监测新冠发病率和死亡率的时空流行病学：德国小区域分析。
Monitoring the spatiotemporal epidemiology of Covid-19 incidence and mortality: A small-area analysis in Germany.
机构信息
Department of Population Medicine and Health Services Research, School of Public Health, Bielefeld University, Universitätsstraße 25, 33501 Bielefeld, Bielefeld, Germany; Section for Health Equity Studies & Migration, Department of General Practice and Health Services Research, University Hospital Heidelberg, Marsilius Arkaden (Turm West), Im Neuenheimer Feld 130.3, 69120 Heidelberg, Heidelberg, Germany.
Department of Population Medicine and Health Services Research, School of Public Health, Bielefeld University, Universitätsstraße 25, 33501 Bielefeld, Bielefeld, Germany; Section for Health Equity Studies & Migration, Department of General Practice and Health Services Research, University Hospital Heidelberg, Marsilius Arkaden (Turm West), Im Neuenheimer Feld 130.3, 69120 Heidelberg, Heidelberg, Germany.
出版信息
Spat Spatiotemporal Epidemiol. 2021 Aug;38:100433. doi: 10.1016/j.sste.2021.100433. Epub 2021 May 21.
Timely monitoring of incidence risks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and associated deaths at small-area level is essential to inform containment strategies. We analysed the spatiotemporal epidemiology of the SARSCoV- 2 pandemic at district level in Germany to develop a tool for disease monitoring. We used a Bayesian spatiotemporal model to estimate the district-specific risk ratios (RR) of SARS-CoV-2 incidence and the posterior probability (PP) for exceedance of RR thresholds 1, 2 or 3. Of 220 districts (55% of 401 districts) showing a RR > 1, 188 (47%) exceed the RR threshold with sufficient certainty (PP ≥ 80%) to be considered at high risk. 47 districts show very high (RR > 2, PP ≥ 80%) and 15 extremely high (RR > 3, PP ≥ 80%) risks. The spatial approach for monitoring the risk of SARS-CoV-2 provides an informative basis for local policy planning.
及时监测严重急性呼吸综合征冠状病毒 2（SARS-CoV-2）在小区域水平的发病风险和相关死亡，对于制定防控策略至关重要。我们分析了德国地区层面 SARSCoV-2 大流行的时空流行病学，以开发一种疾病监测工具。我们使用贝叶斯时空模型来估计 SARS-CoV-2 发病率的地区特异性风险比（RR），以及超过 RR 阈值 1、2 或 3 的后验概率（PP）。在 220 个（401 个地区的 55%）显示 RR > 1 的地区中，有 188 个（47%）具有足够的确定性（PP ≥ 80%）超过 RR 阈值，被认为处于高风险状态。47 个地区的风险非常高（RR > 2，PP ≥ 80%），15 个地区的风险极高（RR > 3，PP ≥ 80%）。用于监测 SARS-CoV-2 风险的空间方法为地方政策规划提供了信息基础。
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