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Tracking the genetic diversity of SARS-CoV-2 variants in Nicaragua throughout the COVID-19 Pandemic.

Gerald Vásquez AlemánCristhiam CerpasJose Guillermo JuarezHanny MoreiraSonia ArguelloJosefina ColomaEva HarrisAubree GordonShannon N BennettAngel Balmaseda
Published in: bioRxiv : the preprint server for biology (2024)
The global circulation of SARS-CoV-2 has been extensively documented, yet the dynamics within Central America, particularly Nicaragua, remain underexplored. This study characterizes the genomic diversity of SARS-CoV-2 in Nicaragua from March 2020 through December 2022, utilizing 1064 genomes obtained via next-generation sequencing. These sequences were selected nationwide and analyzed for variant classification, lineage predominance, and phylogenetic diversity. We employed both Illumina and Oxford Nanopore Technologies for all sequencing procedures. Results indicated a temporal and spatial shift in dominant lineages, initially from B.1 and A.2 in early 2020 to various Omicron subvariants towards the study's end. Significant lineage shifts correlated with changes in COVID-19 positivity rates, underscoring the epidemiological impact of variant dissemination. The comparative analysis with regional data underscored the low diversity of circulating lineages in Nicaragua and their delayed introduction compared to other countries in the Central American region. The study also linked specific viral mutations with hospitalization rates, emphasizing the clinical relevance of genomic surveillance. This research advances the understanding of SARS-CoV-2 evolution in Nicaragua and provide valuable information regarding its genetic diversity for public health officials in Central America. We highlight the critical role of ongoing genomic surveillance in identifying emergent lineages and informing public health strategies.
Keyphrases
  • sars cov
  • public health
  • genetic diversity
  • respiratory syndrome coronavirus
  • copy number
  • single cell
  • coronavirus disease
  • machine learning
  • deep learning
  • dna methylation
  • gene expression
  • big data
  • health information