Forecasting the Dengue Outbreak using Machine Learning Algorithm: A Review

The control of Mosquito-borne disease (MBD) is not one size fits all as various factors can stimulate vector propagation and increase the infection rate. One cannot simply adopt any MBD outbreak prediction system to their study area due to specific issues such as insufficient data and the arduousness of obtaining particular datasets. Manipulating open-source data available from the Internet might be scarce, unstructured, inadequate, irrelevant, and could merely be noise. Hence, the machine learning predictive power will be affected directly. This paper aims to review the available MBD outbreak prediction framework and propose an enhanced framework with the Entomological Index feature. A new conceptual framework is introduced where machine learning is leveraged to increase the future MBD outbreak predictive.