Boosting Methods For Dengue Incidence Rate Prediction in Bandung District

Abstract

Dengue infections are among the top 10 diseases that cause the most deaths worldwide. Dengue is a severe global threat and problem, especially in tropical countries like Indonesia. The Indonesian Ministry of Health also stated that dengue is as dangerous as COVID-19. One of the preventive actions that can be taken is by controlling vectors (the Aedes aegypti mosquito) where weather factors influence their breeding. In this study, the prediction of dengue incidence rate is carried out using three boosting methods i.e., Extreme Gradient Boosting, Adaptive Boosting, and Gradient Boosting. The data used are monthly data of dengue incidence rate and weather data. The case study used is Bandung district, West Java Province, Indonesia. The important issues that is investigated in this study is to find the weather parameters that have the most influence on IR and gradually improve the prediction model through three test scenarios. From the test results, the weather parameter that has the most influence on the next month's IR is temperature. Meanwhile, the best training data length is five years (2016-2020). Finally, the best prediction model achieved by AdaBoost method with value of Root Mean Square Error and Correlation Coefficient for testing data (January-December 2021) are 0.55 and 0.95, respectively.