School of BioSciences - Research Publications
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ItemReconstructing foot-and-mouth disease outbreaks: a methods comparison of transmission network models(NATURE PORTFOLIO, 2019-03-18)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.
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ItemTransmission network reconstruction for foot-and-mouth disease outbreaks incorporating farm-level covariates(PUBLIC LIBRARY SCIENCE, 2020-07-15)Transmission network modelling to infer ‘who infected whom’ in infectious disease outbreaks is a highly active area of research. Outbreaks of foot-and-mouth disease have been a key focus of transmission network models that integrate genomic and epidemiological data. The aim of this study was to extend Lau’s systematic Bayesian inference framework to incorporate additional parameters representing predominant species and numbers of animals held on a farm. Lau’s Bayesian Markov chain Monte Carlo algorithm was reformulated, verified and pseudo-validated on 100 simulated outbreaks populated with demographic data Japan and Australia. The modified model was then implemented on genomic and epidemiological data from the 2010 outbreak of foot-and-mouth disease in Japan, and outputs compared to those from the SCOTTI model implemented in BEAST2. The modified model achieved improvements in overall accuracy when tested on the simulated outbreaks. When implemented on the actual outbreak data from Japan, infected farms that held predominantly pigs were estimated to have five times the transmissibility of infected cattle farms and be 49% less susceptible. The farm-level incubation period was 1 day shorter than the latent period, the timing of the seeding of the outbreak in Japan was inferred, as were key linkages between clusters and features of farms involved in widespread dissemination of this outbreak. To improve accessibility the modified model has been implemented as the R package ‘BORIS’ for use in future outbreaks.
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ItemCosting the Morbidity and Mortality Consequences of Zoonoses Using Health-Adjusted Life Years(WILEY, 2016-10)Governments are routinely involved in the biosecurity of agricultural and food imports and exports. This involves controlling the complex ongoing threat of the broad range of zoonoses: endemic, exotic and newly emerging. Policy-related decision-making in these areas requires accurate information and predictions concerning the effects and potential impacts of zoonotic diseases. The aim of this article was to provide information concerning the development and use of utility-based tools, specifically disability-adjusted life years (DALYs), for measuring the burden on human disease (morbidity and mortality) as a consequence of zoonotic infections. Issues and challenges to their use are also considered. Non-monetary utility approaches that are reviewed in this paper form one of a number of tools that can be used to estimate the monetary and non-monetary 'cost' of morbidity- and mortality-related consequences. Other tools derive from cost-of-illness, willingness-to-pay and multicriteria approaches. Utility-based approaches are specifically designed to capture the pain, suffering and loss of functioning associated with diseases, zoonotic and otherwise. These effects are typically complicated to define, measure and subsequently 'cost'. Utility-based measures will not be able to capture all of the effects, especially those that extend beyond the health sector. These will more normally be captured in financial terms. Along with other uncommon diseases, the quality of the relevant epidemiological data may not be adequate to support the estimation of losses in utility as a result of zoonoses. Other issues in their use have been identified. New empirical studies have shown some success in addressing these issues. Other issues await further study. It is concluded that, bearing in mind all caveats, utility-based methods are important tools in assessing the magnitude of the impacts of zoonoses in human disease. They make an important contribution to decision-making and priority setting across all sectors. In doing so, they highlight the relative importance of the burden of zoonotic disease globally.