Transmission analysis of a large tuberculosis outbreak in London: a mathematical modelling study using genomic data

Inference of Infectious Disease Transmission through a Relaxed Bottleneck Using Multiple Genomes Per Host

In recent times, pathogen genome sequencing has become increasingly used to investigate infectious disease outbreaks. When genomic data is sampled densely enough amongst infected individuals, it can help resolve who infected whom. However, transmission analysis cannot rely solely on a phylogeny of the genomes but must account for the within-host evolution of the pathogen, which blurs the relationship between phylogenetic and transmission trees. When only a single genome is sampled for each host, the uncertainty about who infected whom can be quite high. Consequently, transmission analysis based on multiple genomes of the same pathogen per host has a clear potential for delivering more precise results, even though it is more laborious to achieve. Here, we present a new methodology that can use any number of genomes sampled from a set of individuals to reconstruct their transmission network. Furthermore, we remove the need for the assumption of a complete transmission bottleneck. We use simulated data to show that our method becomes more accurate as more genomes per host are provided, and that it can infer key infectious disease parameters such as the size of the transmission bottleneck, within-host growth rate, basic reproduction number, and sampling fraction. We demonstrate the usefulness of our method in applications to real datasets from an outbreak of Pseudomonas aeruginosa amongst cystic fibrosis patients and a nosocomial outbreak of Klebsiella pneumoniae.

Phylogenetic inference of pneumococcal transmission from cross-sectional data, a pilot study

Background: Inference on pneumococcal transmission has mostly relied on longitudinal studies which are costly and resource intensive. Therefore, we conducted a pilot study to test the ability to infer who infected whom from cross-sectional pneumococcal sequences using phylogenetic inference. Methods: Five suspected transmission pairs, for which there was epidemiological evidence of who infected whom, were selected from a household study. For each pair, Streptococcus pneumoniae full genomes were sequenced from nasopharyngeal swabs collected on the same day. The within-host genetic diversity of the pneumococcal population was used to infer the transmission direction and then cross-validated with the direction suggested by the epidemiological records. Results: The pneumococcal genomes clustered into the five households from which the samples were taken. The proportion of concordantly inferred transmission direction generally increased with increasing minimum genome fragment size and single nucleotide polymorphisms. We observed a larger proportion of unique polymorphic sites in the source bacterial population compared to that of the recipient in four of the five pairs, as expected in the case of a transmission bottleneck. The only pair that did not exhibit this effect was also the pair that had consistent discordant transmission direction compared to the epidemiological records suggesting potential misdirection as a result of false-negative sampling. Conclusions: This pilot provided support for further studies to test if the direction of pneumococcal transmission can be reliably inferred from cross-sectional samples if sequenced with sufficient depth and fragment length.

An overview of tuberculosis outbreaks reported in the years 2011-2020

BACKGROUND

In many countries tuberculosis (TB) remains a highly prevalent disease and a major contributor to infectious disease mortality. The fight against TB requires surveillance of the population of strains circulating worldwide and the analysis of the prevalence of certain strains in populations. Nowadays, whole genome sequencing (WGS) allows for accurate tracking of TB transmission. Currently, there is a lack of a comprehensive summary of the characteristics of TB outbreaks.

METHODS

We systematically analyzed studies reporting TB outbreaks worldwide, monitored through WGS of Mycobacterium tuberculosis. We 1) mapped the reported outbreaks from 2011- 2020, 2) estimated the average size of the outbreaks, 3) indicated genetic lineages causing the outbreaks, and 4) determined drug-resistance patterns of M. tuberculosis strains involved in the outbreaks.

RESULTS

Most data originated from Europe, Asia, and North America. We found that TB outbreaks were reported throughout the globe, on all continents, and in countries with both high and low incidences. The detected outbreaks contained a median of five M. tuberculosis isolates. Most strains causing the outbreaks belonged to lineage four, more rarely to lineage two. Reported outbreak isolates were often drug resistant.

CONCLUSIONS

We conclude that more WGS surveillance of M. tuberculosis outbreaks is needed. Globally standardized procedures might improve the control of M. tuberculosis infections.

Methods Combining Genomic and Epidemiological Data in the Reconstruction of Transmission Trees: A Systematic Review

In order to better understand transmission dynamics and appropriately target control and preventive measures, studies have aimed to identify who-infected-whom in actual outbreaks. Numerous reconstruction methods exist, each with their own assumptions, types of data, and inference strategy. Thus, selecting a method can be difficult. Following PRISMA guidelines, we systematically reviewed the literature for methods combing epidemiological and genomic data in transmission tree reconstruction. We identified 22 methods from the 41 selected articles. We defined three families according to how genomic data was handled: a non-phylogenetic family, a sequential phylogenetic family, and a simultaneous phylogenetic family. We discussed methods according to the data needed as well as the underlying sequence mutation, within-host evolution, transmission, and case observation. In the non-phylogenetic family consisting of eight methods, pairwise genetic distances were estimated. In the phylogenetic families, transmission trees were inferred from phylogenetic trees either simultaneously (nine methods) or sequentially (five methods). While a majority of methods (17/22) modeled the transmission process, few (8/22) took into account imperfect case detection. Within-host evolution was generally (7/8) modeled as a coalescent process. These practical and theoretical considerations were highlighted in order to help select the appropriate method for an outbreak.

Genomic Epidemiology Analysis of Infectious Disease Outbreaks Using TransPhylo

Comparing the pathogen genomes from several cases of an infectious disease has the potential to help us understand and control outbreaks. Many methods exist to reconstruct a phylogeny from such genomes, which represents how the genomes are related to one another. However, such a phylogeny is not directly informative about transmission events between individuals. TransPhylo is a software tool implemented as an R package designed to bridge the gap between pathogen phylogenies and transmission trees. TransPhylo is based on a combined model of transmission between hosts and pathogen evolution within each host. It can simulate both phylogenies and transmission trees jointly under this combined model. TransPhylo can also reconstruct a transmission tree based on a dated phylogeny, by exploring the space of transmission trees compatible with the phylogeny. A transmission tree can be represented as a coloring of a phylogeny where each color represents a different host of the pathogen, and TransPhylo provides convenient ways to plot these colorings and explore the results. This article presents the basic protocols that can be used to make the most of TransPhylo. © 2021 The Authors. Basic Protocol 1: First steps with TransPhylo Basic Protocol 2: Simulation of outbreak data Basic Protocol 3: Inference of transmission Basic Protocol 4: Exploring the results of inference.