In a new study, researchers found the United States may have experienced more than 6.4 million cases of COVID-19 by April 18, 2020.
That is nine-time more than the number of confirmed cases in the same period, which was 721,245.
According to them, the majority of this discrepancy was due to under-testing.
The findings highlight the urgent need for more COVID-19 testing—including testing of asymptomatic individuals exposed to COVID-19—to help stop the spread of the virus.
The research was conducted by a team at UC Berkeley School of Public Health.
The first known case of COVID-19 in the US was confirmed on January 21, 2020.
For the first few months of the pandemic, the US Centers for Disease Control recommended that testing be prioritized for patients in the hospital who presented moderate or severe symptoms.
However, studies suggest that 30–70% of individuals who test positive for the virus present with mild symptoms or may have none at all.
In the study, the team estimated the total number of COVID-19 infections in each US state from February 28 to April 18, 2020, using a probabilistic bias analysis to account for incomplete testing and less than 100% test accuracy.
The authors estimate that there were 6,454,951 cases of COVID-19 infection (19 per 1,000 people).
This estimate is about 9 times larger than the number of confirmed cases during the same period (2 per 1,000 people) and suggests that 89% of infections were undocumented.
The majority of this difference (approximately 86%) was due to incomplete testing, with the remainder due to limited test accuracy.
The team found that COVID-19 incidence was highest in the Northeast, Midwest, and the state of Louisiana when using confirmed case counts or the estimated number of infections.
Underestimation of the number of cases was more common in Puerto Rico, California, and some southern states.
In 33 states, the estimated number of infections was at least 10 times higher than the number of confirmed cases.
The researchers note that their methodology does not incorporate a transmission model and so they are unable to make forecasts about the spread of the virus.
However, they argue that their method provides a more realistic picture of infection burden at a given point in time.
One author of the study is Jade Benjamin-Chung, a professor of epidemiology & biostatistics at Berkeley Public Health.
The study is published in Nature Communications.
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