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Newswise – DALLAS – February 8, 2021 – Global health experts have long suspected that the incidence of COVID-19 is higher than what has been reported. Now, a machine learning algorithm developed at UT Southwestern estimates that the number of COVID-19 cases in the United States since the start of the pandemic is nearly three times that of confirmed cases.
The algorithm, described in a study published today in PLOS ONE, provides daily updated estimates of the total number of infections to date as well as the number of people currently infected in the United States and the 50 countries hardest hit by the pandemic.
As of February 4, according to the model’s calculations, more than 71 million people in the United States – 21.5% of Americans – have contracted COVID-19. This compares to the considerably smaller number of 26.7 million publicly reported confirmed cases, says Jungsik Noh, Ph.D., assistant professor at UT Southwestern in the Lyda Hill Department of Bioinformatics and the study’s first author.
Of the 71 million Americans estimated to have had COVID-19, 7 million (2.1% of the U.S. population) had current infections and were potentially contagious as of February 4, according to the algorithm.
Noh’s written study is based on calculations made in September. At that time, he reports, the number of actual cumulative cases in 25 of the 50 hardest-hit countries was five to 20 times the number of confirmed cases then suggested.
Looking at the information currently available on the algorithm online, the estimates are now closer to the reported numbers – but still much higher. As of February 4, Brazil had more than 36 million cumulative cases according to the algorithm’s estimates, almost four times more than the 9.4 million confirmed cases reported. France had 14 million against 3.2 million declared. And the UK had nearly 25 million instead of around 4 million – over six times as many. Mexico, an outlier, had nearly 15 times the number of reported cases – 27.6 million instead of 1.9 million confirmed cases.
“Estimates of actual infections reveal for the first time the true severity of COVID-19 in the United States and countries around the world,” Noh says.
The algorithm uses the number of reported deaths – considered more accurate and complete than the number of laboratory-confirmed cases – as the basis for its calculations. It then assumes an infection death rate of 0.66%, based on an earlier study of the pandemic in China, and takes into account other factors such as the average number of days between symptom onset and death. or recovery. It also compares its estimate to the number of confirmed cases to calculate a ratio of confirmed / estimated infections.
COVID-19 remains uncertain – especially the death rate – and so estimates are rough, Noh says. But he believes the model’s estimates are more precise and exclude fewer cases than the confirmed cases currently used as a guide for public health policies. It is important to have a more complete estimate of the prevalence of the disease, adds Noh.
“These are critical statistics on the severity of COVID-19 in each region. Knowing the actual severity in different regions will help us effectively fight the spread of the virus, ”he explains. “The currently infected population is the cause of future infections and deaths. Its actual size in a region is a critical variable required to determine the severity of COVID-19 and strategize against regional outbreaks. “
In the United States, infection rates vary widely from state to state. California has recorded nearly 7 million infections since the start of the pandemic, compared to 5.7 million for New York, according to the algorithm’s projections for February 4. .
Other model estimates for Feb. 4: In Pennsylvania, 11.2 percent of the population had current infections – the highest rate of any state, compared with a low of 0.15 percent of those living in Minnesota ; in New York, the first hot spot, 528,000 people had active infections, or about 2.7% of its population. Meanwhile, in Texas, 2.3% had current infections.
Noh says he developed the algorithm last summer while trying to decide whether to send his sixth-grade daughter back to school in person. There was nowhere to find the data he needed to assess the safety of doing so, he said.
Once he built the machine’s algorithm, he discovered that the region he lived in had a current infection rate of around 1%. So her daughter went to school.
Noh verified his results by comparing his results with existing prevalence rates found in several studies that used blood tests to look for antibodies against the SARS-CoV-2 virus, which causes COVID-19. For most of the areas tested, his algorithm’s estimates of infections closely matched the percentage of people who had tested positive for antibodies, according to the PLOS ONE study.
The online model uses COVID-19 death data from Johns Hopkins University and the COVID Tracking Project, a volunteer organization created to help track COVID-19, to run its daily updates. However, the estimates published in the PLOS ONE The study dates from September 3. By that time, about 10% of the U.S. population had been infected with COVID-19, according to Noh’s algorithm.
Gaudenz Danuser, Ph.D., chair of Lyda Hill’s department of bioinformatics and professor of cell biology, was the lead author of the study. He also holds the Patrick E. Haggerty Chair of Excellence in Basic Biomedical Sciences.
Funding came from Lyda Hill Philanthropies.
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