Centre of Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, 1600-276, Lisbon, Portugal.
Associate Laboratory TERRA, 1349-017, Lisbon, Portugal.
Int J Health Geogr. 2023 Apr 6;22(1):8. doi: 10.1186/s12942-023-00329-4.
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.
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.
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.
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.
COVID-19 引发了 21 世纪最大的大流行，迫使全球各国采取遏制政策。许多关于 COVID-19 健康决定因素的研究主要使用多元方法和地理信息系统（GIS），但很少有研究试图证明了解社会、经济、流动性、行为和其他空间决定因素及其影响如何有助于控制疾病。例如，在葡萄牙本土，非药物干预（NPI）主要依赖于流行病学指标，而忽略了感染易感性的空间变化。
我们提出了一种基于数据的 GIS 多标准分析方法，以得出葡萄牙 COVID-19 感染的基于空间的易感性指数。将 14 天的累积发病率用作目标变量，在市级尺度上使用潜在决定因素进行逐步多元线性回归。为了推断决定因素与发病率之间关系中是否存在阈值，使用二元贝叶斯变化点分析检查了最相关的因素。基于这些阈值，使用加权线性组合对易感性指数进行了映射。
回归结果支持葡萄牙本土 COVID-19 的传播与与社会地域特征相关的因素密切相关，即社会人口统计学、经济和流动性因素。变化点分析显示存在非线性证据，易感性分类反映了空间依赖性。感染易感性的空间指数准确地解释了先前和后续的感染。根据易感性地图评估 NPI 水平表明，限制的严重程度与实际传播倾向之间存在不一致，这突显了需要更有针对性的干预措施。
本文认为，为了控制 COVID-19 的传播，NPI 应考虑感染易感性的空间变化。研究结果强调了根据特定地理背景定制干预措施的重要性，因为 COVID-19 感染决定因素的分布不均。该方法具有在其他地理尺度和地区复制的潜力，以更好地理解健康决定因素在解释疾病的时空模式以及促进循证公共卫生政策方面的作用。
