Patients with undiagnosed green symptoms who actually had COVID-19 last winter there were thousands of undiagnosed early cases of the disease at the beginning of this year. In a new paper in The Lancet‘s magazine for open access EClinicalMedicine, Epidemiological researchers from the University of Texas at Austin estimated that COVID-19 was most widespread in Wuhan, China, and Seattle, Washington, weeks prior to lockdown measures in each city.
In the US, about a third of the rough undiagnosed cases were among children. The researchers also concluded that the first case of COVID-19 in Seattle may have arrived as far back as Christmas or New Year’s Day.
Lauren Ancel Meyers, a professor of integrative biology and statistics and data sciences who leads UT Austin COVID-19 Modeling Consortium, worked with its team of researchers to extrapolate the extent of the COVID-19 epidemic in Wuhan and Seattle based on retested throat water taken from patients suffering from influenza-like illnesses in Wuhan in January and in late February and early March in Seattle. When the samples were later analyzed in each city, it turned out to be flu, but some turned out to be positive for SARS-CoV-2, the virus that causes COVID-19.
“Even before we realized that COVID-19 was widespread, the data indicated that there was at least one case of COVID-19 for every two cases of influenza,” Meyers said. “Since we knew how widespread flu was at that time, we were able to reasonably determine the prevalence of COVID-19.”
When the Chinese government shut down Wuhan on January 22, there were 422 known cases. However, extrapolating the throat swab data across the city using a new epidemiological model, Meyers and her team found that more than 12,000 symptoms of COVID-19 could not be detected. On March 9, the week when schools in Seattle closed due to the virus, researchers estimated that more than 9,000 people with flu-like symptoms had COVID-19 and that about a third of that total were children. The data does not indicate that health authorities were aware of these infections, but that they may have gone invisible in the early and uncertain stages of the pandemic.
“Given that COVID-19 appears to be overwhelmingly mild in children, our high estimate of symptomatic pediatric cases in Seattle suggests that there could have been thousands more mild cases by then,” wrote Zhanwei Du, a postdoctoral researcher at Meyers. ‘lab and first author on the study.
According to several other studies, about half of the COVID-19 cases are asymptomatic, leading researchers to believe that there may have been thousands more infected people in Wuhan and Seattle before the respective measures of each city went into effect.
“We can go back in history to this pandemic and combine it with a combination of investigative techniques and modeling,” Meyers said. “This helps us understand how the pandemic is spreading so rapidly around the world and provides insight into what we can see in the coming weeks and months.”
The new technique for estimating the amount of not to be seen COVID-19 based on the ratio of flu cases to COVID-19 cases has also been used to determine how many children were actually infected in each city and the rate of the early pandemic in the US, Meyers said.
The finding in the new paper is consistent with work that Meyers and her team did on the early spread of the virus. Using travel data, she and her team estimated how far the virus had spread and concluded that there were as many as 12,000 cases of COVID-19 in Wuhan before being unlocked.
Reference: “Using the COVID-19 to Pay for Influenza Propagation for Early Pandemic Spread in Wuhan, China and Seattle, US” by Zhanwei Du, Emily Javan, Ciara Nugent, Benjamin J. Cowling and Lauren Ancel Meyers, August 12, 2020, EClinicalMedicine.
DOI: 10.1016 / j.eclinm.2020.100479
In addition to Meyers and Du, graduates Emily Javan and Ciara Nugent from the University of Texas at Austin and Professor Benjamin J. Cowling from the University of Hong Kong contributed to the research. The study was funded by the National Institutes of Health.
