A New Era of Malaria’s Disease Control using AI-Driven Predictive Models

Malaria remains a public health menace affecting around more than 240 million cases, and hundreds of thousands of deaths per annum with most cases reported from Sub-Saharan region. Advancement in Artificial Intelligence and Machine Learning offers transformative solutions in malarial control using predictive analytics, programmed diagnostics, effective treatment and malarial drug discovery. This paper aims to explore the use of AI in managing malaria, outlining methodologies like Recurrent Neural Networks that can predict malarial outbreaks with high efficiency, Convolutional Neural Networks for blood smears detection and Deep Learning models for identifying new drug combinations for malarial management. AI can be useful in vector control by reviewing the behavior and environment of mosquitoes to improve the efficiency of the treatments and its implementation in malaria diagnosis and tailored treatment decreasing human error and smoothening the use of resources while speeding up the treatment process. The long-term advancement includes both AI technology in geo-spatial and predictive analytics in real-time approach to epidemic surveillance and management of resources. Genetic markers for patients and individual treatment history shall also continue to expand the possibilities of the management of malaria. However, issues like ethical hurdles, collection and protection of patient data and medical implementation need to be attended. It is therefore important to engage collective efforts of governments, non-government organizations as well as technology delivering firms to unlock the full potential of AI in eliminating malaria and in improving global health outcomes worldwide.