  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.
