论文标题
通过数据同化，COVID19的流行病学建模方法
An Epidemiological Modelling Approach for Covid19 via Data Assimilation
论文作者
论文摘要
2019-NCOV的全球大流行要求评估政策干预措施，以减轻全球隔离措施的未来社会和经济成本。我们提出了一个用于预测和政策评估的流行病学模型，该模型通过变异数据同化实时合并了新数据。我们分析和讨论中国，美国和意大利的感染率。特别是，我们开发了一个自定义的隔室SIR模型，适合于与中国城市（名为SITR模型）相关的变量。我们比较并讨论随着新观察结果的可进行更新的模型结果。采用混合数据同化方法来使结果适合初始条件。我们使用该模型来推断感染数量以及诸如疾病的传播率或恢复率之类的参数。该模型的参数化是简约且可扩展的，从而允许掺入其他有趣的数据和参数。这允许可扩展性并将模型扩展到其他位置或新型数据源的适应性。
The global pandemic of the 2019-nCov requires the evaluation of policy interventions to mitigate future social and economic costs of quarantine measures worldwide. We propose an epidemiological model for forecasting and policy evaluation which incorporates new data in real-time through variational data assimilation. We analyze and discuss infection rates in China, the US and Italy. In particular, we develop a custom compartmental SIR model fit to variables related to the epidemic in Chinese cities, named SITR model. We compare and discuss model results which conducts updates as new observations become available. A hybrid data assimilation approach is applied to make results robust to initial conditions. We use the model to do inference on infection numbers as well as parameters such as the disease transmissibility rate or the rate of recovery. The parameterisation of the model is parsimonious and extendable, allowing for the incorporation of additional data and parameters of interest. This allows for scalability and the extension of the model to other locations or the adaption of novel data sources.
