| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 384844 | Expert Systems with Applications | 2012 | 7 Pages |
In case of an outbreak of foot and mouth disease, the prediction of airborne spread is an important tool for decision-makers to assess the potential risk of secondary infections. Modelling approaches such as the Gaussian dispersion or Lagrangian particle model have been established but are complex to use and the structure of the models is fixed rather than adjustable to emerging disease situations. The aim of the present study was to evaluate the application of fuzzy logic as a modelling technique based on linguistic variables. Fuzzy logic models are easy to use and to modify. Adaptations to emerging outbreaks seem feasible. Using the Gaussian dispersion model as a reference, livestock-specific fuzzy logic models were developed. In a stepwise modelling process, the input parameters of the Gaussian model were added one-by-one into the fuzzy models. On the basis of weather data and randomly allocated farms, a validation dataset with 10,000 observations was generated and used in a 10-fold cross validation to compare the two modelling approaches. A good agreement between the Gaussian dispersion and the fuzzy logic models concerning the main directions of virus spread were found. The measure of agreement ranged between 87.0% and 99.9%. Falsely classified observations occurred mostly in proximity to the boundary of virus transmission based on the Gaussian dispersion model. In conclusion, fuzzy logic determined the same risk of infection for secondary cases than the Gaussian dispersion model. Limitations to certain livestock were not observed. The inclusion of up to four input variables did not influence the results in a mentionable amount. Including additional input variables into the fuzzy models could improve its application in assessing the risk of airborne foot and mouth disease transmission furthermore.
► Linguistic variables capture complex process of virus dispersion. ► Adequate estimation of livestock at risk using fuzzy logic. ► Expert system adaptable to outbreak conditions.
