论文标题

估计飓风撤离中的共同进口风险:德克萨斯州飓风劳拉的预测框架

Estimating Importation Risk of Covid-19 in Hurricane Evacuations: A Prediction Framework Applied to Hurricane Laura in Texas

论文作者

Audirac, Michelle, Tec, Mauricio, Garcia-Tejeda, Enrique, Fox, Spencer

论文摘要

2020年8月,由于得克萨斯州的一场大型夏季共同涌现,预测劳拉飓风正在向东德克萨斯海岸线沿线的600万居民进行追踪,威胁要在全州范围内传播Covid-19,并引起大流行的复苏。为了协助面对双重威胁的地方当局,我们在统计框架中综合了对沿海居民的调查期望,并观察到飓风疏散率,该统计框架与当地大流行状况相结合,预测了Covid-19如何在飓风中散布。对于劳拉飓风,我们估计有499,500 [90%可信间隔(CI):347,500,624,000]人撤离了德克萨斯县,没有一个县累积超过2.5%的飓风疏散,占2,900 [90%CI:1,1,700,5,800,80000] covend,covend covend covend covend covend covend covend covend covend covend covend covend covend covend covend covend。通常,接收估计集中在人口密度较高的地区。尽管如此,在小区中,预计进口风险较高,在我们的案例研究中,每10,000名居民的进口数量最高为10个。总体而言,我们提出了一个灵活且可转移的框架,该框架捕获了空间异质性,并结合了自然灾害后人口运动的地理组成部分。随着飓风在频率和强度上的不断增加,我们的框架可以响应于指导灾难准备和计划的预期飓风途径而部署。

In August 2020, as Texas was coming down from a large summer COVID-19 surge, forecasts suggested that Hurricane Laura was tracking towards 6M residents along the East Texas coastline, threatening to spread COVID-19 across the state and cause pandemic resurgences. To assist local authorities facing the dual-threat, we integrated survey expectations of coastal residents and observed hurricane evacuation rates in a statistical framework that combined with local pandemic conditions predicts how COVID-19 would spread in response to a hurricane. For Hurricane Laura, we estimate that 499,500 [90% Credible Interval (CI): 347,500, 624,000] people evacuated the Texan counties, that no single county accumulated more than 2.5% of hurricane evacuees, and that there were 2,900 [90% CI: 1,700, 5,800] exportations of Covid-19 across the state. In general, reception estimates were concentrated in regions with higher population densities. Nonetheless, higher importation risk is expected in small Districts, with a maximum number of importations of 10 per 10,000 residents in our case study. Overall, we present a flexible and transferable framework that captures spatial heterogeneity and incorporates geographic components for predicting population movement in the wake of a natural disaster. As hurricanes continue to increase in both frequency and strength, our framework can be deployed in response to anticipated hurricane paths to guide disaster preparedness and planning.

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