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.
Highlights
Modelling the transmission network of infectious disease outbreaks is a very active research area important for informing control of transboundary and emerging infectious diseases such as Ebola haemorrhagic fever and foot-and-mouth disease
Next-generation sequencing has advanced the identification of traceable differences in pathogen genomes and thereby the resolution of our understanding of disease transmission, in some cases down to the host-to-host scale[18]. This technology is available for real-time application during outbreak responses. In this setting it is essential that epidemiological tools to guide infectious disease outbreak response are adapted to keep pace with the advances in genomics and rapid pathogen identification
To benchmark the performance of each transmission network modelling algorithm, a set of 100 foot-and-mouth disease outbreaks were simulated using the Australian Animal Disease Spread (AADIS) hybrid model[23] with corresponding genomic sequences[24] and phylogenetic trees nested within the given transmission networks[25]
Summary
Modelling the transmission network of infectious disease outbreaks is a very active research area important for informing control of transboundary and emerging infectious diseases such as Ebola haemorrhagic fever (in humans) and foot-and-mouth disease (in livestock populations). For such models to reliably inform decision-making they must be transparently validated, robust, and capable of producing accurate predictions within short data collection to inference timeframes typical of outbreak responses Several such models have recently been assessed based on outbreak datasets simulated using very similar methods to the inferential frameworks of the models themselves[3,4,5,6,7,8,9,10,11], and/or model cross-comparisons of predicted transmission networks for small clusters of infected individuals (or ‘infected premises’ in veterinary examples) such as the foot-and-mouth disease ‘Darlington cluster’ in the 2001 outbreak in the United Kingdom[7,9,10,12]. In this setting it is essential that epidemiological tools to guide infectious disease outbreak response are adapted to keep pace with the advances in genomics and rapid pathogen identification
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
