Abstract
Recent years have seen the development of numerous methodologies for reconstructing transmission trees in infectious disease outbreaks from densely sampled whole genome sequence data. However, a fundamental and as of yet poorly addressed limitation of such approaches is the requirement for genetic diversity to arise on epidemiological timescales. Specifically, the position of infected individuals in a transmission tree can only be resolved by genetic data if mutations have accumulated between the sampled pathogen genomes. To quantify and compare the useful genetic diversity expected from genetic data in different pathogen outbreaks, we introduce here the concept of ‘transmission divergence’, defined as the number of mutations separating whole genome sequences sampled from transmission pairs. Using parameter values obtained by literature review, we simulate outbreak scenarios alongside sequence evolution using two models described in the literature to describe transmission divergence of ten major outbreak-causing pathogens. We find that while mean values vary significantly between the pathogens considered, their transmission divergence is generally very low, with many outbreaks characterised by large numbers of genetically identical transmission pairs. We describe the impact of transmission divergence on our ability to reconstruct outbreaks using two outbreak reconstruction tools, the R packages outbreaker and phybreak, and demonstrate that, in agreement with previous observations, genetic sequence data of rapidly evolving pathogens such as RNA viruses can provide valuable information on individual transmission events. Conversely, sequence data of pathogens with lower mean transmission divergence, including Streptococcus pneumoniae, Shigella sonnei and Clostridium difficile, provide little to no information about individual transmission events. Our results highlight the informational limitations of genetic sequence data in certain outbreak scenarios, and demonstrate the need to expand the toolkit of outbreak reconstruction tools to integrate other types of epidemiological data.
Highlights
The increasing availability of genetic sequence data has sparked an interest in using pathogen whole genome sequences to reconstruct the history of individual transmission events
To determine pathogen outbreaks for which genetic data is expected to be informative of transmission events, we introduce here the concept of ‘transmission divergence’, defined as the number of mutations separating pathogen genome sequences sampled from transmission pairs
Understanding transmission dynamics in the early stages of an infectious disease outbreak is essential for informing effective control policy
Summary
Understanding transmission dynamics in the early stages of an infectious disease outbreak is essential for informing effective control policy. Valuable insights can be gained by the reconstruction of the transmission tree, which describes the history of infectious events at the resolution of individual cases [1,2,3,4]. One begins with an underlying transmission model, attaching to this a model of sequence evolution that relates observed genetic relationships between pathogens to unobserved epidemiological relationships (i.e. transmission pairs) between infected individuals. The other approach considers outbreak reconstruction from a phylogenetic perspective, inferring unobserved historical relationships between pathogen samples to capture more complex evolutionary dynamics. Given the unprecedented level of detail of WGS data and the epidemiological insights it has provided in real-life scenarios [21,22,23], genetic analysis is clearly an indispensable tool for outbreak reconstruction
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