Estimating Functional Parameters for Understanding the Impact of Weather and Government Interventions on COVID-19 Outbreak

• Activity Number: 133 - Statistical Issues in Environmental Epidemiology and Pharmacoepidemiology
• Type: Contributed
• Date/Time: Monday, August 9, 2021 : 1:30 PM to 3:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Chih-Li Sung*
• Companies: Michigan State University
• Keywords: Model calibration; Basic reproduction number; Epidemic model; Nonparametric regression; Sensitivity analysis

Abstract

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 and also provide the forecasts of COVID-19 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.