摘要：The spread of the coronavirus disease 2019 (COVID-19) has important links with populationmobility. Social interaction is a known determinant of human-to-human transmission of infectiousdiseases and, in turn, population mobility as a proxy of interaction is of paramount importance toanalyze COVID-19 diffusion. Using mobility data from Google’s Community Reports, this papercaptures the association between changes in mobility patterns through time and the correspondingCOVID-19 incidence at a multi-scalar approach applied to mainland Portugal. Results demonstrate astrong relationship between mobility data and COVID-19 incidence, suggesting that more mobility isassociated with more COVID-19 cases. Methodological procedures can be summarized in a multiplelinear regression with a time moving window. Model validation demonstrate good forecast accuracy,particularly when we consider the cumulative number of cases. Based on this premise, it is possibleto estimate and predict future evolution of the number of COVID-19 cases using near real-timeinformation of population mobility.
关键词：COVID-19;mobility;containment measures;cases estimation;predictive model
