THURSDAY, June 11, 2020 — Here’s a glimmer of hope about the new coronavirus: New research finds it appears to follow a seasonal pattern that is similar to the flu.
Scientists found that all cities/regions with large COVID-19 outbreaks have similar winter climates, with an average temperature of 41 to 52 degrees Fahrenheit, average humidity levels of 47% to 79%, and are located within a latitude band of 30 to 50 N.
This includes Wuhan, China; South Korea; Japan; Iran; Northern Italy; Seattle; and northern California.
The findings also suggest that U.S. mid-Atlantic states may be at risk, as well as New England, according to researchers at the Institute of Human Virology (IHV) at the University of Maryland School of Medicine (UMSOM) and the Global Virus Network (GVN).
“Based on what we have documented so far, it appears that the virus has a harder time spreading between people in warmer, tropical climates,” said study leader Dr. Mohammad Sajadi, an associate professor of medicine at UMSOM, physician-scientist at IHV, and a member of GVN.
The researchers used weather data from the previous few months, as well as typical weather patterns from last year, to predict community spread of COVID-19 within the next few weeks.
The next step is to determine if weather and climate forecasts could make the predictions more reliable.
Study co-author Dr. Anthony Amoroso said, “I think what is important is that this is a testable hypothesis.” Amoroso is an associate professor of medicine at UMSOM and chief of clinical care programs for IHV.
“If it holds true, it could be very helpful for health system preparation, surveillance and containment efforts,” he added in an institute news release.
The study was published online June 11 in JAMA Network Open.
According to Dr. Robert Gallo, an IHV co-founder and director, co-founder and chairman of the International Scientific Leadership Board of GVN, and a professor at UMSOM, “Through this extensive research, it has been determined that weather modeling could potentially explain the spread of COVID-19, making it possible to predict the regions that are most likely to be at higher risk of significant community spread in the near future.”
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Posted: June 2020