Design and development of IoTbased decision support system for dengue analysis and prediction: case study on Sri Lankan context

Abstract

Dengue fever is an epidemic viral disease that is spread by various types of dengue viruses of the genus Aedes, primarily Aedes aegypti. Dengue epidemics are common in humid and subhumid areas of the world, mostly in cities and suburban regions. The old methods were delay in diagnosing and restricting the growth of dengue eruption. This chapter proposes a fresh approach in Fuzzy Rule–based Neural Classification with Internet of Things (IoT), cloud computing, and fog computing to analyze and predict dengue outbreak. The proposed fog-driven IoT architecture in which each component is seamlessly connected with each other to execute activities such as disease management, preventative care, clinical monitoring, early warning systems, e-medicine, and drug and food recommender system. This IoT-based decision support system aims to stop, control, and enable forecasting of eruptions of dengue, facilitating medical officers the information and insights to handle the outbreak, well in advance.