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

Outbreaks of tuberculosis (TB) - such as the large isoniazid-resistant outbreak centred on London, UK, which originated in 1995 - provide excellent opportunities to model transmission of this devastating disease. Transmission chains for TB are notoriously difficult to ascertain, but mathematical modelling approaches, combined with whole-genome sequencing data, have strong potential to contribute to transmission analyses. Using such data, we aimed to reconstruct transmission histories for the outbreak using a Bayesian approach, and to use machine-learning techniques with patient-level data to identify the key covariates associated with transmission. By using our transmission reconstruction method that accounts for phylogenetic uncertainty, we are able to identify 21 transmission events with reasonable confidence, 9 of which have zero SNP distance, and a maximum distance of 3. Patient age, alcohol abuse and history of homelessness were found to be the most important predictors of being credible TB transmitters

Declaring a tuberculosis outbreak over with genomic epidemiology

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 Mycobacterium tuberculosis 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

Sustainable Surveillance of Neglected Tropical Diseases for the Post-Elimination Era.

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

Declaring a tuberculosis outbreak over with genomic epidemiology

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 Mycobacterium tuberculosis 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

Interpreting whole genome sequencing for investigating tuberculosis transmission: a systematic review

BACKGROUND: 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. 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. RESULTS: 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. CONCLUSIONS: 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

How can we use whole genome sequencing and mathematical modelling to understand tuberculosis transmission and inform our public health practices?

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

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

Supplementary Material and Supporting Data for ‘Transmission analysis of a large TB outbreak in London: a mathematical modelling study using genomic data’ as published in Microbial Genomics. The phylogenetic tree-building software beast2 (version 2.6.1) was used to build timed phylogenetic trees. The beast xml file is provided here, along with the H37Rv NC000962.3 reference genome

Case study of physiotherapy treatment of a patient with posttraumatic paresis n. peroneus communis dx.

Title: Case study of physiotherapy treatment of patient with the posttraumatic paresis n. peroneus communis dx. Objectives: Gain of theoretical knowledge about peripheral paresis, especially peripheral paresis n. peroneus communis dx. Subsequent case study formulation of patient with selected diagnosis made during coherent scholarly practice. Methods: The theoretical part of this bachelor thesis contains theoretical knowledge about anatomy of peripheral nervous system of the lower extremities, the clinical image of the peripheral paresis ane the treatment with sequential therapy. Results: Increase of muscle strenght in weakened muscels, improvement of movement range, elimination of reflective changes and restoration of joint play. Conclusion: Indikation of physiotherapy is very important in the treatment of peripheral paresis. Keywords: peripheral paresis, n.peroneus, physiotherap

Additional file 2: of Interpreting whole genome sequencing for investigating tuberculosis transmission: a systematic review

Appendix B. Pre-determined data items for extraction. (DOCX 16 kb