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

TL;DR

This paper demonstrates how statistical data assimilation can identify the necessary measurements to accurately estimate COVID-19 transmission, infection, and detection rates, informing policy decisions and improving epidemic modeling.

Contribution

The study shows the effectiveness of variational data assimilation in determining measurement requirements for accurate COVID-19 epidemiological modeling without prior knowledge of detection rates.

Findings

Accurate estimates of unmeasured infectious populations are possible with certain measurements.

Longer measurement periods improve estimates when noise is present.

Estimates are sensitive to data contamination, emphasizing the need for reliable reporting.

Abstract

We demonstrate the ability of statistical data assimilation to identify the measurements required for accurate state and parameter estimation in an epidemiological model for the novel coronavirus disease COVID-19. Our context is an effort to inform policy regarding social behavior, to mitigate strain on hospital capacity. The 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 cases. In simulations, we obtain accurate 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 rates. Given 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 distancing. With low noise added to the recovered population, accurate state estimates require a lengthening of the temporal baseline of measurements. Estimates of all parameters are sensitive to the contamination, highlighting the need for accurate and uniform methods of reporting. The 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.