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Early Detection Intervals for Evaluating Event-Based Surveillance System: Reference Dataset Development Study
Yannan Shen1, Philip AbdelMalik2, Russell J Steele3
1Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, Faculty of Medicine and Health Sciences, McGill University, Suite 1200, 2001 McGill College Avenue, Montreal, QC, H3A 1G1, Canada, 1 514-396-1153.
This study introduces early detection intervals for the Omicron variant, revealing disparities in detection times across countries. High-income nations with better sequencing data had longer intervals, while low-income countries experienced shorter ones.
Area of Science:
- Epidemiology
- Public Health Surveillance
- Genomic Epidemiology
Background:
- Event-based surveillance (EBS) aids early detection of health threats using diverse data sources.
- Evaluating EBS systems is challenging due to a lack of reference data on outbreak onsets.
Purpose of the Study:
- Introduce the concept of an "early detection interval" for infectious diseases.
- Create a dataset of these intervals for the SARS-CoV-2 Omicron variant across multiple countries.
- Provide reference data to evaluate the timeliness of EBS systems.
Main Methods:
- Defined "early detection interval" as time from pathogen introduction to detectability in surveillance data.
- Determined Omicron introduction dates using phylogenetic studies and genome databases.
- Estimated interval end dates via Bayesian change point detection on COVID-19 case counts.
Main Results:
- Dataset includes early detection intervals for Omicron in 117 countries (median length 28 days).
- High sequencing availability correlated with earlier start dates but prolonged intervals.
- Low-income countries, underrepresented, showed shorter intervals (median 16 days).
Conclusions:
- The created dataset facilitates EBS system evaluation and guides public health practice.
- Highlights significant cross-country disparities in data quality, especially genomic data.
- Emphasizes the need for enhanced data collection and sharing in low-resource settings.
