Studentship: Multi-scale inference of foot-and-mouth disease spread in the UK and Japan
Lead Research Organisation:
THE PIRBRIGHT INSTITUTE
Department Name: UNLISTED
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Technical Summary
Studentship
In this project, large scale stochastic models of FMD spread will be further developed to incorporate recent data on contacts between sheep and cattle on farms, and viral transmission amongst vaccinated cattle. To properly quantify the uncertainties in available data and knowledge the system will be implemented in a fully Bayesian framework. Models will be developed for two scenarios: the 2001 outbreak in the UK and, in collaboration with the University of Miyazaki, the 2010 outbreak in Japan. The aim of this project is to develop and validate a suite of models that can then be used to determine optimal control strategies for future outbreaks in the UK, Japan and elsewhere.
In this project, large scale stochastic models of FMD spread will be further developed to incorporate recent data on contacts between sheep and cattle on farms, and viral transmission amongst vaccinated cattle. To properly quantify the uncertainties in available data and knowledge the system will be implemented in a fully Bayesian framework. Models will be developed for two scenarios: the 2001 outbreak in the UK and, in collaboration with the University of Miyazaki, the 2010 outbreak in Japan. The aim of this project is to develop and validate a suite of models that can then be used to determine optimal control strategies for future outbreaks in the UK, Japan and elsewhere.
Planned Impact
unavailable
Organisations
People |
ORCID iD |
| Simon Gubbins (Principal Investigator) |
Publications
Hu B
(2017)
Bayesian inference of epidemiological parameters from transmission experiments
in Scientific Reports
| Description | Analysis of transmission experiments: A Bayesian framework was developed which to analyse transmission experiments while allowing for the fact that, because of the design of the experiments, infection times and latent periods cannot be directly observed and infectious periods may also be censored. The framework was applied to published data from transmission experiments for foot-and-mouth disease virus. Where the previous analyses of these data made various assumptions in order to draw inferences, the Bayesian approach includes the unobserved infection times and latent periods and quantifies them along with all other model parameters. Drawing inferences about infection times helps identify who infected whom and can also provide insights into transmission mechanisms. Furthermore, the models were able to measure the difference between the latent periods of inoculated and contact-challenged animals and to quantify the effect vaccination has on transmission. Multiscale modelling for 2010 FMDV outbreak in Japan: both a simple between-farm and a multi-scale model were successfully fit to the outbreak data using approximate Bayesian computation-sequential Monte Carlo (ABC-SMC) methods. Both models were able to capture the outbreak data, but it was not clear which model provided a better description. Using the between-farm model to explore alternative control strategies suggested that a control policy of culling dangerous contacts could have resulted in a smaller outbreak and fewer farms and animals culled than the vaccinate-to-kill policy that was implemented. |
| Exploitation Route | The methods developed to analyse transmission experiments can be applied to other diseases which use similar experimental designs. These have been published and the software made available. |
| Sectors | Agriculture Food and Drink |
| Title | Bayesian framework to estimate epidemiological parameters using transmission experiments |
| Description | The code needed to implement the methods described in the paper are provided in both C++ and Matlab formats. |
| Type Of Technology | Software |
| Year Produced | 2017 |
| Impact | None to date |
| URL | http://www.nature.com/articles/s41598-017-17174-8 |