Prediction of Dengue Outbreaks in Sri Lanka Using Machine Learning Techniques

Dengue outbreaks pose a significant public health challenge in tropical and subtropical regions globally, necessitating effective predictive models for early detection and intervention. This study examines the complex nature of the disease by analyzing weather parameters such as temperature, wind speed, precipitation, and humidity. Through machine learning techniques, we successfully forecasted dengue outbreaks in nine districts in Sri Lanka and identified similar weather and dengue patterns in districts such as Gampaha, Colombo, Kandy and Kurunegala. These findings are important for developing an early warning system, optimizing public health measures, and efficiently allocating resources to minimize the negative impact on communities. Furthermore, the model offers valuable insights for epidemiological research on dengue spread and control.