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Accepted for/Published in: JMIR Public Health and Surveillance

Date Submitted: Jan 5, 2022
Date Accepted: Sep 6, 2022

The final, peer-reviewed published version of this preprint can be found here:

Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis

Ganser I, Thiébaut R, Buckeridge DL

Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis

JMIR Public Health Surveill 2022;8(10):e36211

DOI: 10.2196/36211

PMID: 36315218

PMCID: 9664335

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Global variation in event-based surveillance for disease outbreak detection: A time series analysis

  • Iris Ganser; 
  • Rodolphe Thiébaut; 
  • David Llewellyn Buckeridge

ABSTRACT

Background:

Robust and flexible infectious disease surveillance is crucial for public health. Event-based surveillance (EBS) was developed to allow timely detection of infectious disease outbreaks using mostly web-based data. Despite its widespread use, EBS has not been evaluated systematically on a global scale in terms of outbreak detection performance.

Objective:

To assess the variation in timing and frequency of EBS reports compared to true outbreaks and to identify the determinants of variability, using the example of seasonal influenza epidemics in 24 countries.

Methods:

We obtained influenza-related reports from two EBS systems, HealthMap and the WHO Epidemic Intelligence from Open Sources (EIOS), and weekly virologic influenza counts from FluNet as a gold standard. Epidemic influenza periods were detected based on report frequency using Bayesian change point analysis. Timely sensitivity, i.e., outbreak detection within the first two weeks before or after an outbreak onset, was calculated along with sensitivity, specificity, positive predictive value, and timeliness of detection. Linear regressions were performed to assess the influence of country-specific factors on EBS performance.

Results:

Overall, monitoring the frequency of EBS reports detected 73.5% of outbreaks, but only 9.2% within two weeks of onset; in the best case, monitoring the frequency of health-related reports identified 29% of outbreaks within two weeks of onset. We observed a large degree of variability in all evaluation metrics across countries. The number of EBS reports available within a country, the human development index, and the country’s geographical location partially explained this variability.

Conclusions:

Monitoring the frequency of EBS reports allowed just under 10% of seasonal influenza outbreaks to be detected in a timely manner in a worldwide analysis, with a large variability in detection capabilities. This article documents the global variation of EBS performance and demonstrates that monitoring report frequency alone in EBS may be insufficient for timely detection of outbreaks. Moreover, factors such as human development index and geographical location of a country may influence the performance of EBS and should be considered in future evaluations.


 Citation

Please cite as:

Ganser I, Thiébaut R, Buckeridge DL

Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis

JMIR Public Health Surveill 2022;8(10):e36211

DOI: 10.2196/36211

PMID: 36315218

PMCID: 9664335

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