Machine Learning Used to Determine COVID-19's Seasonal Cycle

Scientists at Lawrence Berkeley National Laboratory (Berkeley Lab) are launching a project to apply machine-learning methods to health and environmental datasets, combined with high-resolution climate models and seasonal forecasts, to determine if COVID-19 is seasonal like the flu—waning in warm summer months then resurging in the fall and winter.

"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 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."

The research team will take advantage of an abundance of health data available at the county level—such as the severity, distribution, and duration of the COVID-19 outbreak, as well as what public health interventions were implemented when—along with demographics, climate and weather factors, and, thanks to smartphone data, population mobility dynamics. The initial goal of the research is to predict—for each county in the United States—how environmental factors influence the transmission of the SARS-CoV-2 virus, which causes COVID-19.

The team hopes to have the first phase of their analysis available by late summer or early fall. The next phase will be to make projections under different scenarios, which could aid in public health decisions.