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Review
. 2020 Mar 28;221(Suppl 3):S308-S318.
doi: 10.1093/infdis/jiz302.

Genomic Epidemiology as a Public Health Tool to Combat Mosquito-Borne Virus Outbreaks

S Pollett  1   2   3 , J R Fauver  4   5 , Irina Maljkovic Berry  1 , M Melendrez  6 , A Morrison  7 , L D Gillis  8 , M A Johansson  9 , R G Jarman  1 , N D Grubaugh  4
Affiliations
Review

Genomic Epidemiology as a Public Health Tool to Combat Mosquito-Borne Virus Outbreaks

S Pollett et al. J Infect Dis. .

Abstract

Next-generation sequencing technologies, exponential increases in the availability of virus genomic data, and ongoing advances in phylogenomic methods have made genomic epidemiology an increasingly powerful tool for public health response to a range of mosquito-borne virus outbreaks. In this review, we offer a brief primer on the scope and methods of phylogenomic analyses that can answer key epidemiological questions during mosquito-borne virus public health emergencies. We then focus on case examples of outbreaks, including those caused by dengue, Zika, yellow fever, West Nile, and chikungunya viruses, to demonstrate the utility of genomic epidemiology to support the prevention and control of mosquito-borne virus threats. We extend these case studies with operational perspectives on how to best incorporate genomic epidemiology into structured surveillance and response programs for mosquito-borne virus control. Many tools for genomic epidemiology already exist, but so do technical and nontechnical challenges to advancing their use. Frameworks to support the rapid sharing of multidimensional data and increased cross-sector partnerships, networks, and collaborations can support advancement on all scales, from research and development to implementation by public health agencies.

Keywords: arbovirus; dengue; genomic epidemiology; yellow fever.

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Conflict of interest statement

Potential conflicts of interest. All authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest.

Figures

Figure 1.
Figure 1.
Healthmap-compiled reports of locally acquired or travel-associated cases of mosquito-borne viruses. (A) Dengue virus (DENV), (B) chikungunya virus (CHIKV), (C) ZIKA virus (ZIKV), (D) West Nile virus (WNV), (E) Ross River virus ([RRV] pink), yellow fever virus ([YFV] red), Saint Louis encephalitis virus ([SLEV] blue), and Japanese encephalitis virus ([JEV] green) case alerts from August 2018 to February 2019. (F) A heat-map of all the above reports combined. Data were accessed from (www.healthmap.org, with permission) and mapped using Google Fusion Tables (https://support.google.com/fusiontables/).
Figure 2.
Figure 2.
Wet-laboratory and dry-laboratory workflow for mosquito-borne virus genomic epidemiology. (Clockwise) Sequence reads are generated by a range of short or long read sequencing platforms (or older conventional methods) and undergo quality control before assembly using a reference sequence or de novo method (or both). Assembled genomes undergo manual curation by a trained bioinformatician. Consensus genomes are aligned with reference background data from public and other repositories. Advanced analyses require recombination detection (with consideration of removal), nucleotide model selection, and phylogenetic tree inference. Annotation of phylogenetic trees with epidemiological important metadata such as time, location, host, or clinical phenotype permits phylogeographic and/or phylodynamic analyses. Phylogeography images reproduced and adapted from [17, 89], under the creative commons.
See this image and copyright information in PMC

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