Workshop Introduction

In recent years, Southeast-Asian (SEA) countries have achieved remarkable progress, and are moving toward the goal of malaria elimination by 2030. However, in many respects, the last mile, i.e., reducing the malaria cases to zero and preventing reestablishment of malaria is the hardest part yet coming. To control malaria in the high-burden areas and eliminate malaria in the low-burden areas, and to prevent resurgence, an effective surveillance strategy is of critical importance as it would act as an early-warning tool to monitor the risk and predict the onward transmission potential.

In this workshop, we will review the current progress and challenges in malaria control and elimination, share updated information, as well as discuss and develop action plans to reach the goal of malaria elimination. The workshop, which consists of tutorials, training programs, and case studies, is aimed at sharing the HKBU experience in developing malaria active surveillance strategies. In this targeted workshop, we will examine the challenges that the SEA countries encountered in malaria control and elimination, and discuss and plan how the developed and validated AI-enabled solutions and software can be implemented to address the challenges and facilitate the active surveillance implementation programme in SEA countries.


Hong Kong Baptist University Department of Computer Science, Hong Kong Baptist University IDSC Lab Chinese Center For Disease Control and Prevention - National Institute For Parasitic Disease Chinese Center for Tropical Diseases Research (CTDR)


Institute of Creativity Sponsored by Hung Hin Shiu Charitable Foundation Institute of Computational and Theoretical Studies

Main Topics:

Current Status and Challenges for Malaria Elimination in South East Asia (SEA)

Challenges and Opportunities in AI-enabled Malaria Control and Prevention

AI-enabled Malaria Control and Prevention Methods

Practical Issues of Data Analytics in AI-enabled Malaria Control and Prevention Methods

AI-enabled Malaria Control and Prevention Software

Case Study on AI-enabled Malaria Control and Prevention Strategy