Time series: theory and methods
Time series: theory and methods
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Fuzzy Modeling for Control
Signal Analysis: Wavelets, Filter Banks, Time-Frequency Transforms and Applications
Signal Analysis: Wavelets, Filter Banks, Time-Frequency Transforms and Applications
Fuzzy Logic in Action: Applications in Epidemiology and Beyond
Fuzzy Logic in Action: Applications in Epidemiology and Beyond
Fuzzy modelling of the composting process
Environmental Modelling & Software
Dengue model described by differential inclusions
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Artificial Intelligence in Medicine
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
Estimation of respiratory parameters via fuzzy clustering
Artificial Intelligence in Medicine
Hi-index | 0.00 |
Objective: This article presents a model of a dengue and severe dengue epidemic in Colombia based on the cases reported between 1995 and 2011. Methodology: We present a methodological approach that combines multiresolution analysis and fuzzy systems to represent cases of dengue and severe dengue in Colombia. The performance of this proposal was compared with that obtained by applying traditional fuzzy modeling techniques on the same data set. This comparison was obtained by two performance measures that evaluate the similarity between the original data and the approximate signal: the mean square error and the variance accounted for. Finally, the predictive ability of the proposed technique was evaluated to forecast the number of dengue and severe dengue cases in a horizon of three years (2012-2015). These estimates were validated with a data set that was not included into the training stage of the model. Results: The proposed technique allowed the creation of a model that adequately represented the dynamic of a dengue and severe dengue epidemic in Colombia. This technique achieves a significantly superior performance to that obtained with traditional fuzzy modeling techniques: the similarity between the original data and the approximate signal increases from 21.13% to 90.06% and from 18.90% to 76.83% in the case of dengue and severe dengue, respectively. Finally, the developed models generate plausible predictions that resemble validation data. The difference between the cumulative cases reported from January 2012 until July 2013 and those predicted by the model for the same period was 24.99% for dengue and only 4.22% for severe dengue. Conclusions: The fuzzy model identification technique based on multiresolution analysis produced a proper representation of dengue and severe dengue cases for Colombia despite the complexity and uncertainty that characterize this biological system. Additionally, the obtained models generate plausible predictions that can be used by surveillance authorities to support decision-making oriented to designing and developing control strategies.