Fuzzy model identification of dengue epidemic in Colombia based on multiresolution analysis

Area of Science:

• Epidemiology
• Computational Biology
• Public Health

Background:

• Dengue and severe dengue pose significant public health challenges in Colombia.
• Accurate modeling of epidemic dynamics is crucial for effective disease control.

Purpose of the Study:

• To develop and validate a novel fuzzy model for representing dengue and severe dengue epidemics in Colombia.
• To assess the predictive capabilities of the proposed model for future case numbers.

Main Methods:

• A multiresolution analysis combined with fuzzy systems was employed to model dengue and severe dengue cases (1995-2011).
• The proposed method's performance was compared against traditional fuzzy modeling techniques using mean square error and variance accounted for.
• Predictive accuracy was evaluated for a three-year horizon (2012-2015) and validated against independent data.

Main Results:

• The developed fuzzy model accurately captured the epidemic dynamics of dengue and severe dengue in Colombia.
• The proposed technique demonstrated significantly improved data representation compared to traditional methods, with similarity increasing to 90.06% for dengue and 76.83% for severe dengue.
• Predictions for 2012-2013 showed a low error rate (24.99% for dengue, 4.22% for severe dengue) compared to validation data.

Conclusions:

• Fuzzy model identification based on multiresolution analysis provides a robust representation of complex dengue and severe dengue dynamics in Colombia.
• The model's accurate predictions can aid surveillance authorities in developing targeted control strategies.