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Epidemic Intelligence: Signals from surveillance systems. EpiTrain III – Jurmala, August 2006 Anne Mazick, Statens Serum Institut, Denmark. Epidemic intelligence. All the activities related to early identification of potential health threats

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Epidemic Intelligence:Signals from surveillance systems

EpiTrain III – Jurmala, August 2006

Anne Mazick, Statens Serum Institut, Denmark

Epidemic intelligence

All the activities related to

  • early identification of potential health threats

  • their verification, assessment and investigation

  • in order to recommend public health measures to control them.

Components & core functions

Early warning


Indicator vs. Event-based surveillance

  • Indicator-based surveillance

    • computation of indicators upon which unusual disease patterns to investigate are detected (number of cases, rates, proportion of strains…)

  • Event-based surveillance

    • the detection of public health events based on the capture of ad-hoc unstructured reports issued by formal or informal sources.

Scope of this presentation

  • What surveillance signals are required for EI

    • Current communicable disease surveillance

    • Additional more sensitive surveillance for new, unusual or epidemic disease occurence

  • Basic requirements for signal detection

  • Use of early warning surveillance systems

    3 examples

Indicator-based early warning systemsObjectives

  • to early identify potential health threats - alone or in concert with other sources of EI

    in order to recommend public health measures to control them

  • For new, emerging diseases

  • For unusual or epidemic occurence of known diseases

Indicator-based surveillance

  • Identified risks

    • Mandatory notifications

    • Laboratory surveillance

  • Emerging risks

    • Syndromic surveillance

    • Mortality monitoring

    • Health care activity monitoring

    • Prescription monitoring

  • Non-health care based

    • Poison centers

    • Behavioural surveillance

    • Environmental surveillance

    • Veterinary surveillance

    • Food safety/Water supply

    • Drug post-licensing monitoring

Current surveillance systems for communicable diseases


  • Main attributes

    • Representativity

    • Completeness

    • Predictive positive value


From infection to detectionProportion of infections detected


50 Shigella notifications (5%)

1000 Shigella infections (100%)


From infection to detection:Timeliness





From infection to detection:Timeliness

Urge doctors to report timely

Frequency of reporting

Immediately, daily, weekly





From infection to detection:Timeliness





Automated analysis,



From infection to detection:Timeliness


Automated analysis,



Potential sources of early signals

  • Laboratory test volume

  • Emergency & primary care total patient volume, syndromes

  • Ambulance dispatches

  • Over-the-counter medication sales

  • Health care hotline

  • School absenteeism

Sensitive systems for new,

unusual or epidemic diseases


To detect all events as early as possible

  • More sensitive case definitions

    • Cave: sensitivity ↑= false alerts ↑

      • costs of response

      • Social and political distress

    • Combining information from other sources of epidemic intelligence

  • Frequency of reporting

  • Automated analysis

  • Low alert thresholds

Current surveillance systems for communicable diseases

  • Important source for EI, but…

  • Additional systems needed to fulfil all EI objectives:

    • Timeliness

    • Sensitivity

  • For rapid detection of new, unusual or epidemic diseases

Principle of signal detection

  • To detect excess over the normally expected

  • Observed – expected = system alert

  • What are we measuring? Indicators

  • What is expected? Need historical data

  • Which statistics to use? Depends on disease

  • Where to set threshold? Depends on desired sensitivity

Early warning indicators

  • Early warning indicators

    • Count

    • Rates

      • Number of cases/population at risk/time

    • Proportional morbidity

      • % of ILI consultations among all consultations

    • Percentage of specific cases

      • case fatality ratio

      • % children under 1 years among measles cases

      • % of cases with certain strain

Statistical methods for early warning

  • Depends on the epidemiology of the disease under surveillance


  • Choice of threshold affected by

    • Objectives, epidemiology, interventions

  • Absolute value

    • Count: 1 case of AFP

    • Rate: > 2 meningo. meningitis/100,000/52 weeks

  • Relative increase

    • 2 fold increase over 3 weeks

  • Statistical cut-off

    • > 90th percentile of historical data

    • > 1.64 standard deviations from historical mean

    • Time series analysis

Clinical meningitis, Kara Region, Togo 1997




























WeeklyNotificationofFoodBorneIllness,NationalEWARN System,France,1994-1998

Use of statistics & computer tools

  • For systematic review of data on a regular basis

  • to extract significant changes drowned in routine tables of weekly data

  • They do not on its own detect and confirm outbreaks!

  • Epidemiological verification, interpretation and assessment ALWAYS required!

Tools do not make early warning systems, but early warning systems need appropriate tools

System alert interpretation

Every system alert

Other sources of epidemic intelligence

Media reports


Clinician concern


Food agencies

Meteorological data

Drug sales/prescription

International networks


Validate & analyse



Public health significance?

No Alert


Danish laboratory surveillance systemof enteric bacterial pathogens

  • To detect outbreaks and to analyse long-term trends

  • Administered by Statens Serum Insitute (SSI)

  • Danish reference laboratory

    • Receives all salmonella isolates for further typing

    • Also gets many other strains, including E. coli., for further typing

National register of enteric pathogens

  • At SSI

  • Includes everybody who test positive for a bacterial GI infection in Dk.

  • Person, county, agent, date of lab receiving specimen, travel, no clinical information

  • First-positives only

  • Mandatory weekly notifications from all 13 clinical laboratories

Outbreak algorithm

  • Computer program, which calculates if the current number of patients exceeds what we saw at the same time of year in the 5 previous years

  • Time variable: date of lab receiving specimen

  • Calculation made each week for specimens received in the week before last

  • Calculation made by county and nationally

  • Adjustment for season, long-term trends and past outbreaks

  • Uses poisson regression, principle developed by Farrington and friends

Present counts are compared to the counts in 7 weeks in each of the past 5 years

week 46

week 48

week 43

week 49



Current week & 35 past weeks



  • Each week the output is assessed by an epidemiologist

  • Alerts thought to represent real outbreaks are analysed further

  • Website www.mave-tarm.dk

Point source outbreak

Point source outbreak

Usefulness: Widespread outbreak

S. Oranienburg outbreak

  • Hypothesis generating interviews (7 cases)

  • All had eaten a particular chokolade from a german retail store

  • Outbreak in Germany (400 cases)

    • Case-control study pointed to chokolade

    • But the particular chokolade was very popular in Germany (not in Denmark)

  • Same DNA-profil

Werber et al. BMC Infectious Diseases 5 7 (2005)

What is the most useful?

  • Systematic weekly analysis

  • Defines expected levels

  • Good to detect widespread outbreaks with scattered cases

  • Good use of advanced lab typing method

”Early” warning signals from mortality surveillance

  • Excess deaths

  • due to known disease under surveillance

    • Increased incidence

    • Increased virulence

  • due to disease/threats not under surveillance

    • Known diseases

    • New, emerging threats

    • Environmental threats

    • Deliberate release

Would mortality surveillance been of use in 2003/04to assess the impact of Fujian influenza on children in Denmark?

  • Absence of signal

    • Reassurance of public

All-cause deaths and influenza like illness (ILI) consultation rate, 1998-2004, Denmark

Period of model fitting


Observed and expected all-cause deaths,1998-2004, Denmark,

Excess mortality

Model testing, season 2003/2004

Model testing, season 2003/2004

Model testing, season 2003/2004


disease surveillance

(flu, meningitis etc)

meteorological office


Media reports

Community concern


Clinician concern

Model testing, season 2003/2004


Observed and expected number of death among children (1-15y), Denmark, 1998-2004

Model testing, season 2003/2004

Evaluation of early warning and response systems

  • Important:

    • usefulness has not been established

    • investigating false alarms is costly

  • CDC tool for evaluation of surveillance systems for early detection of outbreaks

Early warning system in Serbia

  • ALERT implemented 2002

    To strenghten early detection of outbreaks of epidemic prone and emerging infectious diseases

    • 11 syndromes to detect priority communicable diseases

    • All primary health facilities report weekly aggregated data

    • Complements routine surveillance of individual confirmed cases

Evaluation of ALERT 2003

  • ALERT detected outbreaks more timely than the routine systems but ALERT did not detect all outbreaks

    • Missed clusters of brucellosis and tularaemia

  • ALERT procedures & response not regulated by law

  • Investigation and verification process that follows system alerts and signals not fully understood

  • Recommendations

    • Add data source (eg emergency wards) to increase sensitivity

    • Better integration with routine system

    • Change in surveillance perspective requires TRAINING!

Valenciano et al, Euro surv 2004; 9(5);1-2

Useful links

  • CDC. Framework for evaluating public health surveillance systems for early detection of outbreaks. http://www.cdc.gov/mmwr/preview/mmwrhtml/rr5305a1.htm

  • Annotated Bibliography for Syndromic Surveillance http://www.cdc.gov/EPO/dphsi/syndromic/index.htm

  • The RODS Open Source Project, Open Source Outbreak and Disease Surveillance Software http://openrods.sourceforge.net/

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