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Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis
Iris Ganser1,2, Rodolphe Thiébaut2,3, David L Buckeridge1
1McGill Clinical and Health Informatics, School of Population and Global Health, McGill University, Montreal, QC, Canada.
JMIR Public Health and Surveillance
|October 31, 2022
Summary
Event-based surveillance (EBS) shows variable performance in detecting infectious disease outbreaks globally. Factors like data quality and country development impact timeliness, necessitating improvements, especially in low-resource settings.
Area of Science:
- Public Health
- Epidemiology
- Infectious Disease Surveillance
Background:
- Robust infectious disease surveillance is vital for public health.
- Event-based surveillance (EBS) uses web data for timely outbreak detection.
- Global systematic evaluation of EBS outbreak detection performance is lacking.
Purpose of the Study:
- Assess variability in timing and frequency of EBS reports versus true outbreaks.
- Identify determinants influencing EBS performance variability.
- Examine seasonal influenza epidemics in 24 countries as a case study.
Main Methods:
- Utilized influenza reports from HealthMap and WHO EIOS (2013-2019).
- Used FluNet virological data as the gold standard.
- Applied Bayesian change point analysis to detect epidemic periods and calculated sensitivity, specificity, PPV, and timeliness.
Main Results:
- Detected 73.5% of outbreaks, but only 9.2% within 2 weeks of onset.
- Positive predictive value varied widely (0-100%) across systems and countries.
- High variability in timeliness (0-94%) and specificity (59-100%) observed.
- Country-specific factors like report volume, HDI, and geography influenced performance.
Conclusions:
- Global variation in EBS performance is significant.
- Monitoring report frequency alone is insufficient for timely outbreak detection.
- Low data quality and frequency in LMICs impair EBS sensitivity and timeliness, requiring enhanced development and evaluation in resource-limited settings.
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