Artificial Intelligence Model as Predictor for Dengue Outbreaks - Raja, Dhesi Baha; Mallol, Rainier; Choo Yee, Ting; Kamaludin, Fadzilah; Ahmad, Rohani; Ismail, Suzilah; Jayaraj, Vivek Jason; Sundram, Murali (Malaysian Public Health Physicians Association, 2019)
Dengue is an increasing threat in Malaysia, particularly in the more densely populated regions of the country. We present an Artificial Intelligence driven model in predicting Aedes outbreak, using predictors of weather variables and vector indices sourced from the Ministry of Health. Analysis and predictions to estimate Aedes populations were conducted, with its results being used to infer the possibility of dengue outbreaks at pre-determined localities around the Klang Valley, Malaysia. A Bayesian Network machine learning technique was employed, with the model being trained using predictor variables such as temperature, rainfall, date of onset and notification, and vector indices such as the Ae. albopictus count, Ae. aegypti count and larval count. The interfaces of the system were developed using the C# language for Server-side configuration and programming, and HTML, CSS and JavaScript for the Client Side programming. The model was then used to predict the population of Aedes at periods of 7, 14, and 30 days. Using the Bayesian Network technique utilising the above predictor variables we proposed a finalised model with predictive accuracy ranging from 79%-84%. This model was developed into a Graphical User Interface, which was purposed to assist and educate the general public of regions at risk of developing dengue outbreak. This remains a valuable casestudy on the importance of public data in the context of combating a public health risk via the development of models for predicting outbreaks of dengue which will hopefully spur further sharing of data by all parties in combating public health threats