Combining digital data streams and epidemic networks for real time outbreak detection
This addresses the challenge of noisy epidemic surveillance for public health, though it is incremental as it builds on existing aggregation methods.
The authors tackled the problem of real-time outbreak detection by developing LRTrend, a machine learning framework that aggregates diverse data streams and learns epidemic networks, enabling detection of COVID-19 waves within 2 weeks of onset when cases are a small fraction of the peak.
Responding to disease outbreaks requires close surveillance of their trajectories, but outbreak detection is hindered by the high noise in epidemic time series. Aggregating information across data sources has shown great denoising ability in other fields, but remains underexplored in epidemiology. Here, we present LRTrend, an interpretable machine learning framework to identify outbreaks in real time. LRTrend effectively aggregates diverse health and behavioral data streams within one region and learns disease-specific epidemic networks to aggregate information across regions. We reveal diverse epidemic clusters and connections across the United States that are not well explained by commonly used human mobility networks and may be informative for future public health coordination. We apply LRTrend to 2 years of COVID-19 data in 305 hospital referral regions and frequently detect regional Delta and Omicron waves within 2 weeks of the outbreak's start, when case counts are a small fraction of the wave's resulting peak.