Scientists model 'true prevalence' of COVID-19 throughout pandemic


Date:
July 26, 2021
Source:
University of Washington
Summary:
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 projects that in the
U.S. as many as 60 percent of COVID-19 cases went undetected as of
March 7, 2021, the last date for which the dataset they employed
is available.



FULL STORY ========================================================================== 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.

"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 UW professor of sociology
and of statistics. "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." Data sources can be biased in different ways. For example, one widely cited COVID-19 statistic is the proportion of test
results in a region or state that come back positive. But since access to tests, and a willingness to be tested, vary by location, that figure alone cannot provide a clear picture of COVID- 19's prevalence, said Raftery.

Other statistical methods often try 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, incorporated three factors: the number of confirmed
COVID-19 cases, the number of deaths due to COVID-19 and the number of
COVID-19 tests administered each day as reported by the COVID Tracking
Project. In addition, they incorporated results from random COVID-19
testing of Indiana and Ohio residents as an "anchor" for their method.



==========================================================================
The researchers used their framework to model COVID-19 prevalence in
the U.S.

and each of the states up through March 7, 2021. On that date, according
to their framework, an estimated 19.7% of U.S. residents, or about
65 million people, had been infected. This indicates that the U.S. is
unlikely to reach herd immunity without its ongoing vaccination campaign, Raftery and Irons said.

In addition, the U.S. had an undercount factor of 2.3, the researchers
found, which means that only about 1 in 2.3 COVID-19 cases were being
confirmed through testing. Put another way, some 60% of cases were not
counted at all.

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

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

With the true prevalence of COVID-19, Raftery and Irons calculated other
useful figures for states, such as the infection fatality rate, which
is the percentage of infected people who had succumbed to COVID-19, as
well as the cumulative incidence, which is the percentage of a state's population who have had COVID-19.

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

"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,"
said Raftery.

The research was funded by the National Institutes of Health.

========================================================================== Story Source: Materials provided by University_of_Washington. Original
written by James Urton. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Nicholas J. Irons, Adrian E. Raftery. Estimating SARS-CoV-2
infections
from deaths, confirmed cases, tests, and random surveys. Proceedings
of the National Academy of Sciences, 2021; 118 (31): e2103272118
DOI: 10.1073/pnas.2103272118 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/07/210726152855.htm

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