Supersize me: how whole-genome sequencing and big data are transforming epidemiology

Abstract

In epidemiology, the identification of 'who infected whom' allows us to quantify key characteristics such as incubation periods, heterogeneity in transmission rates, duration of infectiousness, and the existence of high-risk groups. Although invaluable, the existence of many plausible infection pathways makes this difficult, and epidemiological contact tracing either uncertain, logistically prohibitive, or both. The recent advent of next-generation sequencing technology allows the identification of traceable differences in the pathogen genome that are transforming our ability to understand high-resolution disease transmission, sometimes even down to the host-to-host scale. We review recent examples of the use of pathogen whole-genome sequencing for the purpose of forensic tracing of transmission pathways, focusing on the particular problems where evolutionary dynamics must be supplemented by epidemiological information on the most likely timing of events as well as possible transmission pathways. We also discuss potential pitfalls in the over-interpretation of these data, and highlight the manner in which a confluence of this technology with sophisticated mathematical and statistical approaches has the potential to produce a paradigm shift in our understanding of infectious disease transmission and control.

Whole-Genome Sequencing Allows for Improved Identification of Persistent Listeria monocytogenes in Food-Associated Environments

“…Since whole-genome sequencing (WGS) technology is rapidly transforming epidemiology (13) and food safety (14), we surmised that WGS could improve subtyping and empirical rules to identify persistent L. monocytogenes. WGS technology has improved food-borne disease epidemiology of Salmonella (15,16), Escherichia coli (17), and L. monocytogenes (18), including tracing a listeriosis outbreak back to a food processing facility source (19).…”

Modelling the Effects of Mass Drug Administration on the Molecular Epidemiology of Schistosomes

“…Genomic and gene flow data can likewise be used to build transmission trees, including those aimed to identify, for certain microparasites at least, the origins of any new infections (Kao et al, 2014). While these approaches may have lower resolution in metazoan parasites, due to their slower rates of molecular evolution, multi-locus and genomic approaches will allow us to approximate intraspecific phylogenies and elucidate transmission between hosts, as has been demonstrated for Ascaris .…”

“…Heterogeneity in infection patterns are biological realities and must be incorporated into models (Paterson and Viney, 2000) improving their fit to empirical data. Kao et al (2014) review the use of WGS in contact tracing models to reveal points of control and predict directions of spread of diseases for microparasites (Kao et al, 2014). They discuss the complexities associated with inferring the epidemiological dynamics of multi-host pathogens, as is often the case for NTDs.…”

“…Through applying NGS technology to parasite population studies, well designed research can reap the benefits of sequence data, such as locating regions of the genome under selection (Valentim et al, 2013), and understanding levels and directions of gene flow and infections within and between sub-populations (Kao et al, 2014). Such information will boost our capacity to model and predict the effects of MDA on disease transmission dynamics and selection and how best to maximise control success .…”

Opportunities and Challenges in Public Health Data Collection in Southern Asia: Examples from Western India and Kathmandu Valley, Nepal

“…Medical and public health research communities have recently emphasized "big data" and the development of methods to process and analyze large, complex datasets, with a growing number of research articles on bioinformatics, computational methods, and genomics [9][10][11]. Additionally, projects such as the Million Deaths Study in India use qualitative sampling and verbal autopsy to model the numbers of premature deaths for all causes among 14 million Indians residing in 2.4 million households in cities across the country [12].…”