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.

中文翻译：

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应用到印度尼西亚西爪哇省的贝叶斯时空预测和 COVID-19 风险制图

冠状病毒病 (COVID-19) 已迅速蔓延到包括印度尼西亚在内的多个国家。绘制其时空模式和预测（小区域）爆发对于遏制和缓解战略至关重要。因此，我们引入了一种新感染的简约时空模型，该模型可以产生准确的预测，但只需要有关每个地理单位和时间段的发病数量和人口规模的信息。由于对 COVID-19 原因的了解有限以及需要迅速采取行动来控制疫情，因此模型简约很重要。我们概述了贝叶斯估计、预测和映射的基础知识，特别是对于热点的识别。该方法应用于印度尼西亚西爪哇省的县级数据。