Spatiotemporal evolution of COVID-19 in Portugal’s Mainland with self-organizing maps

International Journal of Health Geographics

Research

Manuel C. Ribeiro¹  Leonardo Azevedo¹  Maria João Pereira¹  Pedro Pinto Leite²  André Peralta-Santos³  Igor Duarte⁴ 

[1] CERENA/DER, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001, Lisbon, Portugal;Direção de Serviços de Informação e Análise, Direção-Geral da Saúde, Lisbon, Portugal;Direção de Serviços de Informação e Análise, Direção-Geral da Saúde, Lisbon, Portugal;NOVA National School of Public Health, Public Health Research Centre, Universidade NOVA de Lisboa, Lisbon, Portugal;Comprehensive Health Research Centre (CHRC), Universidade NOVA de Lisboa, Lisbon, Portugal;Formely: Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001, Lisbon, Portugal;

关键词: Self-organizing maps;    COVID-19;    Geo-Spatial Analysis;    Socio-economic determinants of disease;    SARS-CoV-2;   

DOI  :  10.1186/s12942-022-00322-3

 received in 2022-10-17, accepted in 2022-12-23,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundSelf-Organizing Maps (SOM) are an unsupervised learning clustering and dimensionality reduction algorithm capable of mapping an initial complex high-dimensional data set into a low-dimensional domain, such as a two-dimensional grid of neurons. In the reduced space, the original complex patterns and their interactions can be better visualized, interpreted and understood.MethodsWe use SOM to simultaneously couple the spatial and temporal domains of the COVID-19 evolution in the 278 municipalities of mainland Portugal during the first year of the pandemic. Temporal 14-days cumulative incidence time series along with socio-economic and demographic indicators per municipality were analyzed with SOM to identify regions of the country with similar behavior and infer the possible common origins of the incidence evolution.ResultsThe results show how neighbor municipalities tend to share a similar behavior of the disease, revealing the strong spatiotemporal relationship of the COVID-19 spreading beyond the administrative borders of each municipality. Additionally, we demonstrate how local socio-economic and demographic characteristics evolved as determinants of COVID-19 transmission, during the 1st wave school density per municipality was more relevant, where during 2nd wave jobs in the secondary sector and the deprivation score were more relevant.ConclusionsThe results show that SOM can be an effective tool to analysing the spatiotemporal behavior of COVID-19 and synthetize the history of the disease in mainland Portugal during the period in analysis. While SOM have been applied to diverse scientific fields, the application of SOM to study the spatiotemporal evolution of COVID-19 is still limited. This work illustrates how SOM can be used to describe the spatiotemporal behavior of epidemic events. While the example shown herein uses 14-days cumulative incidence curves, the same analysis can be performed using other relevant data such as mortality data, vaccination rates or even infection rates of other disease of infectious nature.

【 授权许可】

CC BY   
© The Author(s) 2023

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