(CN) — Two University of Washington scientists have created a new statistical method to help calculate the true number of Covid-19 cases in the U.S. and in each individual state.
Their study published Monday in the journal PNAS, short for Proceedings of the National Academy of Sciences, shows that as of the first week of March, as many as 60% of coronavirus cases went undetected in some parts of the country.
The scientists arrived at this number by using data to model the prevalence of the respiratory disease, or the proportion of a particular population that is affected by Covid-19.
The statistical model may could help government and health officials calculate the full effect of Covid-19 in their region.
“There are all sorts of different data sources we can draw on to understand the Covid-19 pandemic -- the number of hospitalizations in a state, or the number of tests that come back positive. But each source of data has its own flaws that would give a biased picture of what's really going on," said senior author Adrian Raftery, a University of Washington professor of sociology and of statistics.
He added, "What we wanted to do is to develop a framework that corrects the flaws in multiple data sources and draws on their strengths to give us an idea of Covid-19's prevalence in a region, a state or the country as a whole."
Rafferty pointed out that even a data point like the proportion of Covid-19 tests from a specific region that are positive is influenced by the region’s access to tests and the willingness of people in that area to be tested.
To correct for these biases, Rafferty and lead author Nicholas Irons, a University of Washington doctoral student in statistics, incorporated several different factors into their method.
Frist, they used the confirmed number of Covid-19 cases in a region, total deaths and the number of Covid-19 tests that were given each day as reported by the Covid Tracking Project.
The final piece of the statistical model involved using data from Ohio and Indiana, which conducted random viral testing of its residents. This type of data is important because by testing a random sample of all residents, researchers can gain a better picture of the true infection rate.
"We think this tool can make a difference by giving the people in charge a more accurate picture of how many people are infected, and what fraction of them are being missed by current testing and treatment efforts," Raftery said.
According to the model, by March 7, 2021, an estimated 19.7% of U.S. residents had been infected with Covid-19. Rafferty and Irons said that given that number, it's unlikely the U.S. could reach herd immunity without its ongoing vaccination campaign.
The scientists were also able to calculate that the U.S. as a whole had a Covid-19 undercount factor of 2.3, which means that only about 1 in 2.3 Covid-19 cases were being confirmed by administered tests.
"It can depend on the severity of the pandemic and the amount of testing in that state," said Irons. "If you have a state with severe pandemic but limited testing, the undercount can be very high, and you're missing the vast majority of infections that are occurring. Or, you could have a situation where testing is widespread and the pandemic is not as severe. There, the undercount rate would be lower."
The model was also able to calculate other statistics such as the fatality rate of people who were infected with Covid-19, and the percentage of a state’s population who have or had the virus.
Raftery and Iron’s research was funded by the National Institutes of Health, and an online dashboard containing their information and results can be found here.
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