Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks

Abstract

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.

Simultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks

“…For comparison, we analysed the same datasets with the Outbreaker package in R [8], which uses the assumption of mutation at transmission, and with the TransPhylo package [7, 24], which requires input of a phylogenetic tree that we created in BEAST v2 [19] with a constant population coalescent model and Jukes-Cantor substitution model. Both Outbreaker and TransPhylo require input of a generation and sampling interval distribution, for which we supplied the distributions used to simulate the data.…”

“…This is implemented in models using genetic models based on pairwise genetic distances [8, 16] and with a model assuming coalescence at transmission [45], but is considered a major challenge with a within-host coalescent model [46]. Multiple index cases could also reflect unobserved hosts in the outbreak itself, recently addressed by Didelot et al [24] in their two-step approach of first inferring a phylogenetic and then a transmission tree.…”

“…In addition, the data were analysed with the Outbreaker [8] and TransPhylo [7, 24] packages in R, with the correct generation and sampling intervals, in Outbreaker discretized in steps of 0.1 year. Outbreaker analyses consisted of a 200,000 iterations burn-in, followed by 2000 samples with thinning interval of 500.…”

Genomic epidemiology of multidrug‐resistant Gram‐negative organisms

“…Moreover, the potential for multiple acquisitions in high‐transmission settings has been documented and can also confound transmission inference . While there are both analytical and sequencing‐based strategies to deal with these issues, they result in decreased power and increased cost, respectively . One solution to combat decreased power of genetic inferences is to supplement transmission‐ inference pipelines with comprehensive location or contact‐tracing data .…”

“…One solution to combat decreased power of genetic inferences is to supplement transmission‐ inference pipelines with comprehensive location or contact‐tracing data . A second solution is to apply methods that account for potential intrahost diversity and uncertainty surrounding the potential transmission, events when constructing transmission networks …”