A coronavirus antibody test conducted by Stanford University scientists concluded that the infection is both more common than previously thought and possesses a lower fatality rate than what current data suggest.

"Our data imply that, by April 1 (three days prior to the end of our survey) between 48,000 and 81,000 people had been infected in Santa Clara County. The reported number of confirmed positive cases in the county on April 1 was 956, 50-85-fold lower than the number of infections predicted by this study," their study reads.

The study, which tested 3,330 people, adjusting for zip code, sex, and race/ethnicity, concludes that the prevalence of coronavirus antibodies in Santa Clara County, California, is more widespread than the number of confirmed cases indicates.

“After adjusting for population and test performance characteristics, we estimate that the seroprevalence of antibodies to SARS-CoV-2 in Santa Clara County is between 2.49% and 4.16%, with uncertainty bounds ranging from 1.80% (lower uncertainty bound of the lowest estimate), up to 5.70% (upper uncertainty bound of the highest estimate),” the study said.

“Test performance characteristics are the most critical driver of this range, with lower estimates associated with data suggesting the test has a high sensitivity for identifying SARSCoV-2, and higher estimates resulting from data suggesting over 30% of positive cases are missed by the test.”

“Coronavirus random sampling study from Stanford,” Fox News’s Lisa Boothe said on Twitter in response to the study. “They found the infection is 50-85 x more common than previously thought & fatality rate accordingly 50-85 x lower than the crude numbers would suggest.”

BREAKING: Coronavirus random sampling study from Stanford. They found the infection is 50-85 x more common than previously thought & fatality rate accordingly 50-85 x lower than the crude numbers would suggest. #COVID-19 https://t.co/LWo4PTSjj0 — Lisa Boothe (@LisaMarieBoothe) April 17, 2020

The study adds that “the most important implication of these findings is that the number of infections is much greater than the reported number of cases.”

Dr. Jay Bhattacharya, one of the contributors to the study, wrote an opinion article in March questioning the models showing that millions of people in the United States would die from the virus and pointed out that more testing is needed to determine the fatality rate.

“Fear of Covid-19 is based on its high estimated case fatality rate — 2% to 4% of people with confirmed Covid-19 have died, according to the World Health Organization and others,” he wrote in the Wall Street Journal. “So if 100 million Americans ultimately get the disease, two million to four million could die. We believe that estimate is deeply flawed. The true fatality rate is the portion of those infected who die, not the deaths from identified positive cases.”

“The main message my colleagues and I want to get across is that the facts to date are consistent with a tremendous range of uncertainty regarding the fatality rate from COVID-19,” Bhattacharya told the Washington Examiner in March. “We desperately need a population-representative estimate of the seroprevalence of the disease so we can reduce that uncertainty and make better policy on the basis of our improved knowledge. Such a study would not be too expensive and is feasible to run immediately.”

It was reported earlier this month that doctors believe the virus was present in California as early as December.

“When public health [officials] tried to track down the start of the disease … we weren’t able to find, specifically, a contact,” Santa Clara County Executive Dr. Jeff Smith said. “That means the virus is in the community already — not, as was suspected by the CDC, as only in China and being spread from contact with China.”