Estimating functional parameters for understanding the impact of weather and government interventions on COVID-19 outbreak
Estimating functional parameters for understanding the impact of weather and government interventions on COVID-19 outbreak
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DOI：
10.1214/22-aoas1601
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发表时间：
2021-01
期刊：
影响因子：
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通讯作者：
Chih-Li Sung
中科院分区：
文献类型：
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作者：
Chih-Li Sung
由于 2019 年冠状病毒病 (COVID-19) 对全球公共卫生和经济产生了深远影响，评估对病毒传播的影响并制定有效策略来应对这一挑战变得至关重要。提出了一种从具有功能参数的 SIR 流行病模型派生的新统计模型，以了解天气和政府干预对病毒传播的影响，并提供美国八个大都市区的 COVID-19 感染预测。该模型利用贝叶斯推理和高斯过程先验对函数参数进行非参数研究，并采用敏感性分析来研究这些因素的主要影响和交互影响。该分析揭示了一些重要结果，包括天气与政府干预措施之间的潜在相互作用影响，这为政策制定者缓解 COVID-19 疫情的有效策略提供了新的线索。
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.