Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots
This work addresses the challenge of accurately capturing infection hotspots in epidemiological models for COVID-19, which is incremental as it builds on existing models by incorporating mobility patterns.
The authors tackled the problem of modeling COVID-19 transmission by introducing a temporal point process framework that explicitly represents infection hotspots and individual mobility, resulting in a model where infection counts are naturally overdispersed, with simulations validated using demographic and site data from Bern, Switzerland.
Multiple lines of evidence strongly suggest that infection hotspots, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19. However, most of the existing epidemiological models fail to capture this aspect by neither representing the sites visited by individuals explicitly nor characterizing disease transmission as a function of individual mobility patterns. In this work, we introduce a temporal point process modeling framework that specifically represents visits to the sites where individuals get in contact and infect each other. Under our model, the number of infections caused by an infectious individual naturally emerges to be overdispersed. Using an efficient sampling algorithm, we demonstrate how to estimate the transmission rate of infectious individuals at the sites they visit and in their households using Bayesian optimization and longitudinal case data. Simulations using fine-grained and publicly available demographic data and site locations from Bern, Switzerland showcase the flexibility of our framework. To facilitate research and analyses of other cities and regions, we release an open-source implementation of our framework.