Bayesian spatiotemporal forecasting and mapping of COVID‐19 risk with application to West Java Province, Indonesia
提出一个简洁的时空模型，仅利用病例数和人口数据预测COVID-19新发病例，并识别热点区域，对印尼西爪哇省县级数据进行了应用。
The coronavirus disease (COVID-19) has spread rapidly to multiple countries including Indonesia. Mapping its spatiotemporal pattern and forecasting (small area) outbreaks are crucial for containment and mitigation strategies. Hence, we introduce a parsimonious space-time model of new infections that yields accurate forecasts but only requires information regarding the number of incidences and population size per geographical unit and time period. Model parsimony is important because of limited knowledge regarding the causes of COVID-19 and the need for rapid action to control outbreaks. We outline the basics of Bayesian estimation, forecasting, and mapping, in particular for the identification of hotspots. The methodology is applied to county-level data of West Java Province, Indonesia.