The number of positive cases confirmed in the viral tests is a probe of the actual number of infections of COVID-19. The bias between these two quantities is a key element underlying the determination of some important parameters of this disease and the policy-making during the pandemic. To study the dependence of this bias on measured variables, we introduce a parameterization model that motivates a method of organizing the daily data of the numbers of the total tests, confirmed cases, hospitalizations and fatalities. After comparing with the historical data of the USA in the past few months, we find a simple formula relating these four variables. As a few applications, we show, among other things, how this formula can be used to project the number of actual infections, to provide guidance on how the test volume should be adjusted, and to derive an upper bound on the overall infection fatality rate of COVID-19 ([â¤]0.64%, 95% C.L.) and a theoretical estimate of its value.
Understanding the Bias between the Number of Confirmed Cases and Actual Number of Infections in the COVID-19 Pandemic