论文标题
在2020-2021大流行期间，美国两个州的贝叶斯时空SIR SIR建模
Bayesian Space-time SIR modeling of Covid-19 in two US states during the 2020-2021 pandemic
论文作者
论文摘要
本文描述了2020 - 2021年在两个对比的美国国家中，COVID-19的3波的贝叶斯Sir Sir建模。在县级评估了各种模型，以评估合适的优点，并对混杂的预测变量进行了评估。发现具有三个剥夺预测因子的模型和邻里影响很重要。此外，还发现了Google移动性的工作指数可提供对传输动态的更多解释。
This paper describes the Bayesian SIR modeling of the 3 waves of Covid-19 in two contrasting US states during 2020-2021. A variety of models are evaluated at the county level for goodness-of-fit and an assessment of confounding predictors is also made. It is found that models with three deprivation predictors and neighborhood effects are important. In addition the work index from Google mobility was also found to provide increased explanation of the transmission dynamic.
