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Advisor(s)
Abstract(s)
The spread of the coronavirus disease 2019 (COVID-19) has important links with population
mobility. Social interaction is a known determinant of human-to-human transmission of infectious
diseases and, in turn, population mobility as a proxy of interaction is of paramount importance to
analyze COVID-19 diffusion. Using mobility data from Google’s Community Reports, this paper
captures the association between changes in mobility patterns through time and the corresponding
COVID-19 incidence at a multi-scalar approach applied to mainland Portugal. Results demonstrate a
strong relationship between mobility data and COVID-19 incidence, suggesting that more mobility is
associated with more COVID-19 cases. Methodological procedures can be summarized in a multiple
linear 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 possible
to estimate and predict future evolution of the number of COVID-19 cases using near real-time
information of population mobility
Description
Keywords
COVID-19 Mobility Containment measures Cases estimation Predictive model
Pedagogical Context
Citation
Publisher
MDPI