CERENA, DECivil, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001, Lisbon, Portugal.
Int J Health Geogr. 2020 Jul 6;19(1):25. doi: 10.1186/s12942-020-00221-5.
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.
SARS-CoV-2 疫情的迅速蔓延具有同时的时间和空间动态。这种行为是由多种因素共同作用的结果，包括社会因素，这导致了国家之间和国家内部时空模式演变的显著差异。通常，使用空间平滑技术来绘制健康结果，很少评估空间预测的不确定性。作为替代方法，我们建议应用直接块序贯模拟来模拟葡萄牙本土 COVID-19 感染风险的空间分布。考虑到葡萄牙卫生局提供的每日感染数据，我们按直辖市计算了感染率的每日更新，并将其用作地理统计学模拟中的实验数据。该模型考虑了与每个直辖市人口规模相关的不确定性/误差。感染风险地图的每日更新计算结果是对 100 个每日更新感染风险的地理统计学实现的集合中的中位数模型的计算结果。还使用地理统计学实现的集合来计算使用四分位距的空间预测的相关空间不确定性。风险地图每日更新，显示感染风险较高的地区以及与随时间发展相关的关键动态。
