We propose an endemic-epidemic model: a negative binomial space–time autoregression, which can be employed to monitor the contagion dynamics of the COVID-19 pandemic, both in time and in space. The model is exemplified through an empirical analysis of the provinces of northern Italy, heavily affected by the pandemic and characterised by similar non-pharmaceutical policy interventions.

中文翻译：

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了解 COVID-19 时空演变的地方流行病模型

我们提出了一种地方病-流行病模型：负二项式时空自回归，可用于监测 COVID-19 大流行在时间和空间上的传染动态。该模型通过对意大利北部省份的实证分析来举例说明，这些省份受疫情影响严重，并具有类似的非药品政策干预措施。