MSU researcher publishes paper examining COVID-19 spread
How many people in the U.S. have had COVID-19? Using a database of information collected after the 2009 H1N1 outbreak, a Montana State University researcher is helping develop a better understanding of the spread of the novel coronavirus.
Alex Washburne, a researcher in the Bozeman Disease Ecology Lab, which is housed in the College of Agriculture’s Department of Microbiology and Immunology, published a paper on the subject this week in the journal Science Translational Medicine. The paper uses data from ILINet a database created by the Centers for Disease Control and Prevention in 2010 to count patients who check into medical clinics with influenza-like illnesses, or ILI. That type of data collection for the purpose of identifying trends is known as syndromic surveillance.
Influenza-like illnesses include any number of infections that carry symptoms similar to the seasonal flu — such as fever, cough and sore throat. Both influenza-like H1N1 and non-influenza diseases like COVID-19 fall into that group. Monitoring trends in ILI clinic visits, Washburne said, could help better understand how quickly and extensively COVID-19 spread during the early days of its appearance in the U.S.
In collaboration with researchers at Pennsylvania State and Cornell universities, Washburne examined the number of ILI visits reported each week over the last decade and compared those historical trends to such visits during March 2020. They identified a surge in March 2020 ILI visits that parallels regional increases in COVID-19 cases.
By examining ILI data alongside the known regional prevalence of COVID-19, Washburne and his collaborators determined that there may have been many cases of the coronavirus disease that weren’t initially identified as such.
Washburne and his colleagues estimate that as many as 87% of coronavirus cases were not diagnosed during early March, which could translate to around 8.7 million people based on the excess March ILI visits. The surge in ILI diminished quickly in the latter part of March, leading researchers to conclude that more cases of COVID-19 were being identified since fewer ILI reports were being logged in the database.
“Early on there seems to have been a low case detection rate, but as time went on that changed,” said Washburne. “By the last week in March, as more and more testing was going on, that case detection rate increased significantly.”
This is good news for scientists seeking to predict and prepare for future epidemics, said Washburne. A baseline has been established through a decade of ILI data collection that allows for the early detection of anomalous surges of ILI that deviate from the annual average.
With much of the research about COVID-19 happening as the pandemic unfolds, Washburne said syndromic surveillance like this shows researchers and the medical community one piece of a larger story. When coupled with COVID-19 testing efforts and serological surveys, which seek to identify the proportion of a population with immunity to an illness, this type of data collection and analysis can illuminate a piece of the puzzle that helps outline our understanding of coronavirus as a whole, he said, while also offering insight for future potential epidemics.
Washburne also said that syndromic surveillance using tools like ILINet could be applied in areas where widespread testing is too expensive.
“For communities that may not have the capacity for more large-scale testing, this may be able to help give them a picture of the movement of their epidemic in time and space,” he said. “That way they can know when to implement actions like mask wearing and social distancing measures.”
The practice of collecting data ahead of a potential outbreak is an investment in future public health, Washburne said. This research into COVID-19 wouldn’t have been possible without the creation of the database after H1N1, so continuing to expanding the baseline data collected for other illnesses could be crucial in navigating future pandemics.
“All these different methods can be used to cross-validate each other,” he said. “We know if our other methods don’t work optimally, we have additional resources. Things like this can really help us be better prepared in the future.”