2020/05/25 by Eve Armstrong, Armstrong, Eve, Manuela Runge +3
Mathematics · Medicine · #COVID-19 epidemiological studies #Influenza Virus Research Studies #Mathematical and Theoretical Epidemiology and Ecology Models
paper · pdf · doi:10.48550/arxiv.2005.12441
We demonstrate the ability of statistical data assimilation to identify the\nmeasurements required for accurate state and parameter estimation in an\nepidemiological model for the novel coronavirus disease COVID-19. Our context\nis an effort to inform policy regarding social behavior, to mitigate strain on\nhospital capacity. The model unknowns are taken to be: the time-varying\ntransmission rate, the fraction of exposed cases that require hospitalization,\nand the time-varying detection probabilities of new asymptomatic and\nsymptomatic cases. In simulations, we obtain accurate estimates of undetected\n(that is, unmeasured) infectious populations, by measuring the detected cases\ntogether with the recovered and dead - and without assumed knowledge of the\ndetection rates. Given a noiseless measurement of the recovered population,\nexcellent estimates of all quantities are obtained using a temporal baseline of\n101 days, with the exception of the time-varying transmission rate at times\nprior to the implementation of social distancing. With low noise added to the\nrecovered population, accurate state estimates require a lengthening of the\ntemporal baseline of measurements. Estimates of all parameters are sensitive to\nthe contamination, highlighting the need for accurate and uniform methods of\nreporting. The aim of this paper is to exemplify the power of SDA to determine\nwhat properties of measurements will yield estimates of unknown parameters to a\ndesired precision, in a model with the complexity required to capture important\nfeatures of the COVID-19 pandemic.\n