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.

Due to its novelty, the recent pandemic of the coronavirus disease (COVID-19), which is associated with the spread of the new severe acute respiratory syndrome coronavirus (SARS-CoV-2), triggered the public’s interest in accessing information, demonstrating the importance of obtaining and analyzing credible and updated information from an epidemiological surveillance context. For this purpose, health authorities, international organizations, and university institutions have published online various graphic and cartographic representations of the evolution of the pandemic with daily updates that allow the almost real-time monitoring of the evolutionary behavior of the spread, lethality, and territorial distribution of the disease. The purpose of this article is to describe the technical solution and the main results associated with the publication of the COMPRIME_COMPRI_MOv dashboard for the dissemination of information and multi-scale knowledge of COVID-19. Under two rapidly implementing research projects for innovative solutions to respond to the COVID-19 pandemic, promoted in Portugal by the FCT (Foundation for Science and Technology), a website was created. That website brings together a diverse set of variables and indicators in a dynamic and interactive way that reflects the evolutionary behavior of the pandemic from a multi-scale perspective, in Portugal, constituting itself as a system for monitoring the evolution of the pandemic. In the current situation, this type of exploratory solutions proves to be crucial to guarantee everyone’s access to information while simultaneously emerging as an epidemiological surveillance tool that is capable of assisting decision-making by public authorities with competence in defining control policies and fight the spread of the new coronavirus.

Monitoring land-use patterns and its trends provides useful information for impact evaluation and policy design. The latest in-depth studies of land-use dynamics for continental Portugal are outdated, and have not examined how municipalities may be classified into a typology of observed dynamics or considered the trajectory profiles of land-use transitions. This paper presents a comprehensive analysis of the spatiotemporal dynamics of land-use in continental Portugal from 1995 to 2018. Our multi-scalar approach used land-use maps in geographic information systems with the following objectives: (i) quantify variations of land-use classes, (ii) assess the transitions between uses, and (iii) derive a municipal typology of land-use dynamics. The methodology employed involved calculating statistical indicators of land-use classes, transition matrices between uses and combinatorial analysis for the most common trajectory-profiles. For the typology, a principal component analysis was used for dimensionality reduction and the respective components were classified by testing several clustering techniques. Results showed that the land-use transitions were not homogeneous in space or time, leading to the growth of territorial asymmetries. Forest (Δ5%), water bodies (Δ28%) and artificial surfaces (Δ35%) had a greater expansion, as opposed to agricultural areas, which had the biggest decline (Δ-8%). Despite the decline of agricultural activities, olive-grove expansion (Δ7%) was a relevant dynamic, and in the case of forests, the increment of eucalyptus (Δ34%) replaced native species such as the maritime pine (Δ-20%). A land-use-dynamics typology was estimated, dividing continental Portugal into 11 clusters, which is informative for sectoral policies and spatial planning, as zonings in need of interventions tailored to their specificities. The findings are a contribution to the study of land-use dynamics in continental Portugal, presenting various challenges for sustainable land uses with regard to the urban system, forest management, food production, soil preservation, and ecosystem protection.

No abstract

Background COVID-19 caused the largest pandemic of the twenty-first century forcing the adoption of containment policies all over the world. Many studies on COVID-19 health determinants have been conducted, mainly using multivariate methods and geographic information systems (GIS), but few attempted to demonstrate how knowing social, economic, mobility, behavioural, and other spatial determinants and their effects can help to contain the disease. For example, in mainland Portugal, non-pharmacological interventions (NPI) were primarily dependent on epidemiological indicators and ignored the spatial variation of susceptibility to infection. Methods We present a data-driven GIS-multicriteria analysis to derive a spatial-based susceptibility index to COVID-19 infection in Portugal. The cumulative incidence over 14 days was used in a stepwise multiple linear regression as the target variable along potential determinants at the municipal scale. To infer the existence of thresholds in the relationships between determinants and incidence the most relevant factors were examined using a bivariate Bayesian change point analysis. The susceptibility index was mapped based on these thresholds using a weighted linear combination. Results Regression results support that COVID-19 spread in mainland Portugal had strong associations with factors related to socio-territorial specificities, namely sociodemographic, economic and mobility. Change point analysis revealed evidence of nonlinearity, and the susceptibility classes reflect spatial dependency. The spatial index of susceptibility to infection explains with accuracy previous and posterior infections. Assessing the NPI levels in relation to the susceptibility map points towards a disagreement between the severity of restrictions and the actual propensity for transmission, highlighting the need for more tailored interventions. Conclusions This article argues that NPI to contain COVID-19 spread should consider the spatial variation of the susceptibility to infection. The findings highlight the importance of customising interventions to specific geographical contexts due to the uneven distribution of COVID-19 infection determinants. The methodology has the potential for replication at other geographical scales and regions to better understand the role of health determinants in explaining spatiotemporal patterns of diseases and promoting evidence-based public health policies.