Tuberculosis outbreak investigation using phylodynamic analysis

Abstract

AbstractThe 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 the epidemiological dynamics underlying two M. tuberculosis outbreaks using phylodynamic methods. The first outbreak occurred in the Swiss city of Bern (1993-2012) 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. We estimate an evolutionary rate of 0.0039 per single nucleotide polymorphism (SNP) per year for Bern and 0.0024 per SNP per year for WTK. Nevertheless, due to its high sampling proportion (90%) the Bern outbreak allows robust estimation of epidemiological parameters despite the poor temporal signal. Conversely, there’s much uncertainty in the epidemiological estimates concerning the WTK outbreak, which has a small sampling proportion (9%). Our results suggest that both outbreaks peaked around 1990, although the Bernese outbreak was only detected in 1993, and the WTK outbreak around 2004. 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.