A new study finds that warmer temperatures are associated with lower rates of SARS-CoV-2. However, the researchers say that the effect is modest and warn against the relaxation of containment measures.
Some viruses display seasonal behavior. Influenza cases, for example, peak during the fall and winter, commonly known as the flu season. There are various reasons for this, including biological factors related to the virus, as well as social and environmental factors.
The seasonality of the flu virus has raised hopes that the same may be true for the novel coronavirus, SARS-CoV-2, and that cases of the virus may, therefore, decrease over the summer months.
Indeed, laboratory data suggest that SARS-CoV-2 survives for a shorter period in higher temperatures. However, the effect of temperature on the real-world transmission of the virus is currently unclear.
As well as temperature, UV light also has a powerful effect on viral agents. People routinely use specific wavelengths of UV light, known as UV-C, to disinfect surfaces in laboratories and hospitals.
It is possible that the summer weather, with its increased sunlight, could also affect the transmission on the ground.
Researchers at Mount Auburn Hospital in Cambridge, MA, have now analyzed the impact of these factors on real-life transmission rates. As well as UV index and temperature, the researchers considered the effect of precipitation levels on the rate of disease, using real-world data on the number of COVID-19 cases occurring in the United States.
They found that temperature and UV both have a small effect on transmission, but precipitation seemingly has none.
The findings appear in full in the journal Clinical Infectious Diseases.
The researchers assessed the number of cases of SARS-CoV-2 infection reported per day across the U.S. from January 22 to April 3, 2020, using Johns Hopkins University’s COVID-19 Dashboard.
They combined this with state-level data on temperature, precipitation, and UV index from the National Centers for Environmental Information.
The team then evaluated the associations between the sets of data using regression analysis, a statistical