Genomic Infectious Disease Epidemiology in Partially Sampled and Ongoing Outbreaks.

Genomic data are increasingly being used to understand infectious disease epidemiology. Isolates from a given outbreak are sequenced, and the patterns of shared variation are used to infer which isolates within the outbreak are most closely related to each other. Unfortunately, the phylogenetic trees typically used to represent this variation are not directly informative about who infected whom-a phylogenetic tree is not a transmission tree. However, a transmission tree can be inferred from a phylogeny while accounting for within-host genetic diversity by coloring the branches of a phylogeny according to which host those branches were in. Here we extend this approach and show that it can be applied to partially sampled and ongoing outbreaks. This requires computing the correct probability of an observed transmission tree and we herein demonstrate how to do this for a large class of epidemiological models. We also demonstrate how the branch coloring approach can incorporate a variable number of unique colors to represent unsampled intermediates in transmission chains. The resulting algorithm is a reversible jump Monte-Carlo Markov Chain, which we apply to both simulated data and real data from an outbreak of tuberculosis. By accounting for unsampled cases and an outbreak which may not have reached its end, our method is uniquely suited to use in a public health environment during real-time outbreak investigations. We implemented this transmission tree inference methodology in an R package called TransPhylo, which is freely available from https://github.com/xavierdidelot/TransPhylo.

Bayesian inference of infectious disease transmission from whole-genome sequence data.

Genomics is increasingly being used to investigate disease outbreaks, but an important question remains unanswered--how well do genomic data capture known transmission events, particularly for pathogens with long carriage periods or large within-host population sizes? Here we present a novel Bayesian approach to reconstruct densely sampled outbreaks from genomic data while considering within-host diversity. We infer a time-labeled phylogeny using Bayesian evolutionary analysis by sampling trees (BEAST), and then infer a transmission network via a Monte Carlo Markov chain. We find that under a realistic model of within-host evolution, reconstructions of simulated outbreaks contain substantial uncertainty even when genomic data reflect a high substitution rate. Reconstruction of a real-world tuberculosis outbreak displayed similar uncertainty, although the correct source case and several clusters of epidemiologically linked cases were identified. We conclude that genomics cannot wholly replace traditional epidemiology but that Bayesian reconstructions derived from sequence data may form a useful starting point for a genomic epidemiology investigation.

Diagnostic testing for vaccinomics: is the regulatory approval framework adequate? A comparison of Canada, the United States, and Europe.

Vaccinomics aims to integrate variability information from multiple levels of the biological hierarchy from genome to proteome to metabolome, and ways in which these biological parts interact with each other and the environment. Vaccinomics holds significant promise as a new public health tool in designing safer and more effective vaccines for both developed and developing countries. Vaccinomics tests that are envisioned to be used in tandem with vaccine-based health interventions could permit an informed forecast of individual and subpopulation variations in immune responses to vaccines, reduce adverse effects, and contribute to a foundation for rational and directed use of vaccines. A proactive, multidisciplinary engagement with vaccinomics is now timely and much needed in order to develop regulations that best ensure the protection of the public and promote the transition of vaccinomics innovations from discovery to real-life public health applications. This article examines and compares the regulatory oversight of vaccinomics tests in Canada, the United States, and Europe. Recent trends in these jurisdictions suggest that regulatory agencies view personalized genomics/omics medicine, such as vaccinomics, as a desirable goal. At the same time, proposals to increase oversight could impact progress in the field and affect the availability of vaccinomics tests in public health practice and the diagnostic test market. The comparative analysis of vaccinomics in three jurisdictions presented in this article highlights both the convergence and divergence of regulatory oversight. In a rapidly emerging field such as vaccinomics that is pivotal for global public health, achieving better harmonization of policies may be an advantageous target, while ensuring that symmetry exists between the goals of public safety and promoting public health innovation. We suggest it is now timely to proactively initiate a constructive dialogue among all stakeholders (publics, policymakers, researchers, private sector, governments) to foster the development of appropriately targeted regulatory policies in this field.