establish a surveillance system
  • Learn field epidemiology by doing
specific objectives of an investigation
Specific objectives of an investigation


  • Causal agent
  • Mode of transmission
  • Source
  • Carrier
  • Population at risk
  • Exposure causing disease (risk factors)
real time vs retrospective investigation
Real time vs. retrospective investigation
  • Outbreaks in existence for several days, weeks, months.
  • Based on the memory of the people
  • Data already collected
    • To be or not to be used

It is never to late,

but it can also be more difficult

  • Collect preliminary information
    • Available data
    • Consult experts (microbiologist, veterinarian, entomologist etc)
    • Check search engines e.g., PUBmed
    • Search from both formal and informal surveillance system (event based and indicator based)
    • Prepare a short memo
  • Inform the concerned
  • Get authorization, travel itinerary
  • Investigation committee
    • Multidisciplinary
    • Assign person in charge
    • Define tasks
example community epidemic due to s typhimurium jura may june 1997
Example: Community epidemic due to S. Typhimurium, Jura, May-June 1997


  • Alert: PH medical officer
  • 80 cases of salmonellosis in 5 weeks
  • Salmonella Typhimurium
  • Clustered in the South department of Jura
  • No connection (a priori) among cases
  • Pressure of media, of politicians
  • Local Department of Public Health, Veterinary Services, Centre National Reference, National Institute of Public Health
steps in outbreak investigation
Steps in Outbreak Investigation

the sequence is not important !

  • Descriptive steps
    • Determine existence of an outbreak
    • Confirm the diagnosis:
      • Which diseases are we talking about?
    • Define a case; find and count cases
    • Orient data as to:
      • Time (When?)
      • Place (Where?)
      • Person (Who?)
steps in outbreak investigation the sequence is not important
Steps in Outbreak Investigation the sequence is not important !
  • Analyse
    • Generate hypotheses
    • Test the hypotheses
    • Compare each hypothesis with facts
    • Plan a more systematic study
  • Synthesis and action
    • Write a report, communicate findings
    • Control measure and prevention
1 determine existence of an outbreak
1. Determine existence of an outbreak
  • Outbreak
    • n° observed cases > n° expected cases
  • Expected cases?
    • Surveillance data
    • Clinicians, hospital registers
    • Hospital investigation, lab, doctors, schools..
  • Be careful of artefacts!
    • Seasonal variation: (diarrhoea)
    • Notification variation: (new surveillance system in place)
    • Diagnostic variation: (new technique)
    • Diagnostic mistake: (“false epidemic")
2 confirm the diagnosis
2. Confirm the diagnosis
  • Laboratory
    • serology
    • isolation, serotype, lysotype, etc.
    • toxic agent
  • Meet the doctors
  • See the patients
  • Visit the laboratories

It is not necessary to confirm all the cases

3 define a case find and count cases
3. Define a case; find and count cases
  • 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
    • restrictions by time, place and person (epidemiological link)
    • laboratory findings (generally)
3 1 define a case
Suspected cases

clinical case definition

enough for immediate action

Confirmed cases

Collect relevant samples


few cases (10-20)

3.1. Define a case

Do not wait for laboratory results to

start treatment and control activities!

3 2 find and count the cases
3.2. Find and count the cases
  • Information sources
    • All possible sources (NGOs, local leaders, etc)
    • Hospitals, health centres, laboratories, doctors, nurses
    • schools, camps, settlements
    • Radio, door to door
    • « snow ball »
    • Laboratory
  • How many? No strictly all
  • Collected information
    • Demographics
    • clinical and biological
    • eventual exposure
4 orient data as to
4. Orient data as to:
  • Time
  • Place
  • Persons
4 1 data description time epidemic curve
4.1 Data description: TIME epidemic curve
  • Case distribution over time (according to the date - hour, week - of onset of signs)
  • Onset, peak, importance, time, end of epidemic
  • Abnormal cases
  • Allow to make hypothesis:
    • incubation period, pathogen responsible
    • source, mode of transmission
    • time of exposure
  • Epidemic evolution
Example: Epidemic curve of Cases due to S.Typhimurium by week of onset of symptoms of isolated bacteria, Jura, May- June 1997.

Number of Cases

One Case





4 2 data description place
4.2 Data description: PLACE
  • Residence
  • Place of exposure
    • work, food places, journeys, tour
  • Maps (“mud maps”, points,attack rate)

Identify areas at risk,

Identify population at risk

Identify priority areas for control activities

4 3 data description person
4.3 Data description: PERSON
  • Distribution of cases by age, sex, profession, etc (Numerator)
    • ex: 50 women, 100 men
  • Distribution of variables in the population from where cases are coming (Denominator)
    • ex: 1500 women, 1000 men
  • Compute attack rate
    • ex: women 50/1500 , men 100/1000

=> Identification of sub-group(s) at risk

Example: Data Description - PersonInfection by S.TyphimuriumAttack Rate by age group, Jura, May-June 1997
Example: Data Description - Person Infection by S.Typhimurium, Attack Rate by age group, Jura, may-June 1997
5 generate hypotheses
5. Generate hypotheses

Starting from:

  • Descriptive information (TPP)
  • Knowledge of the disease
  • Exploratory study on some cases


  • Causal agent
  • Source
  • Way of transmission
  • Carrier


example generating hypothesis infection of s typhimurium
Example: Generating HypothesisInfection of S.Typhimurium
  • Descriptive data:
    • Agent: S. Typhimurium lysotype 12 atypical
    • Time, epidemic curve: persistent common source
    • Place: cases clustered in the south of Jura
    • Persons:
      • Attack rate higher among children
      • All ages affected
      • Muslim among the cases
5 generate hypothesis exploratory survey
5. Generate HypothesisExploratory Survey
  • Formulate hypothesis
  • Interview some cases:
    • Open questionnaire and complete
  • Common exposure?

Example Jura:

  • Big questionnaire, inclusion of regional products (cheese)
  • 17 cases interviewed
6 test the hypothesis
6. Test the hypothesis
  • Objectives
    • Specific exposure: the carrier and the source
    • Factors facilitating or protective
      • host, agent, environment
  • Survey to identify aetiology:
    • Cohort
      • uses attack rates
      • best in a small, well-defined population
    • Case-control
      • odds ratioquantifies the relationship b/w exposure and disease
6 test the hypothesis case control
6. Test the hypothesisCase-control
  • Compare
    • Proportion of exposed among cases
    • Proportion of exposed among the non cases (controls)
  • Compute Odds Ratio (OR) and Confidence Interval (CI) at 95%
  • Select controls
    • “Not sick”
    • Susceptible (e.g., not immunised)
    • Coming from the same population of cases
    • The same chances of being exposed
7 compare the hypothesis with facts
7. Compare the hypothesis with facts
  • Compare the results
    • clinical observation
    • biological examinations
    • epidemiological studies
    • statistical tests
  • The hypothesis should be:
    • plausible
    • biologically acceptable
    • explain causal agent, source, mode transmission, time of exposure
example comparison of hypotheses with observed facts jura may june 1997
Example: Comparison of hypotheses with observed facts, Jura, May-June 1997

Cheese A

  • Raw milk (plausible)
  • Consumed by children (meets persons affected by the outbreak)
  • Regional product (meets place affected)
  • Collect cheese among the cases (data microbiological)
    • S. Typhimurium identified in 3 cheeses A
    • Other cheeses negatives
8 plan a more systematic study
8. Plan a more systematic study
  • At the same time, and oriented by the epidemiological survey
    • Environmental survey
    • Microbiological survey
  • Plan more systematic studies (if needed)
    • More cases, more controls
    • Dose-effect, facilitating factors..
example complementary studies epidemic of s typhimurium jura may june 1997
Example: Complementary studiesEpidemic of S. TyphimuriumJura, May-June, 1997
  • Microbiological survey:
    • Food collection among cases
    • Sample collection among cases suppliers
    • Comparison of human specimens and food products
  • Survey on the distribution network of cheese A
  • Survey among the producers:
    • Veterinary
    • Labour medicine
    • Environmental
survey distribution network
Survey distribution network

Example: Complementary studiesEpidemic of S. TyphimuriumJura, May-June, 1997


Cheese dairy


9 write a report communicate findings
9. Write a report; communicate findings
  • To be written on site
  • Promotes synthesis (of the objectives)
  • Documents the event (for evaluation/ legal purposes)
  • Allows communication of results
  • Provides recommendations
  • Pedagogical tool (training material)
10 control measures and prevention
10. Control measures and prevention
  • Don’t wait for the end of the investigation :
    • General measures at beginning
    • Specific measures according to the results
  • Kinds of measures to control :
    • The source (e.g.: chlorination of water)
    • The transmission (e.g.: hygiene measures)
    • The carrier (e.g.: recall a lot of suspected cheese)
    • Reduce the susceptibility of host (e.g.: vaccination)
    • Communicate risk if outbreak is affecting the public
  • Example Jura:
    • Personal Hygiene
    • Adequate cooking of meat
    • Recall of the incriminated product (Fromage A)

" The art of epidemiological reasoning is to make some reasonable conclusions

starting from imperfect data"

George W. Comstock (1915-2007)

Physician and Professor Emeritus

Johns Hopkins Bloomberg School of Public Health

but better information leads to better results
But better information… leads to better results
  • This means having:
    • A good description of Time, Place, Person (TPP)
    • Good data collection and preservation of samples
    • A well coordinated multidisciplinary team

Immediate detection

Immediate response

Reduced morbidity and mortality

outbreak detection and response
Outbreak Detection and Response









for control



outbreak detection and response44
Outbreak Detection and Response









for control



ethical aspect
Ethical Aspect
  • Securing consent (participants)
  • Informing local authorities, communities
  • Ethical treatment of animals
best practices
Best practices
  • Establish clear and concise policies and procedures
  • Careful documentation and proper recording of events/results
  • Effective communication skills
  • Evaluate and review responses
  • Expect the unexpected
    • Key people away, new, emerging pathogen, demystifying rumour, etc.
  • De Valk, Henriette, French Institute for Public Health Surveillance (Institut de veille sanitaire, InVS)
  • European Programme on Intervention Epidemiology Training