Whole genome sequencing (WGS) is becoming an important part of epidemiological investigations of infectious diseases due to greater resolution and cost reductions compared to traditional typing ...approaches. Many public health and clinical teams will increasingly use WGS to investigate clusters of potential pathogen transmission, making it crucial to understand the benefits and assumptions of the analytical methods for investigating the data. We aimed to understand how different approaches affect inferences of transmission dynamics and outline limitations of the methods.
We comprehensively searched electronic databases for studies that presented methods used to interpret WGS data for investigating tuberculosis (TB) transmission. Two authors independently selected studies for inclusion and extracted data. Due to considerable methodological heterogeneity between studies, we present summary data with accompanying narrative synthesis rather than pooled analyses.
Twenty-five studies met our inclusion criteria. Despite the range of interpretation tools, the usefulness of WGS data in understanding TB transmission often depends on the amount of genetic diversity in the setting. Where diversity is small, distinguishing re-infections from relapses may be impossible; interpretation may be aided by the use of epidemiological data, examining minor variants and deep sequencing. Conversely, when within-host diversity is large, due to genetic hitchhiking or co-infection of two dissimilar strains, it is critical to understand how it arose. Greater understanding of microevolution and mixed infection will enhance interpretation of WGS data.
As sequencing studies have sampled more intensely and integrated multiple sources of information, the understanding of TB transmission and diversity has grown, but there is still much to be learnt about the origins of diversity that will affect inferences from these data. Public health teams and researchers should combine epidemiological, clinical and WGS data to strengthen investigations of transmission.
Abstract
The World Health Organization’s (WHO’s) 2030 road map for neglected tropical diseases (NTDs) emphasizes the importance of strengthened, institutionalized “post-elimination” surveillance. The ...required shift from disease-siloed, campaign-based programming to routine, integrated surveillance and response activities presents epidemiological, logistical, and financial challenges, yet practical guidance on implementation is lacking. Nationally representative survey programs, such as demographic and health surveys (DHS), may offer a platform for the integration of NTD surveillance within national health systems and health information systems. Here, we describe characteristics of DHS and other surveys conducted within the WHO Africa region in terms of frequency, target populations, and sample types and discuss applicability for post-validation and post-elimination surveillance. Maximizing utility depends not only on the availability of improved diagnostics but also on better understanding of the spatial and temporal dynamics of transmission at low prevalence. To this end, we outline priorities for obtaining additional data to better characterize optimal post-elimination surveillance platforms.
We report an updated method for inferring the time at which an infectious disease was transmitted between persons from a time-labelled pathogen genome phylogeny. We applied the method to 48
genomes ...as part of a real-time public health outbreak investigation, demonstrating that although active tuberculosis (TB) cases were diagnosed through 2013, no transmission events took place beyond mid-2012. Subsequent cases were the result of progression from latent TB infection to active disease, and not recent transmission. This evolutionary genomic approach was used to declare the outbreak over in January 2015.
In 2020, WHO recognised the importance of strongyloidiasis alongside soil-transmitted helminths (STH) in their 2021–30 roadmap, which aspires to target Strongyloides stercoralis with preventive ...chemotherapy by use of ivermectin. Combination treatment with both albendazole, the primary drug used to treat STH, and ivermectin, would improve the efficiency of mass drug administration targeting both STH and S stercoralis. In this Personal View, we discuss the challenges and opportunities towards the development of an efficient control programme for strongyloidiasis, particularly if it is to run concurrently with STH control. We argue the need to define the prevalence threshold to implement preventive chemotherapy for S stercoralis, the target populations and optimal dosing schedules, and discuss the added benefits of a fixed-dose coformulation of ivermectin and albendazole. Implementation of an efficient control programme will require improvements to current diagnostics, and validation of new diagnostics, to target and monitor S stercoralis infections, and consideration of the challenges of multispecies diagnostics for S stercoralis and STH control. Finally, the evolution of ivermectin resistance represents a credible risk to control S stercoralis; we argue that genome-wide approaches, together with improved genome resources, are needed to characterise and prevent the emergence of resistance. Overcoming these challenges will help to reduce strongyloidiasis burden and enhance the feasibility of controlling it worldwide.
Tuberculosis (TB) remains a public health problem in cities in high-income, low-incidence countries, such as London, where it disproportionately affects particular population groups and, as such, ...more effective intervention strategies are needed. With whole genome sequencing (WGS) data being increasingly used for TB epidemiology, I investigated how WGS data alongside statistical inference and mathematical modelling can improve our understanding of transmission in these population groups. By reviewing the literature on WGS in TB epidemiology studies, I concluded that whilst genomic data can improve our understanding of TB transmission, including epidemiological data alongside is helpful for mitigating uninformative genomic data or strengthening conclusions. I then employed a statistical inference method on sequencing data from a Canadian outbreak and used the inferred transmission network to determine that the outbreak had ended, demonstrating the use of genomic epidemiology in public health. As we must analyse genomic data using bioinformatics and sometimes phylogenetic methods before we can interpret it for epidemiological purposes, I undertook bioinformatics analysis of 415 genomes from a London TB outbreak and attempted to create a timed-phylogenetic tree that could be used for genomic epidemiology inferences. However, the data proved difficult to interpret resulting in a tree with little confidence, potentially due to little variation amongst the sequences. Finally, I constructed a novel mathematical transmission model to recapitulate the London outbreak and investigate public health interventions to conclude that despite loss-to-follow-up being considered an important factor amongst the cohort anecdotally, focusing interventions on reducing loss-to-follow-up or increasing re-engagement does not significantly reduce the number of outbreak cases. Finding infectious cases early achieves the most impact. In conclusion, combining epidemiological and sequencing with novel quantitative analysis using statistical inference and transmission modelling, provides useful insight into the spread of TB in urban outbreaks and illustrates the limitations of new approaches and data.
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