An Epidemiological Modelling Approach for Covid19 via Data Assimilation
This work addresses the need for scalable and adaptable forecasting models to inform policy decisions during the COVID-19 pandemic, but it appears incremental as it builds on existing compartmental models with data assimilation techniques.
The authors tackled the problem of forecasting COVID-19 and evaluating quarantine policies by developing a custom SITR epidemiological model that incorporates real-time data through variational data assimilation, applied to China, the US, and Italy, but no concrete numerical results are provided.
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.