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Advisor(s)
Abstract(s)
Abstract
Background COVID-19 caused the largest pandemic of the twenty-frst 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 efects 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 specifcities, namely sociodemographic, economic and mobility. Change point analysis
revealed evidence of nonlinearity, and the susceptibility classes refect 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 fndings highlight the importance of customising interventions to specifc 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.
Description
Keywords
COVID-19 Health determinants GIS Multicriteria decision analysis Non-pharmacological interventions Spatial-based policies Spatiotemporal analysis
Pedagogical Context
Citation
Alves, A., Marques da Costa, N., Morgado, Paulo, & Marques da Costa, E. (2023). Uncovering COVID-19 infection determinants in Portugal: towards an evidence-based spatial susceptibility index to support epidemiological containment policies. International Journal of Health Geographics, 22(8). https://doi.org/10.1186/s12942-023-00329-4
Publisher
BMC, Springer Nature