Real-time public health communication of local SARS-CoV-2 genomic epidemiology - PubMed (original) (raw)

. 2020 Aug 21;18(8):e3000869.

doi: 10.1371/journal.pbio.3000869. eCollection 2020 Aug.

Cole G Jensen 1, Peter Neugebauer 1, Mary E Petrone 1, Mario Peña-Hernández 2, Isabel M Ott 1, Anne L Wyllie 1, Tara Alpert 1, Chantal B F Vogels 1, Joseph R Fauver 1, Nathan D Grubaugh 1, Anderson F Brito 1

Affiliations

Real-time public health communication of local SARS-CoV-2 genomic epidemiology

Chaney C Kalinich et al. PLoS Biol. 2020.

Abstract

Genomic epidemiology can provide a unique, real-time understanding of SARS-CoV-2 transmission patterns. Yet the potential for genomic analyses to guide local policy and community-based behavioral decisions is limited because they are often oriented towards specially trained scientists and conducted on a national or global scale. Here, we propose a new paradigm: Phylogenetic analyses performed on a local level (municipal, county, or state), with results communicated in a clear, timely, and actionable manner to strengthen public health responses. We believe that presenting results rapidly, and tailored to a non-expert audience, can serve as a template for effective public health response to COVID-19 and other emerging viral diseases.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1

Fig 1. Open Science and Communication.

(A) The COVIDTracker home page (

coviditrackerct.com

). Using an easy to maintain online platform, background information and regular reports are shared with the general public. (B) Our workflow from data generation, to rapid testing and communication, following principles of Open Science. (C) A maximum-likelihood phylogenetic tree showing 1048 SARS-CoV-2 genomes, including the first 200 SARS-CoVi2 genomes sequenced by our group. (D) The phylogeographic reconstruction of the genomes shown in (C) reveals the potential sources of introductions and patterns of viral spread within Connecticut. The size of the nodes represents the number of genomes sampled in the corresponding area, and the edges depict viral spread events between these areas. The results, which are embedded into our reports (available on

covidtrackerct.com/portfolio/current/

), can also be viewed standalone by loading output files hosted on GitHub (

https://github.com/grubaughlab/CT-SARS-CoV-2

).

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