Initial COVID-19 infection rate may be 80 times greater than originally reported

Date:
June 22, 2020
Source:
Penn State
Summary:
A new study estimates that the number of early COVID-19 cases in
the U.S.

may have been more than 80 times greater and doubled nearly twice
as fast as originally believed.



FULL STORY ==========================================================================
Many epidemiologists believe that the initial COVID-19 infection rate
was undercounted due to testing issues, asymptomatic and alternatively symptomatic individuals, and a failure to identify early cases.


==========================================================================
Now, a new study from Penn State estimates that the number of early
COVID-19 cases in the U.S. may have been more than 80 times greater and
doubled nearly twice as fast as originally believed.

In a paper published today (June 22) in the journal Science Translational Medicine, researchers estimated the detection rate of symptomatic
COVID-19 cases using the Centers for Disease Control and Prevention's influenza-like illnesses (ILI) surveillance data over a three week period
in March 2020.

"We analyzed each state's ILI cases to estimate the number that
could not be attributed to influenza and were in excess of seasonal
baseline levels," said Justin Silverman, assistant professor in Penn
State's College of Information Sciences and Technology and Department
of Medicine. "When you subtract these out, you're left with what we're
calling excess ILI -- cases that can't be explained by either influenza or
the typical seasonal variation of respiratory pathogens." The researchers found that the excess ILI showed a nearly perfect correlation with the
spread of COVID-19 around the country.

Said Silverman, "This suggests that ILI data is capturing COVID cases,
and there appears to be a much greater undiagnosed population than
originally thought." Remarkably, the size of the observed surge of
excess ILI corresponds to more than 8.7 million new cases during the
last three weeks of March, compared to the roughly 100,000 cases that
were officially reported during the same time period.



==========================================================================
"At first I couldn't believe our estimates were correct," said
Silverman. "But we realized that deaths across the U.S. had been
doubling every three days and that our estimate of the infection rate
was consistent with three-day doubling since the first observed case was reported in Washington state on January 15." The researchers also used
this process to estimate infection rates for each state, noting that
states showing higher per capita rates of infection also had higher per
capita rates of a surge in excess ILI. Their estimates showed rates much
higher than initially reported but closer to those found once states
began completing antibody testing.

In New York, for example, the researchers' model suggested that at
least 9% of the state's entire population was infected by the end of
March. After the state conducted antibody testing on 3,000 residents,
they found a 13.9% infection rate, or 2.7 million New Yorkers.

Excess ILI appears to have peaked in mid-March as, the researchers
suggest, fewer patients with mild symptoms sought care and states
implemented interventions which led to lower transmission rates. Nearly
half of U.S. states were under stay-at-home orders by March 28.

The findings suggest an alternative way of thinking about the COVID-19 pandemic.

"Our results suggest that the overwhelming effects of COVID-19 may have
less to do with the virus' lethality and more to do with how quickly it
was able to spread through communities initially," Silverman explained. "A lower fatality rate coupled with a higher prevalence of disease and rapid growth of regional epidemics provides an alternative explanation of the
large number of deaths and overcrowding of hospitals we have seen in
certain areas of the world." Other collaborators on the project included Nathaniel Hupert of Cornell University and the New York-Presbyterian
Hospital, and Alex Washburne of Montana State University.


========================================================================== Story Source: Materials provided by Penn_State. Original written by
Jordan Ford. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Justin D. Silverman, Nathaniel Hupert, Alex D. Washburne. Using
influenza
surveillance networks to estimate state-specific prevalence of
SARS-CoV- 2 in the United States. Science Translational Medicine,
June 22, 2020; DOI: 10.1126/science.abc1126 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/06/200622141143.htm

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