An In-Depth Epidemiological Characterization of Dengue in the Philippines by Artificial Intelligence

Previous studies regarding Dengue fever have identified various factors related to climate change and dengue transmission, but less research paper has explored whether previously identified factors are still significant. Moreover, there is no full evaluation of dengue patterns in terms of creating equations and model that can describe the dengue phenomena. The study created a mathematical model that will explain the dengue phenomena in the Philippines. The model will be used to describe the behavior exhibit in the national dengue cases. Moreover, the said mathematical equations will assist epidemiologist in forecasting dengue cases. This study used a new methodology enclosed in the Complex Adaptive System. The cases from the national surveillance report of the Department of Health (DOH) were used to analyze the monthly number of reported dengue cases.  A five-year interval from 2012 to 2017 was utilized to determine changes in Dengue pattern.  The said datasets were processed using symbolic regression with the used of freely downloadable software Eurega ® (Nutonian, 2015). The study showed that the Dengue Cases in the Philippines has lost its seasonality and can occur anytime in the year. The previously identified variables such as rainfall and temperature, are no longer contributory factors of Dengue Cases. A mathematical model can be used to predict the incidences of Dengue in the Philippines.

Keywords: dengue fever, complex adaptive system, dengue case model