Alfie Lailiyah

Alfi Lailiyah

Spatiotemporal deep learning model and cost-effectiveness analysis for dengue prediction and control in Indonesia

Dengue is a common mosquito-borne viral disease in tropical and subtropical regions. Indonesia is an endemic country where dengue continues to pose a significant public health challenge. This project will conduct a scoping review to map existing spatiotemporal modelling approaches for dengue prediction and develop statistical and deep learning-based spatiotemporal prediction models, utilising oceanographic, climatic, population and socio-economic data to improve early detection of dengue outbreaks in Indonesia. A cost-effectiveness analysis will be conducted to evaluate the use of Wolbachia mosquitoes as a dengue control strategy in Indonesia.

Principal Supervisor’s name: A/Prof Agus Salim

Co-supervisors’ name: Prof Julie A Simpson

Source of funding: Indonesia Endowment Fund for Education Agency (LPDP)