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

Samuel Bloomfield,Philip Carter,Timothy Vaughan,David Hayman,Jonathan Marshall,Jackie Benschop,Patrick Biggs,Nigel French,Li Chen

doi:10.1371/journal.pone.0214169

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.

Highlights

Ancestral state reconstruction models estimate the ancestral states of organisms based on their evolutionary history
The discrete trait analysis (DTA) and structured coalescent (SC) models are ancestral state reconstruction models that were designed to estimate the ancestral state of a group of organisms based on their evolutionary history [13,14]
In this study we demonstrated using simulated outbreaks and a previously described salmonellosis outbreak that neither of these models could accurately estimate known population and transmission parameters for these outbreaks

Summary

Introduction

Ancestral state reconstruction models estimate the ancestral states of organisms based on their evolutionary history. The discrete trait analysis (DTA) and structured coalescent (SC) models are ancestral state reconstruction models. They have been used to predict ancestral migrations [3,4], movement of agents between hosts [5,6], and factors that influenced these changes, e.g. geographical changes [7]. These models represent the main Bayesian ancestral reconstruction models, but variations on these models have been developed to be more computational efficient [8,9] or to take into consideration within-host diversity [10]. In this study we focused on two Bayesian models that estimate ancestral states alongside phylogeny

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