Estimating functional parameters for understanding the impact of weather and government interventions on COVID-19 outbreak

TL;DR

This paper introduces a Bayesian nonparametric model based on the SIR framework to analyze how weather and government interventions jointly influence COVID-19 transmission, providing insights for policy strategies.

Contribution

It develops a novel functional parameter model with Gaussian process priors to nonparametrically assess the impact of weather and interventions on COVID-19 spread.

Findings

Interaction effects between weather and policies identified

Model reveals key factors influencing transmission dynamics

Provides a tool for policymakers to evaluate intervention strategies

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.