Methods Combining Genomic and Epidemiological Data in the Reconstruction of Transmission Trees: A Systematic Review

Abstract

In order to better understand transmission dynamics and appropriately target control and preventive measures, studies have aimed to identify who-infected-whom in actual outbreaks. Numerous reconstruction methods exist, each with their own assumptions, types of data, and inference strategy. Thus, selecting a method can be difficult. Following PRISMA guidelines, we systematically reviewed the literature for methods combing epidemiological and genomic data in transmission tree reconstruction. We identified 22 methods from the 41 selected articles. We defined three families according to how genomic data was handled: a non-phylogenetic family, a sequential phylogenetic family, and a simultaneous phylogenetic family. We discussed methods according to the data needed as well as the underlying sequence mutation, within-host evolution, transmission, and case observation. In the non-phylogenetic family consisting of eight methods, pairwise genetic distances were estimated. In the phylogenetic families, transmission trees were inferred from phylogenetic trees either simultaneously (nine methods) or sequentially (five methods). While a majority of methods (17/22) modeled the transmission process, few (8/22) took into account imperfect case detection. Within-host evolution was generally (7/8) modeled as a coalescent process. These practical and theoretical considerations were highlighted in order to help select the appropriate method for an outbreak.

Unveiling invisible farm‐to‐farm PRRSV‐2 transmission links and routes through transmission tree and network analysis

“…To infer transmission networks from the MCC trees, we conducted transmission tree analysis in TransPhylo 1.4.5 in R (R Core Team, 2019). Among several available packages for transmission tree inference, TransPhylo had the most appropriate assumptions and options that best aligned with our data, notably in that it allows for incomplete sampling of cases and ongoing (endemic) outbreak scenarios (Didelot et al, 2017;Duault et al, 2022). Given a time-scaled phylogeny, molecular clock, and assuming a stochastic branching epidemiological process, TransPhylo uses a Bayesian approach to create a network indicating who infected whom and inferring the number of unsampled individuals in the transmission chain connecting a pair of sampled cases (Didelot et al, 2017).…”

Integrating Transmission Dynamics and Pathogen Evolution Through a Bayesian Approach

“…This gap underscores a need in transmission inference: a model that can account for transmission bottleneck, complete transmission history, and within-host diversity, as well as provide uncertainty intervals for its estimates. See also recent work by Duault, Durand, andCanini 2022 andSobkowiak et al 2022 for a more detailed review and comparison of the available methods.…”

Sorting out assortativity: when can we assess the contributions of different population groups to epidemic transmission?

“…These approaches have their own limitations. For instance, accurately reconstructing transmission chains is challenging [16] and even with perfectly known transmission chains, transmission assortativity estimation may be impeded by differences in group sizes and group-level saturation ( i.e . the depletion of susceptibles).…”