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

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.

Genomic surveillance: Circulating lineages and genomic variation of SARS-CoV-2 in early pandemic in Ceará state, Northeast Brazil.

In the Northeast of Brazil, Ceará was the second state most impacted by COVID-19 in number of cases and death rate. Despite that, the early dynamics of the pandemic in Ceará was not yet well understood due the low genomic surveillance of SARS-CoV-2 in 2020. In this study, we analyze the circulating lineages and the genomic variation of the virus in Ceará state. Thirty-four genomes were sequenced and combined with sequences available in GISAID database from March 2020 to June 2021 to compose the study dataset. The most prevalent lineages detected were B.1.1.33, in 2020, and P.1, in 2021. Other lineages were found, such as P.2, sublineages of P.1, B.1, B.1.1, B.1.1.28 and B.1.212. Analyzing the mutations, a total of 202 single-nucleotide polymorphisms (SNPs) were identified among the 34 genomes sequenced, of which 127 were missense, 74 synonymous, and one was a nonsense mutation. Among the missense mutations, C14408T, A23403G, T27299C, G28881A G28883C, and T29148C were the most prevalent within the dataset. Although SARS-CoV-2 sequencing data was limited in 2020, our results could provide insights to better understand the genetic diversity of the circulating lineages in Ceará.