Abstract
The analysis of animal trade movements plays a crucial role in understanding the spreading of zoonotic diseases in livestock. This article addresses the problem of predicting sending or receiving animal transports by farms and other premises. Two recurrent neural network models are used for this task: classical Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks. Optimization of neural network weights is performed using the MOEA/D algorithm with the goal of obtaining good trade-offs between the false positive (FP) and true positive (TP) rates. The results show, that neural classifiers optimized on historical data (in this article taken from the years 2017–2019) can be used for making predictions on future data (in this article taken from the year 2020) without a serious degradation of the classification quality. In the experiments, the overall performance of the RNN model was better than that of the LSTM model, however, the LSTM performed slightly better than the RNN in the range of lower FP rates. The results of this study motivate further research on using predictive models for optimizing counter-epidemic measures, for example vaccination campaigns.
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Acknowledgment
This work was supported by the Polish National Science Centre under grant no. 2015/19/D/HS4/02574. We acknowledge that the results of this research have been achieved using the DECI resource ARIS based in Greece at the National Infrastructures for Research and Technology S.A. (GRNET) with support from the PRACE aisbl under project ID DYNNETOPT. The authors would like to acknowledge Informatica Area Prevenzione of the ASL CN1 of Piedmont for the data and useful discussion.
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Michalak, K., Giacobini, M. (2023). Multiobjective Optimization of Evolutionary Neural Networks for Animal Trade Movements Prediction. In: Emmerich, M., et al. Evolutionary Multi-Criterion Optimization. EMO 2023. Lecture Notes in Computer Science, vol 13970. Springer, Cham. https://doi.org/10.1007/978-3-031-27250-9_38
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