  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.
