Scientists Model “True Prevalence” of COVID-19 in the United States Throughout Pandemic

The death rate from COVID-19 infection for US states and Washington, DC, as of March 7, 2021. The figures are the rear median. Credit: Rebecca Gourley/University of Washington

Government officials and policymakers have tried to use numbers to understand the impact of COVID-19. Figures such as the number of hospitalizations or deaths reflect some of this burden. Each data point only tells part of the story. But no figure describes the true ubiquity of the new coronavirus by revealing the number of people actually infected at any given time — an important figure to help scientists understand whether herd immunity can be achieved even with vaccinations.

Now, two scientists from the University of Washington have developed a statistical framework that includes key COVID-19 data — such as the number of cases and deaths from COVID-19 — to measure the true prevalence of this disease in the United States and individual states. to model. Their approach, published the week of July 26 in the Proceedings of the National Academy of Sciences, predicts that in the US, as many as 60% of COVID-19 cases went undetected as of March 7, 2021, the last date for which the data set it. employ is available.

This framework could help officials determine the true burden of disease in their region — both diagnosed and undiagnosed — and direct resources accordingly, the researchers said.

The cumulative incidence of COVID-19 for US states and Washington, DC, as of March 7, 2021. Figures are the rear median. Credit: Rebecca Gourley/University of Washington

“There are all kinds 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 data source has its own flaws that would distort what’s really going on,” said senior author Adrian Raftery, professor of sociology and statistics at the UW. “What we wanted to do is develop a framework that corrects the flaws in multiple data sources and leverages their strengths to give us an idea of ​​the prevalence of COVID-19 in a region, a state, or the country as a whole.”

Data sources can be biased in several ways. For example, a much-cited COVID-19 statistic is the proportion of test results in a region or state that come back positive. But since access to testing and willingness to be tested vary by location, that figure alone cannot provide a clear picture of the prevalence of COVID-19, Raftery said.

Other statistical methods often attempt to correct the bias in one data source to model the true prevalence of disease in a region. For their approach, Raftery and lead author Nicholas Irons, a UW doctoral student in statistics, took into account three factors: the number of confirmed COVID-19 cases, the number of deaths from COVID-19, and the number of COVID-19 tests each taken. day as reported by the COVID Tracking Project. In addition, they included results of random COVID-19 tests from residents of Indiana and Ohio as an “anchor” for their method.

The COVID-19 undercount factors for US states and Washington, DC, as of March 7, 2021. Figures are the back median. Credit: Rebecca Gourley/University of Washington

The researchers used their framework to model the prevalence of COVID-19 in the US and each of the states through March 7, 2021. By that date, according to their framework, an estimated 19.7% of US residents, or about 65 million people, had been infected. This indicates that the US is unlikely to achieve herd immunity without the ongoing vaccination campaign, Raftery and Irons said. In addition, the US had an undercount factor of 2.3, the researchers found, meaning only about 1 in 2.3 cases of COVID-19 were confirmed through testing. In other words, about 60% of the cases were not counted at all.

This undercount of COVID-19 also varied widely by state and could have multiple causes, according to Irons.

“It may depend on the severity of the pandemic and the amount of testing in that state,” Irons said. “If you have a state with a severe pandemic but limited testing, the undercount can be very high and you miss the vast majority of infections that occur. Or you may have a situation where testing is rife and the pandemic isn’t as severe. The undercount would be lower there.”

In addition, the undercount factor fluctuated by state or region as the pandemic progressed due to differences in access to medical care between regions, changes in test availability and other factors, Raftery said.

Using the true prevalence of COVID-19, Raftery and Irons calculated other useful figures for states, such as the death rate from the infection, the percentage of infected people who had succumbed to COVID-19, as well as the cumulative incidence, which is the percentage of the population of a state that has had COVID-19.

Ideally, regular random tests of individuals would show the level of infection in a state, region or even nationally, Raftery said. But during the COVID-19 pandemic, only Indiana and Ohio conducted random viral tests on residents, data sets that were critical in helping the researchers develop their framework. In the absence of widespread randomized testing, this new method could help officials assess the true burden of disease in this pandemic and the next.

“We think this tool can make a difference by giving the people responsible a more accurate picture of how many people are infected and what proportion of them are being missed by current testing and treatment efforts,” Raftery said.

LIST (below) of death rates from COVID-19 infection, cumulative incidence, and underestimation factors (respectively) for US states and Washington, DC, as of March 7, 2021. Figures are rear median, with 95% confidence intervals indicated in parentheses . Credit: Irons and Raftery, PNAS, 2021.

State/Territory: Infection Death Rate, Cumulative Incidence, Undercount Factor

Alabama: 0.90% (0.70-1.15), 24.0% (18.8-30.6), 2.4 (1.8-3.0)

Alaska: 0.35% (0.28-0.43), 11.8% (10.1-14.2), 1.5 (1.3-1.8)

Arizona: 0.93% (0.75-1.14), 24.5% (20.1-30.3), 2.2 (1.8-2.7)

Arkansas: 0.78% (0.64-0.95), 23.2% (19.2-28.4), 2.2 (1.8-2.7)

California: 0.88% (0.70-1.09), 16.9% (13.6-20.9), 1.9 (1.6-2.4)

Colorado: 0.60% (0.49-0.71), 17.2% (14.6-20.9), 2.3 (2.0-2.8)

Connecticut: 1.37% (1.10-1.70) 15.9% (12.9-19.9) 2.0 (1.6-2.5)

Delaware: 0.92% (0.74-1.13), 17.0% (14.0-21.0), 1.9 (1.6-2.3)

Florida: 0.78% (0.64-0.94), 19.5% (16.3-23.8), 2.2 (1.9-2.8)

Georgia: 0.67% (0.54-0.82), 25.9% (21.4-32.0), 2.7 (2.2-3.4)

Hawaii: 0.70% (0.54-0.91), 4.6% (3.6-5.8), 2.3 (1.8-2.9)

Idaho: 0.28% (0.23-0.33), 37.8% (32.1-45.9), 4.0 (3.4-4.8)

Illinois: 0.96% (0.78-1.16), 19.3% (16.1-23.7), 2.0 (1.7-2.5)

Indiana: 0.84% ​​(0.70-1.00), 22.9% (19.2-27.6), 2.3 (1.9-2.8)

Iowa: 0.76% (0.61-0.97), 23.6% (18.7-29.5), 2.6 (2.1-3.3)

Kansas: 0.59% (0.47-0.75), 28.6% (22.6-36.1), 2.8 (2.2-3.5)

Kentucky: 0.53% (0.43-0.63), 21.4% (18.0-25.8), 2.3 (2.0-2.8)

Louisiana: 1.01% (0.82-1.22), 21.1% (17.5-26.1), 2.3 (1.9-2.8)

Maine: 0.73% (0.60-0.87), 7.3% (6.2-8.8), 2.1 (1.8-2.6)

Maryland: 0.94% (0.77-1.15), 13.9% (11.5-17.1), 2.2 (1.8-2.7)

Massachusetts: 1.71% (1.36–2.13), 14.4% (11.6–18.0), 1.7 (1.4–2.1)

Michigan: 1.00% (0.77-1.29), 17.1% (13.3-22.1), 2.6 (2.0-3.4)

Minnesota: 0.64% (0.52-0.76), 18.4% (15.4-22.4), 2.1 (1.8-2.6)

Mississippi: 0.74% (0.61-0.89), 31.6% (26.6-38.6), 3.2 (2.7-3.9)

Missouri: 0.69% (0.55-0.87), 19.6% (15.8-24.7), 2.5 (2.0-3.2)

Montana: 0.62% (0.50-0.75), 21.0% (17.5-25.5), 2.3 (1.9-2.8)

Nebraska: 0.53% (0.43-0.64), 21.1% (17.7-25.7), 2.0 (1.7-2.5)

Nevada: 0.73% (0.59-0.87), 22.5% (18.8-27.5), 2.4 (2.0-2.9)

New Hampshire: 0.66% (0.54-0.79), 13.4% (11.3-16.2), 2.4 (2.0-2.9)

New Jersey: 1.22% (0.97-1.53), 22.2% (17.7-28.0), 2.4 (1.9-3.1)

New Mexico: 1.01% (0.83-1.21) 18.6% (15.6-22.7) 2.1 (1.8-2.5)

New York: 1.12% (0.87-1.42), 18.6% (14.7-23.9), 2.1 (1.7-2.8)

North Carolina: 0.58% (0.48-0.68), 19.4% (16.5-23.4), 2.4 (2.0-2.8)

North Dakota: 0.86% (0.71-1.03), 22.4% (19.0-27.1), 1.7 (1.4-2.0)

Ohio: 0.83% (0.68-1.03), 19.5% (15.9-23.7), 2.3 (1.9-2.9)

Oklahoma: 0.47% (0.38-0.56), 25.1% (21.1-30.6), 2.3 (1.9-2.8)

Oregon: 0.69% (0.55-0.85), 8.2% (6.7-10.0), 2.2 (1.8-2.8)

Pennsylvania: 1.00% (0.82-1.19), 19.4% (16.4-23.7), 2.6 (2.2-3.2)

Rhode Island: 1.41% (1.14-1.72), 17.4% (14.3-21.4), 1.4 (1.2-1.8)

South Carolina: 0.76% (0.63-0.91), 23.0% (19.4-28.0), 2.3 (1.9-2.8)

South Dakota: 0.68% (0.56-0.82), 31.2% (26.1-37.9), 2.5 (2.1-3.0)

Tennessee: 0.79% (0.64-0.97), 21.8% (17.7-26.9), 1.9 (1.6-2.4)

Texas: 0.70% (0.57-0.85), 22.7% (18.8-27.8), 2.5 (2.1-3.0)

Utah: 0.22% (0.18-0.26), 28.0% (23.8-33.7), 2.5 (2.1-3.0)

Vermont: 0.72% (0.57-0.92), 4.6% (3.8-5.6), 1.8 (1.5-2.2)

Virginia: 1.53% (1.09-2.36), 8.3% (5.5-11.7), 1.2 (0.8-1.7)

Washington: 0.51% (0.41-0.64), 12.9% (10.3-16.1), 2.9 (2.3-3.6)

West Virginia: 0.86% (0.71-1.01), 15.4% (13.2-18.5), 2.0 (1.8-2.5)

Wisconsin: 0.57% (0.46-0.69), 21.5% (17.9-26.6), 2.0 (1.7-2.5)

Wyoming: 0.58% (0.46-0.71), 21.0% (17.3-25.9), 2.2 (1.8-2.7)

Washington, DC: 1.19% (0.94-1.49), 12.2% (9.8-15.3), 2.1 (1.7-2.7)

Reference: “Estimating SARS-CoV-2 Infections by Deaths, Confirmed Cases, Tests, and Random Investigations” by Nicholas J. Irons and Adrian E. Raftery, July 26, 2021, Proceedings of the National Academy of Sciences.
DOI: 10.1073/pnas.2103272118

The research was funded by the National Institutes of Health.