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Activity Number: 110 - Spatio-Temporal Modeling of the COVID-19 Pandemic: Statistics, Data, and the Stories They Tell
Type: Invited
Date/Time: Monday, August 9, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #317028
Title: Use of Time-Varying Contact Data in Endemic-Epidemic Modeling of COVID-19 Incidence
Author(s): Leonard Held*
Companies: University of Zurich
Keywords: endemic-epidemic modelling; time-varying transmission weights; power law-based adjacency weights; social distancing policies; Covid-19

The endemic-epidemic (EE) modelling framework for infectious disease surveillance data was introduced in 2005 and extended in recent years. It has seen applications to many types of infectious diseases, most recently COVID-19. We examine how social distancing policies can be incorporated through the use of time-varying transmission weights in such a model. Our work supports and extends inclusion of age-structured contact data in the EE framework. Social distancing policies reduce number of opportunities for susceptible individuals to be at infection risk and so we examine the model when contacts are adjusted over time to reflect policy. This motivates a counterfactual analysis of the potential increase in disease spread in the absence of certain social distancing policies. We also consider travel-related policy and its effect on spatio-temporal disease spread through time-varying adjacency matrices. These are created on the basis of mobility data. This supports and complements the inclusion of power law-based adjacency weights in the EE framework. Finally we provide a brief foray into the pitfalls associated with not correcting for underreporting when modelling infectious disease.

Authors who are presenting talks have a * after their name.

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