Reconstructing foot-and-mouth disease outbreaks: a methods comparison of transmission network models

Abstract

A number of transmission network models are available that combine genomic and epidemiological data to reconstruct networks of who infected whom during infectious disease outbreaks. For such models to reliably inform decision-making they must be transparently validated, robust, and capable of producing accurate predictions within the short data collection and inference timeframes typical of outbreak responses. A lack of transparent multi-model comparisons reduces confidence in the accuracy of transmission network model outputs, negatively impacting on their more widespread use as decision-support tools. We undertook a formal comparison of the performance of nine published transmission network models based on a set of foot-and-mouth disease outbreaks simulated in a previously free country, with corresponding simulated phylogenies and genomic samples from animals on infected premises. Of the transmission network models tested, Lau’s systematic Bayesian integration framework was found to be the most accurate for inferring the transmission network and timing of exposures, correctly identifying the source of 73% of the infected premises (with 91% accuracy for sources with model support >0.80). The Structured COalescent Transmission Tree Inference provided the most accurate inference of molecular clock rates. This validation study points to which models might be reliably used to reconstruct similar future outbreaks and how to interpret the outputs to inform control. Further research could involve extending the best-performing models to explicitly represent within-host diversity so they can handle next-generation sequencing data, incorporating additional animal and farm-level covariates and combining predictions using Ensemble methods and other approaches.

Reconstructing a transmission network and identifying risk factors of secondary transmissions in the 2010 foot‐and‐mouth disease outbreak in Japan

“…Lau's model has been developed to facilitate systematic integration of epidemiological and genetic data, jointly inferring the transmission network and genome of the transmitted virus, even when data on a subset of the infected population is unavailable (Lau et al, ). This approach appears to provide a highly reasonable inference of the transmission network for this outbreak, although the levels of model support were lower than those reported for similarly sized simulated outbreaks (Firestone et al, ), or in the original model development and verification studies (Lau et al, ). This difference in model support between the current study and the previous studies may be either due to the simulations in previous studies not capturing the full extent of the complexity of the real system, or the lower availability of genetic data in this real setting.…”

“…In a comparative study of transmission network models using simulated FMD outbreak data (Firestone et al, ), Lau's systematic Bayesian model was found to be the most accurate for the purpose of our investigation. Lau's model has been developed to facilitate systematic integration of epidemiological and genetic data, jointly inferring the transmission network and genome of the transmitted virus, even when data on a subset of the infected population is unavailable (Lau et al, ).…”

“…Through a review of comparisons between the features of several transmission network models (Firestone et al, ; De Maio et al, ), a systematic Bayesian transmission network model published by Lau et al () was considered as the most appropriate method in this study. This model is a Bayesian Markov Chain Monte Carlo (MCMC) framework that simultaneously and explicitly infers the transmission network and unobserved transmitted sequences by systematically integrating the available epidemiological and genetic data.…”

“…Thus, Lau's model was chosen to reconstruct the transmission network and analyse the spatial and temporal spread of this disease during the FMD epidemic despite lack of genomic data for some of affected farms. This model has recently been demonstrated to be the most accurate for inferring the transmission network and timing of exposures based on a benchmarking study that evaluated the reproducibility of transmission networks using simulated FMD outbreaks (Firestone et al, ).…”

“…This approach enables us to infer the transmission network by incorporating epidemiological and genetic data. A detailed review and benchmarking study of recently published transmission network models is available (Firestone et al, ). Estimation of the transmission network for an infectious disease outbreak enables us to understand the mechanism of disease transmission, which complements the epidemiological investigations and helps us to better understand the features of spread.…”