Tuberculosis outbreak investigation using phylodynamic analysis

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.

Keywords: Epidemic dynamics; Phylodynamic analysis; Transmission dynamics; Tuberculosis outbreak.

Fig. 1

Plot of phylogenetic Root-to-tip distance relative to sampling time (TempEst). Each dot represents one sample per data set (left: Bern, right: WTK).

Fig. 2

Bern reproduction number through time (BDSKY). The median effective reproduction number (black line) with its 95% highest posterior density (HPD) interval (shaded area). The grey bars display a histogram of the number of cases diagnosed per year.

Fig. 3

Bern maximum clade credibility tree.

Fig. 4

WTK reproduction number through time (BDSKY). The median effective reproduction number (black line) with its 95% highest posterior density (HPD) interval (shaded area). The grey bars display a histogram of the number of cases diagnosed per year.

Fig. 5

Hmong maximum clade credibility tree.