EPIMODEL · Applied stochastic modelling in veterinary epidemiology
FP6 — Marie Curie Actions (Human Resources and Mobility)
- Duration
- 2007-03-01 → 2008-02-29
- EU contribution
- €107,226
- Participants
- 1
- Scheme
- EIF
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Results in brief
Final Activity Report Summary - EPIMODEL (Applied stochastic modelling in veterinary epidemiology)
A new model for the airborne spread of the virus of foot-and-mouth disease was developed, based on the so-called Lagrangian particle model. The model improved the former Gaussian model, since it was additionally applicable on hilly regions. By using the model, it was possible to identify risk areas for different animal species such as sheep, cattle and swine around an infected farm. The model took into account for the calculations weather data, atmospheric conditions and topography of landscape. The model could also be applied to other diseases with just some parameters, e.g. virus emission of infected and sensitivity of susceptible animals, having to be accordingly updated. A new stochastic model for vector-borne infectious diseases was developed on the basis of a former deterministic process model. Data from the recent Usutu virus outbreak in Austria were used to develop the process model. The Usutu virus was an arbovirus transmitted by mosquitoes which caused disease in blackbirds. The virus was first detected in Austria in 2001 and a major outbreak occurred in 2003. The main advantage of the new model was that it enabled statistical inference, including parameter estimation and assessment of uncertainty of predictions, and it allowed for enhanced sensitivity analyses. We applied the hierarchical Bayesian approach to embed the nine compartment SEIR model into a stochastic environment. Statistical analysis of the new model was made by Markov chain Monte Carlo, which was a computer intensive simulation method. We analysed the Austrian Usutu virus data that were obtained through monitoring performed between 2001 and 2005. We explored the structure of interrelationships among parameters and then calculated Bayesian estimates for model parameters and predictions. The same model would be applied to explain the dynamics of the West Nile virus in the United States of America and to predict recent bluetonge outbreaks in Central Europe.
Data: CORDIS, © European Union
Project objective
Many recent examples, such as avian flu, SARS, West-Nile virus (WNV), BSE, foot-and-mouth-disease (FMD), etc. demonstrate that infectious diseases may have serious economic and public health consequences. Modelling the spread of disease in time and space i s considered as part of decision support related to disease control. Lots of models have been developed to describe the dynamics of various epidemics. It is typical, however, that traditional dynamical models focus on average trends and pay less attention to assessing the uncertainty of the predictions.The aim of the project is to develop a methodology to assess the precision of results from existing dynamical epidemic models using modern statistical methods. This may lead to better identification of risk factors, more accurate predictions, more adequate planning of control measures, and improved ability to compare some alternatives. The project focuses on two applications: FMD and West Nile virus disease. In the host institution, there is high quality research on modelling of infectious diseases, with much experience on general methodology as well as on concrete models (rabies, FMD, phocine distemper virus, etc). I would like to contribute to this with my skills in statistics and stochastic modelling.My field within statistics is re-sampling methods, which can be used to incorporate random variability into dynamical models in a natural way. Models developed at the host institution in former projects give opportunities to extensive experimentation with these methods. The project is based on a former cooperation: we had a joint scientific seminar in veterinary epidemiology in 2003-2004. I expect from the project to become an expert in epidemic modelling and to improve at organization and management of research projects. I could use these skills in future projects in Hungary and/or in the EU. On the other hand, the host institution could make use of my expertise in statistics and stochastic modelling.
Original text from CORDIS.
Participants
- UNIVERSITY OF VETERINARY MEDICINE VIENNA · VIENNACoordinatorCity levelAustria
Links
Data: CORDIS, © European Union