Investigation of the validity of two Bayesian ancestral state reconstruction models for estimating Salmonella transmission during outbreaks

Abstract

Ancestral state reconstruction models use genetic data to characterize a group of organisms’ common ancestor. These models have been applied to salmonellosis outbreaks to estimate the number of transmissions between different animal species that share similar geographical locations, with animal host as the state. However, as far as we are aware, no studies have validated these models for outbreak analysis. In this study, salmonellosis outbreaks were simulated using a stochastic Susceptible-Infected-Recovered model, and the host population and transmission parameters of these simulated outbreaks were estimated using Bayesian ancestral state reconstruction models (discrete trait analysis (DTA) and structured coalescent (SC)). These models were unable to accurately estimate the number of transmissions between the host populations or the amount of time spent in each host population. The DTA model was inaccurate because it assumed the number of isolates sampled from each host population was proportional to the number of individuals infected within each host population. The SC model was inaccurate possibly because it assumed that each host population's effective population size was constant over the course of the simulated outbreaks. This study highlights the need for phylodynamic models that can take into consideration factors that influence the characteristics and behavior of outbreaks, e.g. changing effective population sizes, variation in infectious periods, intra-population transmissions, and disproportionate sampling of infected individuals.

Disentangling the role of poultry farms and wild birds in the spread of highly pathogenic avian influenza virus H5N8 in Europe

“…Bayesian phylogeographic approaches (34) are more common than structured phylodynamic approaches (like the MTBD model) to infer the transmission of lineages between different host species, their popularity being partly associated to their computational efficiency (7,35). However, one shortcoming of Bayesian phylogeographic approaches is the assumption of independence between the phylogeny and the transmission process, which can lead to loss of information (36)(37)(38). Another shortcoming is the assumption of proportionality between the sample sizes across sub-populations and the subpopulation sizes, which make it sensitive to biased sampling.…”

Quantifying the transmission of antimicrobial resistance at the human and livestock interface with genomics

“…This approach requires a well-sampled lineage of bacteria that has diverged recently enough to obtain a robust phylogeny not significantly affected by homologous recombination. These methods can be sensitive to over-sampling and undersampling of isolates from different host populations and different sampling times, as the result of a violation of the assumption in the models that the number of samples from each population reflects the proportion of colonized individuals in each population [55,56]. The demographic assumptions made by each phylogenetic model should be taken into account when selecting the samples for analysis [57].…”

A simple correction to adjust for sampling biases in phylogeographic discrete trait analysis

“…Briefly, DTA assumes i) the shape of the phylogeny is independent of migration, ii) sampling intensity in each state is proportional to the infected population in that state at the time of sampling, iii) prevalence of states can drift with states able to become fixed or extinct over time, iv) any states not sampled at the tips are not able to be inferred as ances-tral locations. These factors can lead, amongst other issues, to biased estimates of migration rates and the location inferred at internal nodes where there is unequal sampling between states [25].…”