Mobility Restrictions for the Control of Covid-19 Epidemic

Abstract

Objective: To determine whether the SEIR model, associated to mobility changes parameters, can determine the likelihood of establishing control over an epidemic in a city, state or country.
 Study design and setting: The critical step in the prediction of COVID-19 by a SEIR model are the values of the basic reproduction number (R0) and the infectious period, in days. R0 and the infectious periods were calculated by mathematical constrained optimization, and used to determine the numerically minimum SEIR model errors in a country, based on COVID-19 data until April 11th. The Community Mobility Reports from Google Maps (<https://www.google.com/covid19/mobility>) provided mobility changes on April 5th compared to the baseline (Jan 3th to Feb 6th). The data was used to measure the non-pharmacological intervention adherence. The impact of each mobility component was calculated by logistic regression models. COVID-19 control was defined by SEIR model R0<1.0 in a country.
 Results: The ECDC has registered 1,653,204 COVID-19 worldwide on April 11th. Sixteen countries presented 78% of all cases. Of the six Google Maps mobility parameters, the “Stay at home” parameter was the strongest one to control COVID-19 in a country: an increase of 50% in mobility trends for places of residence has a 99% chance of outbreak control.
 Conclusions: Residential mobility restriction presented itself as the most effective measure. The SEIR model associated with mobility parameters proved to be a useful tool in determining the chance of COVID-19 outbreak control.

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NO2 pollution decrease in big cities of Latin America during COVID-19 pandemic

“…Population mobility data were taken from the Google Mobility Reports[56], widely used during 2020[57,58,59,60,61,62]. The Google Mobility Reports show changes in baseline daily trips and time spent at home for six categories in 131 countries around the world and their subregions.…”