Abstract: Dengue is a viral mosquito-borne infection that is endemic and has become a major public health concern in the Philippines. Cases of dengue in the country have been recorded to be increasing, however, it is reported that the country lacks predictive system that could aid in the formulation of an effective approach to combat the rise of dengue cases. Various studies have reported that climatic factors can influence the transmission rate of dengue. Thus, this study aimed to predict the probability of dengue incidence in Iligan City per barangay based on the relationship of climatic factors and dengue cases using different predictive models with data from 2008 to 2017. Multiple Linear Regression, Poisson Regression, and Random Forest are integrated in a mini-system to automate the display of the prediction result. Results indicate that Random Forest works better with 73.0% accuracy result and 33.58% error percentage, with time period and mean temperature as predictive variables.

Ian Lindley G Olmoguez, Mia Amor C. Catindig, Minchie Fel Lou Amongos and Fatima G. Lazan, “Developing a Dengue Forecasting Model: A Case Study in Iligan City” International Journal of Advanced Computer Science and Applications(IJACSA), 10(9), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100936

@article{Olmoguez2019,
title = {Developing a Dengue Forecasting Model: A Case Study in Iligan City},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100936},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100936},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {9},
author = {Ian Lindley G Olmoguez and Mia Amor C. Catindig and Minchie Fel Lou Amongos and Fatima G. Lazan}
}