An epidemiological modelling approach for COVID-19 via data assimilation
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COVID-19
An epidemiological modelling approach for COVID-19 via data assimilation Philip Nadler1 · Shuo Wang1 · Rossella Arcucci1 · Xian Yang1 · Yike Guo1 Received: 2 May 2020 / Accepted: 10 August 2020 / Published online: 4 September 2020 © The Author(s) 2020
Abstract 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. Keywords Data assimilation · 2019-nCov · Inference · Bayesian updating · Compartmental model
Introduction The global outbreak of n-Cov2019 and the possibility of severe social and economic costs worldwide requires immediate action on suppresion measures. In order to evaluate the efficacy of past and future policy measures to fight and contain the spread of n-Cov2019, a robust and quantifiable analysis system is required. We propose a methodology for forecasting the spread of n-Cov2019 and show how to estimate latent infection rates, accounting for high uncertainty in observation and model specification, which is done by * Philip Nadler [email protected] * Yike Guo [email protected] Shuo Wang [email protected] Rossella Arcucci [email protected] Xian Yang [email protected] 1
Data Science Institute, Imperial College London, London SW7 2AZ, UK
combining real-time Bayesian updating with epidemiological models. To show the generalisability of our updating approach we first embed a standard SIR model in our framework and then develop a custom compartmental SIR model which is fit to data related to the spread of the coronavirus worldwide which we name SITR. The SITR model adds an additional compartment for patients under treatment T and allows for more granular inference on the underlying dynamics of the epidemic, separating confirmed cases under treatment with latent unconfirmed cases of Covid19. The models are embedded in a data assimilation framework, a form of recursive Bayesian estimation [1], which conducts model upda
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