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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 #317029
Title: A Latent Spatial Model for Pandemic Prediction
Author(s): Marcos O. Prates*
Companies: Universidade Federal de Minas Gerais
Keywords: Epidemic modeling; epidemic evolution and prediction; Hierarchical modeling; Covid-19

Epidemic modeling consists of the specification of an underlying structure that could rely entirely on epidemiological reasoning, be data-driven or a combination of them. In any case, it is based on identification of characteristics that are shared by many regions. Some of these features present similarities across observational units. Hierarchical modeling is particularly useful in these settings as it allows the explicit incorporation of these similarities, thus enabling borrowing of information across regions. The resulting set-up is suitable for estimation of the epidemic evolution and prediction of future epidemic cases, In this work, a number of options are considered, including those taking spatial configuration into account. These ideas are illustrated in the analysis of the evolution of Covid19 in Brazil, integrated across its 27 states.

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

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