Geostatistical COVID-19 infection risk maps for Portugal
Geostatistical COVID-19 infection risk maps for Portugal
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DOI：
10.1186/s12942-020-00221-5
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发表时间：
2020-07-06
影响因子：
4.9
通讯作者：
Soares, Amilcar
中科院分区：
文献类型：
--
作者：
Azevedo, Leonardo;Pereira, Maria Joao;Soares, Amilcar
新冠肺炎疫情的快速传播具有同步的时空动态。这种行为是包括社会因素在内的多种因素复杂结合的结果，这些因素导致国家之间和国家内部时空格局的演变存在显著差异。通常，空间平滑技术用于绘制健康结果图，很少评估空间预测的不确定性。作为替代方案，我们建议应用直接块序贯模拟来模拟葡萄牙大陆COVID-19感染风险的空间分布。鉴于葡萄牙卫生总局每天提供的感染数据数量，每日更新的感染率由各城市计算，并用作地质统计模拟中的实验数据。该模型考虑了与每个城市人口规模相关的不确定性/误差。感染风险图每日更新的计算结果来自于感染风险每日更新的100个地理统计实现的一个集合的中位数模型。地统计实现的集合也被用来计算空间预测的相关空间不确定性，利用四分位间距。风险图每天更新，并显示感染风险较大的区域及其随时间发展的关键动态。
The rapid spread of the SARS-CoV-2 epidemic has simultaneous time and space dynamics. This behaviour results from a complex combination of factors, including social ones, which lead to significant differences in the evolution of the spatiotemporal pattern between and within countries. Usually, spatial smoothing techniques are used to map health outcomes, and rarely uncertainty of the spatial predictions are assessed. As an alternative, we propose to apply direct block sequential simulation to model the spatial distribution of the COVID-19 infection risk in mainland Portugal. Given the daily number of infection data provided by the Portuguese Directorate-General for Health, the daily updates of infection rates are calculated by municipality and used as experimental data in the geostatistical simulation. The model considers the uncertainty/error associated with the size of each municipality's population. The calculation of daily updates of the infection risk maps results from the median model of one ensemble of 100 geostatistical realizations of daily updates of the infection risk. The ensemble of geostatistical realizations is also used to calculate the associated spatial uncertainty of the spatial prediction using the interquartile distance. The risk maps are updated daily and show the regions with greater risks of infection and the critical dynamics related to its development over time.