When the COVID-19 coronavirus hit American shores, there were testing issues and little was known about asymptomatic and alternatively symptomatic people. Now, Penn State University epidemiologists believe these factors led to an infection rate 80 times higher than originally estimated.
What if 80 times more people were infected with COVID-19 than we originally counted? As of April 1, approximately 200,000 cases (cumulative) had been identified in the US. As of today, there are just over 2.5 million cases.
What the Penn State team is saying is that by the end of March, the actual cumulative infection count in America was as high as 16 million people. It was also doubling nearly twice as fast as the tracking told us.
That would put the mortality rate in the infinitesimal range. Of course, that will spur the easily anticipated questions. Did we overreact? Could we have gotten through this without shutting the country down? Should we worry less about spikes in cases during reopening?
Those are policy questions that need to be closely and apolitically examined when this is all over. For now, let’s look at what the PSU team found.
The team used the Centers for Disease Control and Prevention’s influenza-like Illness (ILI) surveillance data to estimate the detection rate of cases that were symptomatic. They reviewed a three week period in March 2020. Their findings were published on June 22 in the journal Science Translational Medicine.
“We analyzed each state’s ILI cases to estimate the number that could not be attributed to influenza and were in excess of seasonal baseline levels,” said Justin Silverman, assistant professor in Penn State’s College of Information Sciences and Technology and Department of Medicine. “When you subtract these out, you’re left with what we’re calling excess ILI—cases that can’t be explained by either influenza or the typical seasonal variation of respiratory pathogens.”
What did they find? A near-perfect correlation between the excess ILI and the spread of COVID-19 throughout the US.
Said Silverman, “This suggests that ILI data is capturing COVID cases, and there appears to be a much greater undiagnosed population than originally thought.”
Approximately 100,000 COVID-19 cases were reported during the last three weeks of March. But the size of the surge of excess ILI noted by Silverman and the PSU team indicates and actual new case count of over 8.7 million for the same period.
“At first I couldn’t believe our estimates were correct,” said Silverman. “But we realized that deaths across the U.S. had been doubling every three days and that our estimate of the infection rate was consistent with three-day doubling since the first observed case was reported in Washington state on January 15.”
The research team wanted to know if the state-by-state results would match up. They weren’t disappointed. When they inspected data from each of the states, they noted that states with higher per capita infection rates also had higher per capita measures of their surge in excess ILI. The gaps began to close somewhat when those states started antibody testing.
For example, the researchers model for New York indicated that at least 9% of the total population of the Empire State had been infected by the end of March. Antibody testing done by the state on 3,000 New Yorkers indicated a 13.9% infection rate. That translates to about 2.7 million people.
This surge in excess ILI seems to have hit its apex in mid-March. The researchers believe that, around that time, fewer people with mild symptoms sought treatment. That was also about the time many states began massive interventions to reduce transmission rates. Almost half of the United States was under some kind of stay-at-home orders by March 28.
These findings may suggest a different way of thinking about the COVID-19 coronavirus pandemic. We may have been focused on the wrong aspects of the virus and its impacts.
“Our results suggest that the overwhelming effects of COVID-19 may have less to do with the virus’ lethality and more to do with how quickly it was able to spread through communities initially,” Silverman explained. “A lower fatality rate coupled with a higher prevalence of disease and rapid growth of regional epidemics provides an alternative explanation of the large number of deaths and overcrowding of hospitals we have seen in certain areas of the world.”
As we learn more about COVID-19, we realize that the transmission rate may have been significantly higher than anyone estimated. However, the mortality rate is vastly lower than believed by public health authorities.
When a pandemic begins, those same authorities must take action based on the worse possible scenario and predictions. But when more information becomes available that makes better decision-making possible, better decisions have to be made. Even if that makes those public health and government authorities look less authoritative than they might like.
We do as well as we can based on what we know. But when we know better, we must do better. Next time – and there will be a next time – we must do better.
Keep the faith and keep after it!
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Journal Reference – Justin D. Silverman et al, Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States, 22 June 2020, Science Translational Medicine. DOI: 10.1126/scitranslmed.abc1126 , stm.sciencemag.org/content/ear … scitranslmed.abc1126