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Identifying the measurements required to estimate rates of COVID-19 transmission, infection, and detection, using variational data assimilation

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Infectious Disease Modelling 2021年第1期6卷 133-147页

作者： Eve Armstrong manuela runge Jaline Gerardin Department of Physics New York Institute of Technology1855 BroadwayNew YorkNY10023USA Department of Astrophysics American Museum of Natural HistoryNew YorkNY10024USA Department of Preventive Medicine Northwestern University710 N Lake Shore Drive Suite 800ChicagoIL60611USA

We demonstrate the ability of statistical data assimilation(SDA)to identify the measurements required for accurate state and parameter estimation in an epidemiological model for the novel coronavirus disease *** context is an effort to inform policy regarding social behavior,to mitigate strain on hospital *** model unknowns are taken to be:the time-varying transmission rate,the fraction of exposed cases that require hospitalization,and the time-varying detection probabilities of new asymptomatic and symptomatic *** simulations,we obtain estimates of undetected(that is,unmeasured)infectious populations,by measuring the detected cases together with the recovered and dead-and without assumed knowledge of the detection *** a noiseless measurement of the recovered population,excellent estimates of all quantities are obtained using a temporal baseline of 101 days,with the exception of the time-varying transmission rate at times prior to the implementation of social *** low noise added to the recovered population,accurate state estimates require a lengthening of the temporal baseline of *** of all parameters are sensitive to the contamination,highlighting the need for accurate and uniform methods of *** aim of this paper is to exemplify the power of SDA to determine what properties of measurements will yield estimates of unknown parameters to a desired precision,in a model with the complexity required to capture important features of the COVID-19 pandemic.

关键词： COVID-19 Epidemiology Inference Measurement noise

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