Tracking and prediction of dengue outbreak using cloud-based services and artificial neural network

Abstract

Dengue is considered as one of the diseases which needs serious attention, especially in the less developed areas of the world. In order to allow sufficient time in taking necessary decisions and actions to safeguard the situation for local authorities, an accurate analysis of dengue epidemic seasons is crucial in preventing and counteracting its effect. To address the issue, this paper proposes a web-based dengue tracking system (DTS) that utilizes environmental factors in predicting the future behavior of dengue cases. The study aimed to track down and analyze the dengue cases that take place in the city of Iloilo, Philippines. The researchers used Artificial Neural Network for prediction based on the amount of rainfall, relative humidity, mean temperature, and monthly recorded cases. The system can serve a valuable purpose for the health sectors as it guides them to take action on recorded cases in areas which are prone to dengue. Through this, early detection and warning of dengue case growth can be monitored and preventive measures can be implemented immediately, thereby reducing the possibility of an outbreak.