biostudies-literatureLi YNCATS NIH HHSNLM NIH HHSNational Institutes of Health648https://www.ebi.ac.uk/biostudies/studies/S-EPMC9030792biostudies-literatureUnknown13(4)Deciphering the population structure of SARS-CoV-2 is critical to inform public health management and reduce the risk of future dissemination. With the continuous accruing of SARS-CoV-2 genomes worldwide, discovering an effective way to group these genomes is critical for organizing the landscape of the population structure of the virus. Taking advantage of recently published state-of-the-art machine learning algorithms, we used an unsupervised deep learning clustering algorithm to group a total of 16,873 SARS-CoV-2 genomes. Using single nucleotide polymorphisms as input features, we identified six major subtypes of SARS-CoV-2. The proportions of the clusters across the continents revealed distinct geographical distributions. Comprehensive analysis indicated that both genetic factors and hGenesUsing an Unsupervised Clustering Model to Detect the Early Spread of SARS-CoV-2 Worldwide.PMC9030792R01 LM013337UL1 TR0014221R01LM0133375UL1TR001422Li YZeng ZLiu QLuo YfalseUsing an Unsupervised Clustering Model to Detect the Early Spread of SARS-CoV-2 Worldwide.Deciphering the population structure of SARS-CoV-2 is critical to inform public health management and reduce the risk of future dissemination. With the continuous accruing of SARS-CoV-2 genomes worldwide, discovering an effective way to group these genomes is critical for organizing the landscape of the population structure of the virus. Taking advantage of recently published state-of-the-art machine learning algorithms, we used an unsupervised deep learning clustering algorithm to group a total of 16,873 SARS-CoV-2 genomes. Using single nucleotide polymorphisms as input features, we identified six major subtypes of SARS-CoV-2. The proportions of the clusters across the continents revealed distinct geographical distributions. Comprehensive analysis indicated that both genetic factors and h2022-01-01T00:00:00Z2022 Apr2026-05-19T03:11:53.664Z2025-02-19T05:05:12.029ZS-EPMC90307923545645410.3390/genes13040648