Machine Learning Could be Used to Estimate COVID-19 Seasonable Cycles

Scientists at Lawrence Berkley National Laboratory are launching a project to apply machine learning methods to a myriad of health and environmental datasets to try and predict COVID-19 seasonable cycles. Berkely Lab Scientist, Eoin Brodie, said that “environmental variables, such as temperature, humidity, and UV [ultraviolet radiation] 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”. “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 and development will take advantage of the health data available during the COVID-19 pandemic, along with other factors such as climate and weather, to try and predict how these factors can influence the transmission of COVID-19.

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