Metadata about: We developed a mobility-informed disease-transmission model for COVID-19, inspired by collision theory in gas-phase chemistry. This simple kinetic model leads to a closed-form infectious population as a function of time and cumulative mobility. This model uses fatality data from Johns Hopkins to infer the infectious population in the past, and mobility data from Google, without social-distancing policy, geological or demographic inputs. It was found that the model appears to be valid for twenty hardest hit counties in the United States. Based on this model, the number of infected people grows (shrinks) exponentially once the relative mobility exceeds (falls below) a critical value (~30% for New York City and ~60% for all other counties, relative to a median mobility from January 3 to February 6, 2020). A simple mobility cap can be used by government at different levels to control COVID-19 transmission in reopening or imposing another shutdown.

About: We developed a mobility-informed disease-transmission model for COVID-19, inspired by collision theory in gas-phase chemistry. This simple kinetic model leads to a closed-form infectious population as a function of time and cumulative mobility. This model uses fatality data from Johns Hopkins to infer the infectious population in the past, and mobility data from Google, without social-distancing policy, geological or demographic inputs. It was found that the model appears to be valid for twenty hardest hit counties in the United States. Based on this model, the number of infected people grows (shrinks) exponentially once the relative mobility exceeds (falls below) a critical value (~30% for New York City and ~60% for all other counties, relative to a median mobility from January 3 to February 6, 2020). A simple mobility cap can be used by government at different levels to control COVID-19 transmission in reopening or imposing another shutdown.

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