Abstract
This paper presents a method for estimation of parameters in dynamical systems, applied to a model of the HIV-AIDS epidemics in Cuba. This estimation technique, based upon artificial neural networks, has been successfully applied to robotic systems, whereas the application to epidemiological models is challenged by the possible uncertainty of the model; besides, a state variable exists that is not directly measurable. With regard to the first limitation, a model provided by experts, previously validated by statistical techniques, has been used; with respect to the second drawback, an evaluation of the unknown variable has been carried out from comparisons with other models of the development of the disease. Among the parameters that intervene in the model, three important factors have been considered: the detection rate of the disease, through the contact tracing program; the detection rate through other methods; and the rate of transition to AIDS of previously undetected infected individuals. Results are plausible, according to experts, and they support both the estimation method and the model.
This work has been partially supported by the Spanish Agencia Española de Cooperación Internacional (AECI), Projects A/2840/05 and A/6294/06, as well as the Spanish Ministerio de Educación y Ciencia, Project No. TIN2005-01359.
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Atencia, M., Joya, G., García-Garaluz, E., de Arazoza, H., Sandoval, F. (2007). Estimation of the Rate of Detection of Infected Individuals in an Epidemiological Model. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_114
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DOI: https://doi.org/10.1007/978-3-540-73007-1_114
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