Using machine learning, scientists may be able to assess the role climate and environmental variables play in the transmission of COVID-19.

A team from Lawrence Berkeley National Laboratory is applying machine learning methods to health and environmental datasets to determine whether the virus fades in warmer months and surges once it gets colder – similar to the flu.

“Environmental variables, such as temperature, humidity, and UV exposure, can have an effect on the virus directly, in terms of its viability. They can also affect the transmission of the virus and the formation of aerosols,” said Berkeley Lab scientist Eoin Brodie, the project lead.

“We will use state-of-the-art machine-learning methods to separate the contributions of social factors from the environmental factors to attempt to identify those environmental variables to which disease dynamics are most sensitive.”

Researchers will leverage county-level data, including the severity, distribution, and duration of the COVID-19 outbreak, and what public health interventions were issued when. The team will also analyze demographics, climate and weather factors, and population mobility dynamics.

Source: healthitanalytics.com