The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks

Abstract

Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country’s measures, which were implemented to contain the virus’ spread. Results: This approach demonstrated that the spread of the disease in Brazil does not have a standard behavior, changing according to the region, state, or city. The analyses showed that cities and states in the north and northeast regions of the country were the most affected by the disease, with the highest number of cases and deaths registered per 100,000 inhabitants. Conclusions: The SOM clustering was able to spatially group cities, states, and regions according to their coronavirus cases, with similar behavior. Thus, it is possible to benefit from the use of similar strategies to deal with the virus’ spread in these cities, states, and regions.

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“…The SOM network routine developed was used according to the algorithm described in Haykin [37] and was applied using the Matlab software routine. For more details about the theoretical and mathematical foundations of the SOM algorithm, readers can consult the references [34,39,40] and Supplementary Material.…”

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“…The SOM clustering could spatially group cities, states, and regions based on coronavirus cases with similar activity. As a result, similar techniques to combat the virus's spread in these towns, states, and regions could be beneficial [ 45 ].…”

“…Galvan, Effting [ 45 ] developed SOM-type unsupervised NNs, which were used to determine the spatial and temporal distribution of COVID-19 in Brazil, based on the number of cases and deaths. The SOM does not determine the steps to help curb the virus's spread, but the datasets reflect the consequences of the country's measures.…”

“… No The Humanitarian Data Exchange was used to collect the dataset. No SOM Analyze the global coronavirus pandemic's spatial evolution -High accuracy - Low robustness -Low dependability Galvan, Effting [ 45 ] Using SOM's spatial clustering capability to spatially group related towns, states, and regions based on COVID-19 cases. -Able to spatially group cities, states, and regions based on the prevalence of coronavirus -Comorbidities, the number of hospital beds available, trained staff, the human development index, and the environment are not considered.…”

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“…It has outstanding visualisation capabilities, it is ease to implement and robust to missing data ( Asan and Ercan, 2012 ; Vesanto, 1999 ). In the context of the COVID-19 pandemic, SOM has been used to find similarities in the transmission dynamics among cities, regions or countries worldwide ( Galvan et al, 2021 ; Hartono, 2020 ; Melin et al, 2020 ). Other authors focused on evaluating the influence of demographic, socio-economic, and health conditions in the virus spread ( Galvan et al, 2020 ; Khan et al, 2021 ).…”