An epidemiological modelling approach for COVID-19 via data assimilation

• DOI: 10.1007/s10654-020-00676-7
• Publication Date: 2020-09-04T19:02:32Z
• Authors: Philip Nadler; Shuo Wang; Rossella Arcucci; Xian Yang; Yike Guo
• Keywords: Pandemic; Epidemic model

Abstract The global pandemic of the 2019-nCov requires evaluation policy interventions to mitigate future social and economic costs quarantine measures worldwide. We propose an epidemiological model for forecasting which incorporates new data in real-time through variational assimilation. analyze discuss infection rates UK, US Italy. furthermore develop a custom compartmental SIR fit variables related available pandemic, named SITR model, allows more granular inference on numbers. compare results conducts updates as observations become available. A hybrid assimilation approach is applied make robust initial conditions measurement errors data. use conduct numbers well parameters such disease transmissibility rate or recovery. parameterisation parsimonious extendable, allowing incorporation additional interest. This scalability extension other locations adaption novel sources.