ArticleEpidemics2018
Tuberculosis outbreak investigation using phylodynamic analysis.
Article in Epidemics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- ClinicalFrontiers in microbiology · 2025Pooled it
- The Phylodynamic Threshold of Measurably Evolving Populations.Molecular biology and evolution · 2026Article
- Fast hospital discharge rates blur within-hospital 'transmission footprint' in bacterial genomes, as showcased with Staphylococcus aureus.PLoS computational biology · 2026Article
- Host-Driven Genetic Diversity ofTransboundary and emerging diseases · 2026Article
- Exploiting Viral DNA Genomes to Explore the Dispersal History of African Swine Fever Genotype II Lineages in Europe.Genome biology and evolution · 2025Article
- Gene conversion and duplication contribute to genetic variation in an outbreak ofMicrobial genomics · 2025Article
- How does date-rounding affect phylodynamic inference for public health?PLoS computational biology · 2025Article
- Global epidemiology of Mycobacterium tuberculosis lineage 4 insights from Ecuadorian genomic data.Scientific reports · 2025Article
- Sustained transmission over two decades of a previously unrecognised MPT64 negativeThe Lancet regional health. Western Pacific · 2024Article
- Understanding the Decline of Incident, Active Tuberculosis in People With Human Immunodeficiency Virus in Switzerland.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023Article
- Phylodynamic signatures in the emergence of community-associated MRSA.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Understanding drivers of phylogenetic clustering and terminal branch lengths distribution in epidemics ofeLife · 2022Article
- Epidemiological inference from pathogen genomes: A review of phylodynamic models and applications.Virus evolution · 2022Review
- Fundamental Identifiability Limits in Molecular Epidemiology.Molecular biology and evolution · 2021Article
- Article
- A seventeenth-century Mycobacterium tuberculosis genome supports a Neolithic emergence of the Mycobacterium tuberculosis complex.Genome biology · 2020Article
- A model of tuberculosis clustering in low incidence countries reveals more transmission in the United Kingdom than the Netherlands between 2010 and 2015.PLoS computational biology · 2020Article
- A mosaic tetracycline resistance gene tet(S/M) detected in an MDR pneumococcal CC230 lineage that underwent capsular switching in South Africa.The Journal of antimicrobial chemotherapy · 2020Article
- Whole genome sequencing of Mycobacterium tuberculosis: current standards and open issues.Nature reviews. Microbiology · 2019Review
- The molecular clock of Mycobacterium tuberculosis.PLoS pathogens · 2019Article
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Authors and funding
8 authors.
Funding
Abstract
The fast evolution of pathogenic viruses has allowed for the development of phylodynamic approaches that extract information about the epidemiological characteristics of viral genomes. Thanks to advances in whole genome sequencing, they can be applied to slowly evolving bacterial pathogens like Mycobacterium tuberculosis. In this study, we investigate and compare the epidemiological dynamics underlying two M. tuberculosis outbreaks using phylodynamic methods. Specifically, we (i) test if the outbreak data sets contain enough genetic variation to estimate short-term evolutionary rates and (ii) reconstruct epidemiological parameters such as the effective reproduction number. The first outbreak occurred in the Swiss city of Bern (1987-2012) and was caused by a drug-susceptible strain belonging to the phylogenetic M. tuberculosis Lineage 4. The second outbreak was caused by a multidrug-resistant (MDR) strain of Lineage 2, imported from the Wat Tham Krabok (WTK) refugee camp in Thailand into California. There is little temporal signal in the Bern data set and moderate temporal signal in the WTK data set. Thanks to its high sampling proportion (90%) the Bern outbreak allows robust estimation of epidemiological parameters despite the poor temporal signal. Conversely, there is much uncertainty in the epidemiological estimates concerning the sparsely sampled (9%) WTK outbreak. Our results suggest that both outbreaks peaked around 1990, although they were only recognized as outbreaks in 1993 (Bern) and 2004 (WTK). Furthermore, individuals were infected for a significantly longer period (around 9 years) in the WTK outbreak than in the Bern outbreak (4-5 years). Our work highlights both the limitations and opportunities of phylodynamic analysis of outbreaks involving slowly evolving pathogens: (i) estimation of the evolutionary rate is difficult on outbreak time scales and (ii) a high sampling proportion allows quantification of the age of the outbreak based on the sampling times, and thus allows for robust estimation of epidemiological parameters.
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