Data-driven decision making in malaria control: the role of AI in the public health policy

Abstract

Malaria endures a significant part in public health concern, especially in tropical and subtropical regions. Traditional malaria control methods often face limitations with surveillance, diagnosis and efficient resource allocation. This review explores the role of Artificial Intelligence (AI) in augmenting data-driven decision-making for malaria control and elimination efforts, focusing on surveillance systems, enhancing the effectiveness of intervention strategies and optimizing the resource allocations. AI technologies, mainly machine learning algorithms and computer vision systems, demonstrate significant potential in improving malaria control outcomes. Key findings include increased accuracy in outbreak prediction, improved diagnostic precision through automated microscopy and optimized resource allocation reducing response times. Additionally, deep learning models are emerging as promising tools in identifying drug resistance patterns and personalizing treatment protocols. AI integration in malaria control programs offers substantial benefits for public health decision-making. In this article, we conducted a comprehensive review of peer-reviewed literature, analyzing AI applications in malaria control across key domains such as surveillance, diagnosis, treatment and resource management. However, effective implementation requires robust data infrastructure, ethical frameworks addressing algorithmic bias and sustained international collaboration. Future directions prioritize equitable access, capacity building and development of standardized evaluation metrics for evaluating AI-driven interventions.