Journal Article

An epidemiological modelling approach for COVID-19 via data assimilation

Wang, Shuo, Arcucci, Rossella, Yang, Xian, Nadler, Philip, Guo, Yike

2020

Overview

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 the UK, US and Italy. We furthermore develop a custom compartmental SIR model fit to variables related to the available data of the pandemic, named SITR model, which allows for more granular inference on infection numbers. 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 and measurement errors in the data. We use the model to conduct 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.

• Publisher: Springer Netherlands,Springer Nature B.V
• Subject: Bayes Theorem / Betacoronavirus / Cardiology / Computer Simulation / Coronavirus Infections - epidemiology / Coronaviruses / COVID-19 / Data assimilation / Data collection / Disease Outbreaks / Economic conditions / Economic impact / Economic models / Epidemic models / Epidemiology / Evaluation / Forecasting / Humans / Infections / Infectious Diseases / Inference / Initial conditions / Italy - epidemiology / Mathematical models / Medicine / Medicine & Public Health / Models, Biological / Models, Statistical / Oncology / Pandemics / Parameterization / Parameters / Pneumonia, Viral - epidemiology / Public Health / Public Health Informatics - methods / Quarantine / SARS-CoV-2 / United Kingdom - epidemiology / United States - epidemiology