论文标题
COVID-19具有流行病学上有意义的动态的无偏时空风险模型（用于Covid-19疾病的时空建模的系统框架）
An unbiased spatiotemporal risk model for COVID-19 with epidemiologically meaningful dynamics (A systematic framework for spatiotemporal modelling of COVID-19 disease)
论文作者
论文摘要
传染病（例如COVID-19）的时空建模涉及使用各种流行病学指标，例如病例的区域比例或区域阳性率。尽管观察它们随着时间的变化对于估计区域疾病负担至关重要，但这些措施的动力学特性以及交叉关系的动力学特性并未系统解释。在这里，我们提供了一个时空框架，该框架由六个常用和新建的流行病学指标组成，并进行了案例研究评估。我们介绍了一种精致的风险模型，该模型既不受人口规模差异，也不受到测试的空间异质性的偏见。特别是，提出的方法可用于公正地识别Covid-19风险升高的时间段，而对既不是人口也不对空间异质性的敏感性也不敏感。我们的结果还提供了有关测试的区域优先级的见解以及区域之间流行病潜在同步的后果。
Spatiotemporal modelling of infectious diseases such as COVID-19 involves using a variety of epidemiological metrics such as regional proportion of cases or regional positivity rates. Although observing their changes over time is critical to estimate the regional disease burden, the dynamical properties of these measures as well as cross-relationships are not systematically explained. Here we provide a spatiotemporal framework composed of six commonly used and newly constructed epidemiological metrics and conduct a case study evaluation. We introduce a refined risk model that is biased neither by the differences in population sizes nor by the spatial heterogeneity of testing. In particular, the proposed methodology is useful for the unbiased identification of time periods with elevated COVID-19 risk, without sensitivity to spatial heterogeneity of neither population nor testing. Our results also provide insights regarding regional prioritization of testing and the consequences of potential synchronization of epidemics between regions.
