  With rapid transmission, the coronavirus disease 2019 (COVID-19) has led to
over 2 million deaths worldwide, posing significant societal challenges.
Understanding the spatial patterns of patient visits and detecting the local
spreading events are crucial to controlling disease outbreaks. We analyze
highly detailed COVID-19 contact tracing data collected from Seoul, which
provides a unique opportunity to understand the mechanism of patient visit
occurrence. Analyzing contact tracing data is challenging because patient
visits show strong clustering patterns while clusters of events may have
complex interaction behavior. To account for such behaviors, we develop a novel
interaction Neyman-Scott process that regards the observed patient visit events
as offsprings generated from a parent spreading event. Inference for such
models is complicated since the likelihood involves intractable normalizing
functions. To address this issue, we embed an auxiliary variable algorithm into
our Markov chain Monte Carlo. We fit our model to several simulated and real
data examples under different outbreak scenarios and show that our method can
describe spatial patterns of patient visits well. We also provide visualization
tools that can inform public health interventions for infectious diseases such
as social distancing.
