Spatiotemporal Dynamics of COVID-19 Infections in Mainland Portugal

Melissa Silva, Iuria Betco, César Capinha, Rita Roquette, Cláudia M. Viana and Jorge Rocha ()
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Melissa Silva: Associated Laboratory TERRA, Institute of Geography and Spatial Planning, University of Lisbon, 1649-004 Lisbon, Portugal
Iuria Betco: Associated Laboratory TERRA, Institute of Geography and Spatial Planning, University of Lisbon, 1649-004 Lisbon, Portugal
César Capinha: Associated Laboratory TERRA, Institute of Geography and Spatial Planning, University of Lisbon, 1649-004 Lisbon, Portugal
Rita Roquette: NOVA IMS Information Management School, NOVA University of Lisbon, 1099-085 Lisbon, Portugal
Cláudia M. Viana: Associated Laboratory TERRA, Institute of Geography and Spatial Planning, University of Lisbon, 1649-004 Lisbon, Portugal
Jorge Rocha: Associated Laboratory TERRA, Institute of Geography and Spatial Planning, University of Lisbon, 1649-004 Lisbon, Portugal

Sustainability, 2022, vol. 14, issue 16, 1-28

Abstract: The World Health Organization declared COVID-19 as a pandemic disease on 12 March 2020. Currently, this disease caused by the SARS-CoV-2 virus remains one of the biggest public health problems in the world. Thus, it is essential to apply methods that enable a better understanding of the virus diffusion processes, not only at the spatial level but also at the spatiotemporal one. To that end, we tried to understand the spatial distribution of COVID-19 pathology in continental Portugal at the municipal level and to comprehend how mobility influences transmission. We used autocorrelation indices such as Getis-Ord (with Euclidian distance and commuting values), Local Moran, and a new hybrid approach. Likewise, aiming to identify the spatiotemporal patterns of the virus propagation by using Man–Kendall statistics, we found that most hotspots of infected individuals occur in the municipalities of metropolitan areas. The spatiotemporal analysis identified most of the municipalities as oscillating hotspots.

Keywords: SARS-CoV-2; spatiotemporal analysis; hybrid approach; mobility; mainland Portugal (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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