Spatial-temporal monitoring risk analysis and decision-making of COVID-19 distribution by region

Pereira, Leandro; Correia, Jorge; Sequeiros, José; Santos, José Luís; Jerónimo, Carlos

doi:10.1504/ijspm.2022.123472

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the case of Portugal, evidence-based knowledge about the existence of geographical contexts that are more favourable to transmission and outbreaks has been shown and highlighted by several authors [14,[43][44][45]. The spread of COVID-19 in the country has been associated with settlement patterns, transport networks, mobility behaviours, employment and other economic and social characteristics [8,[46][47][48].…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, spatial analysis can help analyze policy effects on transmission spatial dynamics. For the portuguese territory, Sá Marques et al [14] suggested the need for territorial customized NPI, proposing a geographic mosaic based on a vulnerability risk index, while Pereira et al [45] developed a risk conceptual model to monitor COVID-19 spatiotemporal dynamics.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Uncovering COVID-19 infection determinants in Portugal: towards an evidence-based spatial susceptibility index to support epidemiological containment policies

et al. 2023

View full text Add to dashboard Cite

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.

show abstract