Abstract
The objective of the work described in this paper is twofold. On the one hand, the aim is to present and validate a model of Dengue fever for the Cuban case which is defined by a delay differential system. Such a model includes time-varying parameters, which are estimated by means of a method based upon Hopfield Neural Networks. This method has been successfully applied in both robotic and epidemiological models described by Ordinary Differential Equations. Therefore, on the other hand, an additional aim of this work is to assess the behaviour of this neural estimation technique with a delay differential system. Experimental results show the ability of the estimator to deal with systems with delays, as well as plausible parameter estimations, which lead to predictions that are coherent with actual data.
This work has been partially supported by the Agencia Española de Cooperación Internacional para el Desarrollo (AECID), project no. D/017218/08. Thanks are due to Dr. Hector de Arazoza for his useful suggestions and, in particular, for providing the model of Dengue epidemics.
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García-Garaluz, E., Atencia, M., García-Lagos, F., Joya, G., Sandoval, F. (2009). System Identification of Dengue Fever Epidemics in Cuba. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_113
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DOI: https://doi.org/10.1007/978-3-642-02478-8_113
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