Using genetic data to identify transmission risk factors: Statistical assessment and application to tuberculosis transmission

Abstract

Identifying host factors that influence infectious disease transmission is an important step toward developing interventions to reduce disease incidence. Recent advances in methods for reconstructing infectious disease transmission events using pathogen genomic and epidemiological data open the door for investigation of host factors that affect onward transmission. While most transmission reconstruction methods are designed to work with densely sampled outbreaks, these methods are making their way into surveillance studies, where the fraction of sampled cases with sequenced pathogens could be relatively low. Surveillance studies that use transmission event reconstruction then use the reconstructed events as response variables (i.e., infection source status of each sampled case) and use host characteristics as predictors (e.g., presence of HIV infection) in regression models. We use simulations to study estimation of the effect of a host factor on probability of being an infection source via this multi-step inferential procedure. Using TransPhylo—a widely-used method for Bayesian estimation of infectious disease transmission events—and logistic regression, we find that low sensitivity of identifying infection sources leads to dilution of the signal, biasing logistic regression coefficients toward zero. We show that increasing the proportion of sampled cases improves sensitivity and some, but not all properties of the logistic regression inference. Application of these approaches to real world data from a population-based TB study in Botswana fails to detect an association between HIV infection and probability of being a TB infection source. We conclude that application of a pipeline, where one first uses TransPhylo and sparsely sampled surveillance data to infer transmission events and then estimates effects of host characteristics on probabilities of these events, should be accompanied by a realistic simulation study to better understand biases stemming from imprecise transmission event inference.

Effect of compensatory evolution in the emergence and transmission of rifampicin-resistant Mycobacterium tuberculosis in Cape Town, South Africa: a genomic epidemiology study

“…Although previous evaluations have shown that TransPhylo has a good performance identifying tuberculosis transmitters, this method still presents some limitations. 30 One of these is that transmitters might not be identified as such if the individuals they transmitted to are unsampled. In our study, we applied two correcting strategies to account for such a potential bias: we limited the multivariable analyses to individuals diagnosed before the last 2 years of the study (before 2016, thus allowing an incubation time of 2 years), and adjusted the regression models by diagnosis date.…”

DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction

“…The stochasticity of parameterization further discourages over-fitting of the model to the simulated data. Beginning with a single infected individual, rate of transmission of infection to susceptible individuals varied according to two scenarios—1) transmission of an acute infectious respiratory virus (ARI) or 2) transmission of Mycobacterium tuberculosis (TB) among populations of HIV-infected or HIV-uninfected individuals, as described in [ 38 ]. Specific paramaters for each outbreak scenario are described in more depth below.…”

“…These groups were further split into static and dynamic transmitting groups. Based on a reported average of 53% percent of infection recipients harboring HIV [ 39 ], infection rate for HIV risk groups A and D were 0.53/2 = 26.5%. Remaining non-HIV-infected risk groups B and E comprised the remaining 47% (equally represented).…”

Genomic approaches to tuberculosis management and control

“…For example, identifying the central role of incarceration in community transmission of TB in certain settings [54,55]. The limitation of transmission trees is that it cannot be applied to every transmission cluster as a minimum number of cases and SNPs are needed among others [56]. Thus whole genome sequencing is enabling the transition from a cluster-based molecular epidemiology to an individual-based one.…”