Comparison of routine field epidemiology and whole genome sequencing to identify tuberculosis transmission in a remote setting

Guthrie, Jennifer L.; Strudwick, Lori; Roberts, Barrie; Allen, Marie; McFadzen, J.; Roth, David; Jorgensen, Danielle; Rodrigues, Mabel; Tang, Patrick; Hanley, Brendan; Johnston, James C.; Cook, Victoria; Gardy, Jennifer L.

doi:10.1017/s0950268820000072

Cited by 9 publications

(7 citation statements)

References 37 publications

(48 reference statements)

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“…WGS has been shown to provide superior discrimination compared to other typing methods and may be more cost-effective [ 46 , 47 ]. The use of WGS may therefore support more efficient and effective contact tracing, earlier and more appropriate treatment and the initiation of focused public health interventions [ 48 , 49 ]. Suggested elements to include in an evaluation of TB WGS implementation are provided in Additional File 6 : Table S3.…”

Section: Resultsmentioning

confidence: 99%

An implementation science approach to evaluating pathogen whole genome sequencing in public health

et al. 2021

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Background Pathogen whole genome sequencing (WGS) is being incorporated into public health surveillance and disease control systems worldwide and has the potential to make significant contributions to infectious disease surveillance, outbreak investigation and infection prevention and control. However, to date, there are limited data regarding (i) the optimal models for integration of genomic data into epidemiological investigations and (ii) how to quantify and evaluate public health impacts resulting from genomic epidemiological investigations. Methods We developed the Pathogen Genomics in Public HeAlth Surveillance Evaluation (PG-PHASE) Framework to guide examination of the use of WGS in public health surveillance and disease control. We illustrate the use of this framework with three pathogens as case studies: Listeria monocytogenes, Mycobacterium tuberculosis and SARS-CoV-2. Results The framework utilises an adaptable whole-of-system approach towards understanding how interconnected elements in the public health application of pathogen genomics contribute to public health processes and outcomes. The three phases of the PG-PHASE Framework are designed to support understanding of WGS laboratory processes, analysis, reporting and data sharing, and how genomic data are utilised in public health practice across all stages, from the decision to send an isolate or sample for sequencing to the use of sequence data in public health surveillance, investigation and decision-making. Importantly, the phases can be used separately or in conjunction, depending on the need of the evaluator. Subsequent to conducting evaluation underpinned by the framework, avenues may be developed for strategic investment or interventions to improve utilisation of whole genome sequencing. Conclusions Comprehensive evaluation is critical to support health departments, public health laboratories and other stakeholders to successfully incorporate microbial genomics into public health practice. The PG-PHASE Framework aims to assist public health laboratories, health departments and authorities who are either considering transitioning to whole genome sequencing or intending to assess the integration of WGS in public health practice, including the capacity to detect and respond to outbreaks and associated costs, challenges and facilitators in the utilisation of microbial genomics and public health impacts.

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Section: Resultsmentioning

confidence: 99%

An implementation science approach to evaluating pathogen whole genome sequencing in public health

et al. 2021

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“…A further limitation was the absence of a ‘gold-standard’ validation of the predicted transmission events to confirm true links and evaluate the accuracy of the transmission reconstruction that incorporated indel polymorphisms against SNP only data. Obtaining these confirmed links is difficult as comprehensive contact investigation data are required, which are often not available for all pairs in an outbreak and can often disagree with genomic inferences (Alaridah et al 2019; Guthrie et al 2020). Nevertheless, our finding that the greatest change in the posterior probability of transmission events between the two analyses was in pairs with indel polymorphisms indicates that including these variants can increase the informative sequence diversity used to infer true transmission.…”

Section: Discussionmentioning

confidence: 99%

Characterising indel diversity in a largeMycobacterium tuberculosisoutbreak – implications for transmission reconstruction

Sobkowiak

Colijn

2022

Preprint

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Genomic sequencing of Mycobacterium tuberculosis (Mtb), the primary aetiological agent of tuberculosis (TB) in humans, has been used to understand transmission dynamics and reconstruct past outbreaks. Putative transmission events between hosts can be predicted by linking cases with low genomic variation between pathogen strains, though typically only variation in single nucleotide polymorphisms (SNPs) is used to calculate divergence. In highly clonal Mtb populations there can be many strains that appear identical by SNPs, reducing the utility of genomic data to disentangle potential transmission routes in these settings. Small insertions and deletions (indels) are found in high numbers across the Mtb genome and can be an important source of variation to increase the observed diversity in outbreaks. Here, we examine the value of including indels in the transmission reconstruction of a large Mtb outbreak in London, UK, characterised by low levels of SNP diversity between 1998 and 2013. Our results show that including indel polymorphism decreases the number of strains in the outbreak with at least one other identical sequence by 43% compared to using only SNP variation and reduces the size of largest clonal cluster by 53%. Considering both SNPs and indel polymorphisms alters the reconstructed transmission network and decreases likelihood of direct transmission between hosts with variation in indels. This work demonstrates the importance of incorporating indels into Mtb transmission reconstruction and we provide recommendations for further work to optimise the inclusion of indel diversity in such analyses.

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“…The field of molecular epidemiology for investigating recent TB transmission is rapidly evolving, with WGS increasingly being used in public health practice to detect clusters and identify and characterize recent transmission (13)(14)(15)(16). However, analysis and use of WGS, such as SNP cutoffs, is still not standardized (17,18).…”

Section: Discussionmentioning

confidence: 99%

Logically Inferred Tuberculosis Transmission (LITT): A Data Integration Algorithm to Rank Potential Source Cases

Winglee

McDaniel

Linde

et al. 2021

Front. Public Health

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Understanding tuberculosis (TB) transmission chains can help public health staff target their resources to prevent further transmission, but currently there are few tools to automate this process. We have developed the Logically Inferred Tuberculosis Transmission (LITT) algorithm to systematize the integration and analysis of whole-genome sequencing, clinical, and epidemiological data. Based on the work typically performed by hand during a cluster investigation, LITT identifies and ranks potential source cases for each case in a TB cluster. We evaluated LITT using a diverse dataset of 534 cases in 56 clusters (size range: 2–69 cases), which were investigated locally in three different U.S. jurisdictions. Investigators and LITT agreed on the most likely source case for 145 (80%) of 181 cases. By reviewing discrepancies, we found that many of the remaining differences resulted from errors in the dataset used for the LITT algorithm. In addition, we developed a graphical user interface, user's manual, and training resources to improve LITT accessibility for frontline staff. While LITT cannot replace thorough field investigation, the algorithm can help investigators systematically analyze and interpret complex data over the course of a TB cluster investigation.Code available at:https://github.com/CDCgov/TB_molecular_epidemiology/tree/1.0; https://zenodo.org/badge/latestdoi/166261171.

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Comparison of routine field epidemiology and whole genome sequencing to identify tuberculosis transmission in a remote setting

Cited by 9 publications

References 37 publications

An implementation science approach to evaluating pathogen whole genome sequencing in public health

An implementation science approach to evaluating pathogen whole genome sequencing in public health

Characterising indel diversity in a largeMycobacterium tuberculosisoutbreak – implications for transmission reconstruction

Logically Inferred Tuberculosis Transmission (LITT): A Data Integration Algorithm to Rank Potential Source Cases

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