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SARS-CoV-2 entry into and evolution within a skilled nursing facility
Nicole R Sexton1,2, Parker J Cline1, Emily N Gallichotte1
1Department of Microbiology, Immunology and Pathology, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO, 80523, USA.
Scientific Reports
|July 19, 2023
Summary
This study tracked SARS-CoV-2 evolution during a skilled nursing facility outbreak. A single introduction led to widespread infection, highlighting the importance of understanding viral evolution in specific settings.
Area of Science:
- Virology
- Epidemiology
- Genomics
Background:
- Coronaviridae, including SARS-CoV-2, significantly impact aging populations.
- SARS-CoV-2 is adapting to human hosts, with ongoing research into its evolution.
- Previous studies often examined SARS-CoV-2 evolution across broad populations.
Purpose of the Study:
- To investigate SARS-CoV-2 evolution within a single, epidemiologically linked outbreak at a skilled nursing facility.
- To analyze viral evolution from the initial introduction of the pathogen.
- To understand consensus and intra-host variation during transmission.
Main Methods:
- Genomic sequencing of SARS-CoV-2 samples from individuals within a skilled nursing facility outbreak.
- Analysis of viral sequences at both consensus and intra-host variation levels.
- Tracking of pathogen introduction and transmission dynamics within the facility.
Main Results:
- Multiple introductions of SARS-CoV-2 occurred without sustained interfacility spread.
- A single introduction event resulted in a large-scale, rapid infection within the facility.
- This dominant genotype persisted throughout the outbreak period.
- Analysis revealed both consensus and intra-host genetic variability.
Conclusions:
- Understanding SARS-CoV-2 variability during transmission is crucial for disease spread insights.
- Findings can inform improved mitigation strategies for infectious diseases in congregate settings.
- Focusing on specific outbreak dynamics provides valuable evolutionary data.
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