The integration of artificial intelligence in malaria control and surveillance
Ekaterina Lyutsova, Kalina Stoyanova, Margarita Gospodinova, Diana Radkova · Scripta Scientifica Medica · 2024
Introduction: Malaria, caused by Plasmodium spp., remains a global health challenge despite significant reductions in incidence and mortality over recent decades. Factors such as drug and insecticide resistance, diagnostic limitations, and the expansion of Anopheles vectors into new regions hinders control efforts. Artificial intelligence (AI) offers innovative tools to address these challenges by processing complex data, improving diagnostics, and supporting prevention strategies.Aim: This review explores the implementation of AI-based technologies in malaria control, focusing on their roles in epidemiological surveillance, diagnosis, treatment, vector control, and vaccine development.Materials and Methods: A comprehensive literature review was conducted for studies published between 2020 and 2024. Keywords included “malaria,” “Plasmodium,” and “artificial intelligence,” “AI.” The results were reviewed, emphasizing observational research on AI applications in epidemiological modeling of malaria transmission, enhancing vector control, improving diagnostic methods and protocols, and advancing the development of new vaccines and treatments.Results and Discussion: AI technologies demonstrate а transformative potential in malaria control. AI-driven models can predict outbreaks by analyzing climatic, environmental, and medical data; they can improve the accuracy of rapid diagnostic tests and automate the analysis of blood smears for parasite detection, enhancing diagnostic reliability. In addition, they can accelerate drug discovery by identifying novel therapeutic targets, identify antigenic candidates, and predict immune responses assisting vaccine research. AI-integrated citizen science initiatives, supported by smartphone apps, enhances vector surveillance, while satellite imagery processed through machine learning predicts vector breeding sites.Conclusion: AI-based tools represent a promising frontier in malaria management, offering innovative solutions for surveillance, diagnostics, treatment, and prevention. With continued research and refinement, these modern-day technologies could significantly contribute to the eradication of malaria.