Predicting Dengue in the Philippines Using an Artificial Neural Network

2023/10/24 by Zafra, Bryan
#Philippines #artificial neural network #climate change #dengue

paper · doi:10.25929/e3bk-a370

Abstract

Dengue fever is an infectious disease caused by Flavivirus transmitted by Aedes mosquito. This disease predominantly occurs in the tropical and subtropical regions. With no specific treatment, the most effective way to prevent dengue is vector control. The dependence of Aedes mosquito population on meteorological variables make prediction of dengue infection possible using conventional statistical and epidemiologic models. However, with increasing average global temperature, the predictability of these models may be lessened employing the need for artificial neural network. This study uses artificial neural network to predict dengue incidence in the entire Philippines with humidity, rainfall, and temperature as independent variables. All generated predictive models have mean squared logarithmic error of less than 0.04.

Related

• Neural networks for dengue forecasting: a systematic review
• When climate variables improve the dengue forecasting: a machine learning approach
• Machine Learning Models for Dengue Forecasting in Singapore
• Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques
• Linking climate and dengue in the Philippines using a two-stage Bayesian spatio-temporal model