Government officials and policymakers have tried to use numbers to grasp COVID-19’s impact. Figures like the number of hospitalizations or deaths reflect part of this burden. Each datapoint tells only part of the story. But no one figure describes the true pervasiveness of the novel coronavirus by revealing the number of people actually infected at a given time – an important figure to help scientists understand if herd immunity can be reached, even with vaccinations.
Now, two University of Washington scientists have developed a statistical framework that incorporates key COVID-19 data – such as case counts and deaths due to COVID-19 – to model the true prevalence of this disease in the United States and individual states. Their approach, published the week of July 26 in the Proceedings of the National Academy of Sciences, projects that in the U.S. as many as 60% of COVID-19 cases went undetected as of March 7, 2021, the last date for which the dataset they employed is available.
This framework could help officials determine the true burden of disease in their region – both diagnosed and undiagnosed – and direct resources accordingly, said the researchers.