Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots

TL;DR

This paper introduces a novel temporal point process model that explicitly captures individual mobility and contact sites to better understand COVID-19 transmission, especially in hotspots, and provides tools for estimating transmission rates from data.

Contribution

It presents a new modeling framework that explicitly represents visits to contact sites and infection hotspots, addressing limitations of existing epidemiological models.

Findings

Model naturally produces overdispersed infection counts.

Efficient Bayesian estimation of transmission rates from case data.

Framework demonstrated with real demographic and site data from Bern, Switzerland.

Abstract

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.