Molecular Epidemiological Investigations of Localized SARS-CoV-2 Outbreaks-Utility of Public Algorithms

The recent rapid expansion of targeted viral sequencing approaches in conjunction with available bioinformatics have provided an effective platform for studying severe acute respiratory syndrome coronavirus-2 (CoV-2) virions at the molecular level. These means can be adapted to the field of viral mo...

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Bibliographic Details
Published in:Epidemiolgia (Basel, Switzerland) Switzerland), 2022-09, Vol.3 (3), p.402-411
Main Authors: Bilal, Mahmood Y, Klutts, James S
Format: Article
Language:English
Subjects:
Algorithms
Alliances
Coronaviruses
COVID-19
Disease control
disease outbreaks
Disease transmission
Epidemics
Epidemiology
Genomes
Infections
Medical laboratories
molecular epidemiology
Mutation
Nextclade
Phylogenetics
RNA-seq
SARS-CoV-2
Severe acute respiratory syndrome coronavirus 2
Software
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Summary:The recent rapid expansion of targeted viral sequencing approaches in conjunction with available bioinformatics have provided an effective platform for studying severe acute respiratory syndrome coronavirus-2 (CoV-2) virions at the molecular level. These means can be adapted to the field of viral molecular epidemiology, wherein localized outbreak clusters can be evaluated and linked. To this end, we have integrated publicly available algorithms in conjunction with targeted RNASeq data in order to qualitatively evaluate similarity or dissimilarity between suspect outbreak strains from hospitals, or assisted living facilities. These tools include phylogenetic clustering and mutational analysis utilizing Nextclade and Ultrafast Sample placement on Existing tRee (UShER). We herein present these outbreak screening tools utilizing three case examples in the context of molecular epidemiology, along with limitations and potential future developments. We anticipate that these methods can be performed in clinical molecular laboratories equipped with CoV-2-sequencing technology.
ISSN:2673-3986
2673-3986
DOI:10.3390/epidemiologia3030031