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 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.
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