Newswise – DALLAS – February 8, 2021 – World health experts have long suspected that the incidence of COVID-19 has been higher than reported. Now, a machine learning algorithm developed at UT Southwestern estimates that the number of COVID-19 cases in the U.S. since the pandemic began is nearly three times as many confirmed cases.
The algorithm, described in a study published today in PLOS ONE, provides daily updated estimates of total infections to date, as well as how many people are currently infected in the United States and the 50 countries most affected by the pandemic.
As of Feb. 4, according to model estimates, more than 71 million people in the U.S. (21.5% of Americans) had contracted COVID-19. This compares with the number of publicly confirmed cases of 26.7 million published, says Jungsik Noh, Ph.D., assistant professor at Southwest UT in Lyda Hill’s Bioinformatics Department and first author of study.
Of those 71 million Americans estimated to have COVID-19, 7 million (2.1% of the U.S. population) had current infections and were potentially contagious on Feb. 4, according to the algorithm.
Noh’s written study is based on calculations made in September. At that time, it is reported, the number of actual cumulative cases in 25 of the 50 most affected countries was five to twenty times higher than the number of confirmed cases then suggested.
Seeing the current information available on the online algorithm, the estimates are now closer to the figures reported, but are still much higher. As of February 4, Brazil had more than 36 million cases accumulated according to the algorithm estimate, almost four times more than the 9.4 million confirmed cases reported. France had 14 million compared to the 3.2 million reported. And the UK had almost 25 million instead of about 4 million, more than six times as many. Mexico, an atypical value, had almost 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 entire United States and in countries around the world,” says Noh.
The algorithm uses the number of reported deaths (which are believed to be more accurate and complete than the number of laboratory-confirmed cases) as a basis for its calculations. It then assumes an infection mortality rate of 0.66 percent, based on a previous pandemic study in China, and considers other factors such as the average number of days from the onset of symptoms to death. or recovery. It also compares its estimate with the number of confirmed cases to calculate a proportion of confirmed and estimated infections.
There are still many uncertainties about COVID-19 (particularly the mortality rate) and therefore the estimates are approximate, Noh says. But he believes the model estimates are more accurate and leave out fewer cases than the confirmed ones currently used as a guide for public health policies. Noh adds that it is important to have a more complete estimate of the prevalence of the disease.
“These are critical statistics on the severity of COVID-19 in each region. Knowing the true severity of different regions will help us effectively fight the spread of the virus, “he said.” The currently infected population is the cause of future infections and deaths. Its actual size in a region is a crucial variable that is required. to determine the severity of COVID-19 and build strategies against regional outbreaks “.
In the U.S., infection rates vary widely by state. California has had nearly 7 million infections since the start of the pandemic compared to New York’s 5.7 million, according to the algorithm’s projections for Feb. 4. .
Other model estimates for Feb. 4: In Pennsylvania, 11.2 percent of the population had current infections, the highest rate in any state, compared to a minimum of 0.15 percent of people who they lived in Minnesota; in New York, an early hot spot, 528,000 people had active infections, or about 2.7 percent of its population. Meanwhile, in Texas, 2.3% had current infections.
Noh says he developed the algorithm last summer while trying to decide if he wanted to send his sixth-grader daughter to school in person. He says there was nowhere to find the data he needed to assess the security of doing so.
Once he built the algorithm of the machine, he discovered that the area where he lived had a current infection rate of about 1%. So her daughter went to school.
Noh checked their results by comparing their results with existing prevalence rates in several studies that used blood tests to check for antibodies to the SARS-CoV-2 virus, which causes COVID-19. For most areas tested, estimates of their infection algorithm corresponded closely to the percentage of people who had tested positive for antibodies, according to the PLOS ONE to study.
The online model uses COVID-19 death data from Johns Hopkins University and The COVID Tracking Project, a volunteer organization founded to help track COVID-19, to run its daily updates. However, estimates published in PLOS ONE the study dates from September 3rd. At that time, about 10 percent of the U.S. population had become 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 Distinguished Chair in Basic Biomedical Science.
Funding came from Lyda Hill Philanthropies.