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.
Highlights
The global outbreak of n-Cov2019 and the possibility of severe social and economic costs worldwide requires immediate action on suppresion measures
We find that compared to static models, updating the parameters in a dynamic fashion leads to an upward correction of the true number of infected people as well as reducing forecasting errors
We combined compartmental models in epidemiology with data assimilation schemes showing the advantage of real-time forecasting and parameter estimation in the current crisis
Summary
The global outbreak of n-Cov2019 and the possibility of severe social and economic costs worldwide requires immediate action on suppresion measures. 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. We find that compared to static models, updating the parameters in a dynamic fashion leads to an upward correction of the true number of infected people as well as reducing forecasting errors. We estimate both short term and long term dynamics in Italy, the United States and the United Kingdom, finding a peak of infections in the middle of March and a flattened but sustained number of infected cases in the United States and the United Kingdom. Section six states discusses empirical results and section seven concludes
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