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Activity Number: 260 - Science-Integrated Statistical Learning
Type: Invited
Date/Time: Tuesday, August 9, 2022 : 10:30 AM to 12:20 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #320459
Title: When Epidemic Models Meet Statistics: Understanding the Impact of Weather and Government Interventions on COVID-19 Outbreak
Author(s): Chih-Li Sung*
Companies: Michigan State University
Keywords: Basic reproduction number; Asymptomatic infections; Epidemic model; Nonparametric regression; Sensitivity analysis

As the coronavirus disease 2019 (COVID-19) has shown profound effects on public health and the economy worldwide, it becomes crucial to assess the impact on the virus transmission and develop effective strategies to address the challenge. A new statistical model derived from the SIR epidemic model with functional parameters is proposed to understand the impact of weather and government interventions on the virus spread in the presence of asymptomatic infections among eight metropolitan areas in the United States. The model uses Bayesian inference with Gaussian process priors to study the functional parameters nonparametrically, and sensitivity analysis is adopted to investigate the main and interaction effects of these factors. This analysis reveals several important results including the potential interaction effects between weather and government interventions, which shed new light on the effective strategies for policymakers to mitigate the COVID-19 outbreak.

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

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