Dengue Disease Spread Prediction Using Twofold Linear Regression

Abstract

Dengue is a disease caused by four types of related viruses transmitted by a mosquito, most commonly Aedes Aegypti. The disease is considered an alarming threat to the health of populations spanning millions of people living in tropical and subtropical areas of the globe where the mosquito thrives. A large number of studies have confirmed that the rise of dengue disease is positively correlated with climate and climatic conditions, specifically, humidity, temperature and precipitation levels. Many of these studies include quantitative models correlating climate variables with the incidence of dengue cases. The quantitative models invite the question: how well would we be able to predict future cases of the disease based on climate variables that are included in weather forecasts? To answer this question we conducted a study on Dengue Forecasting using machine learning, which utilizes climate and dengue data (available to data scientists by US government) to forecast future dengue epidemics. In this research we proposed a novel model twofold linear regression which out perform compare to all previous models. we achieve 19.81 mean absolute error which is minimum as compare to traditional machine learning techniques. Moreover, we have analyzed various neural network, support vector machine, random forest, boosted tree, XGBoost based predictive models and evaluate their performance against proposed method.