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Researchers plan to use machine learning methods to estimate the seasonal cycle of COVID-19 in the US

pharmafile | May 21, 2020 | News story | Business Services | COVID-19, coronavirus

Scientists at the Lawrence Berkeley National Laboratory are launching a project to apply machine learning methods to map the severity, distribution and duration of the COVID-19 pandemic.

The goal of the research is to predict how environmental factors influence the transmission of the virus across each county in the United States. It will also utilize existing public health data including climate, weather factors, population mobility dynamics and public health interventions.

Researchers have already indicated that the geographical differences may have an impact on the rates of transmission. Temperature, humidity and the UX index have all been associated with the rates of COVID-19, while human contact still remains as the dominant influence on the spread of the virus. For example, in the southern hemisphere the spread of the disease has been slower, and places like India do not seem to have the same rate of spread as northern hemisphere countries.

The National Biodefense Analysis and Countermeasures Center (NBACC) assessed the longevity of the virus on various surfaces and found under sunlight and humidity it loses viability in about 60 minutes. This is compared to low temperatures in dark places, where the virus can exist for eight days.

Eoin Brodie, the Deputy Director of Berkeley Lab’s Climate and Ecosystem Sciences Division, is leading a team of lab scientists with expertise in climate modeling, data analytics, machine learning and geospatial analytics to investigate these factors on the virus.

Speaking on the project, Brodie said: “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.

“We would use models to project forward, with different weather scenarios, different health intervention scenarios – such as continued social distancing or whether there are vaccines or some level of herd immunity – in different parts of the country. For example, we hope to be able to say, if you have kids going back to school under this type of environment, the climate and weather in this zone will influence the potential transmission by this amount.”

The Berkeley team hopes to have the first part of their analysis available by late summer, and aim for the second phase to help make projections under different scenarios to aid public health decisions.

Conor Kavanagh