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

Abstract

Background Self-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. Methods We 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. Results The 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. Conclusions The 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.

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

“…Furthermore, municipalities with higher susceptibility were geographically close to similar classes. As Duarte et al [82] have assessed neighbouring municipalities tended to share similar behaviour because local effects justify spatial dependence in COVID-19 diffusion, confirmed in Portugal [43,44], and which our modelling process did not account for. This is not unusual, since one of the most common processes infectious disease spatial diffusion-contagious diffusion-is based essentially on spatial contiguity [38,83] and which was boosted by mobility movements between municipalities.…”

Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: A functional data analysis from August 2020 to March 2022

“…Specifically, they found socio-demographic and economic factors to have a higher impact on COVID-19 incidence in northern regions along the Porto region and along the Atlantic shore, due to stronger cultural relationships between neighbouring municipalities. Duarte et al [ 18 ] also identified socio-demographic and economic profiles as relevant risk factors for the increased COVID-19 incidence observed in the same region during the winter 2020–2021.…”

“…Quantitative exploratory approaches using time-series data have been applied to model the temporal [ 12 ] and spatiotemporal [ 13 ] evolution of COVID-19 cases, mortality over different regions [ 14 ], to identify vaccine hesitancy clusters [ 15 ] or to extract mobility trends at different spatial scales [ 16 ]. These methods could be supplemented with exploratory data analysis tools (e.g., dimension reduction techniques or unsupervised classification methods) to detect patterns or to discriminate differentiating features [ 17 , 18 ]. However, while these tools aim to describe and understand the spatiotemporal patterns of COVID-19 incidence, they do not explicitly consider the spatial correlations occurring over time.…”

Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: a functional data analysis from August 2020 to March 2022

“…Nonetheless, previous research from Almendra et al 36 outlined a geographical pattern compatible with our results, as they found sociodemographic and economic factors to have higher impact on COVID-19 incidence in that region due to stronger cultural relationships between neighbor municipalities. Duarte et al 15 also identi ed sociodemographic and economic pro les as relevant risk factors for increased COVID-19 incidence observed in the same region during the winter 2020-21.…”

“…Quantitative approaches using time-series data have been applied to model the temporal evolution of COVID-19 new cases (e.g., Carroll et al, 2020) or the mortality over different regions 13 . These methods could be complemented with exploratory data analysis tools (e.g., dimension reduction techniques or unsupervised classi cation methods) to detect patterns or to discriminate differentiating features 14,15 . Alternatively, functional data analysis (FDA) transforms time-series observations into smoothing functions providing a model for the underlying process giving rise to them.…”