'SEEDY' (Simulation of Evolutionary and Epidemiological Dynamics): An R Package to Follow Accumulation of Within-Host Mutation in Pathogens

• Type of Material: Article
• Authors: Colin J. Worby, Harvard University; Timothy Read, Emory University
• Language: English
• Date: 2015-06-15
• Publisher: Public Library of Science
• Publication Version: Final Published Version
• Copyright Statement: © 2015 Worby, Read.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pone.0129745
• Title of Journal or Parent Work: PLoS ONE
• ISSN: 1932-6203
• Volume: 10
• Issue: 6
• Start Page: e0129745
• End Page: e0129745
• Grant/Funding Information: This work was supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number U54GM088558.
• Keywords: MODELS; Phylogenetic analysis; Science & Technology - Other Topics; Evolutionary genetics; Genome evolution; OUTBREAK; Infectious disease epidemiology; TREES; Simulation and modeling; Science & Technology; DISEASE TRANSMISSION; Disease dynamics; Genomic libraries; VIRUS; Multidisciplinary Sciences; Pathogens; STAPHYLOCOCCUS-AUREUS
• Research Categories: Health Sciences, Epidemiology; Biology, Virology; Biology, Genetics

Abstract

Genome sequencing is an increasingly common component of infectious disease outbreak investigations. However, the relationship between pathogen transmission and observed genetic data is complex, and dependent on several uncertain factors. As such, simulation of pathogen dynamics is an important tool for interpreting observed genomic data in an infectious disease outbreak setting, in order to test hypotheses and to explore the range of outcomes consistent with a given set of parameters. We introduce 'seedy', an R package for the simulation of evolutionary and epidemiological dynamics (http://cran.r-project.org/web/packages/seedy/). Our software implements stochastic models for the accumulation of mutations within hosts, as well as individual-level disease transmission. By allowing variables such as the transmission bottleneck size, within-host effective population size and population mixing rates to be specified by the user, our package offers a flexible framework to investigate evolutionary dynamics during disease outbreaks. Furthermore, our software provides theoretical pairwise genetic distance distributions to provide a likelihood of personto-person transmission based on genomic observations, and using this framework, implements transmission route assessment for genomic data collected during an outbreak. Our open source software provides an accessible platform for users to explore pathogen evolution and outbreak dynamics via simulation, and offers tools to assess observed genomic data in this context.