I-GAME: Integrated Genomics and AI as a tool for Malaria Elimination in Brazil

Abstract

Malaria remains a significant global health issue, with millions of cases and hundreds of thousands of deaths annually, predominantly caused by Plasmodium falciparum (Pf) and Plasmodium vivax (Pv). Control efforts are complicated by emerging drug resistance, particularly against artemisinin-based treatments (ACTs). While artemisinin resistance is primarily observed in Southeast Asia, the risk exists in other regions with similar transmission dynamics, including parts of South America such as the Guiana Shield. The importance of genomic data in malaria control is highlighted, particularly in identifying drug resistance (DR) mutations and tracking transmission patterns. Whole-genome sequencing (WGS) and targeted amplicon sequencing (AMP-SEQ) are employed to detect species, DR mutations, and genetic diversity. These technologies, supported by platforms like Oxford Nanopore and Illumina, enable precise epidemiological insights and aid in surveillance strategies. However, the effective utilization of genomic data is hindered by challenges such as the need for advanced informatics tools. The development of AI-driven tools, like the Malaria-Profiler software, facilitates rapid analysis and interpretation of WGS data, providing actionable insights into species identification, DR profiles, and geographic origins. Such tools are crucial for informing clinical management, surveillance efforts, and public health interventions, particularly in regions with sparse data like Brazil. The LSHTM and ICB-USP aim to enhance these informatics tools further by integrating AI models to continually update mutation libraries and improve predictive accuracy for species and DR profiling, in addition to other genomic information that could assist the National Malaria Control Programme. The project involves performing WGS/AMP-SEQ in Brazilian malaria hotspots to enhance understanding of genetic diversity and improve disease control and elimination strategies in the country. The integration of AI with genomic data promises to revolutionize malaria control by enabling proactive surveillance, personalized treatment strategies, and timely response to emerging threats such as drug resistance. This approach not only enhances clinical care but also strengthens public health systems through informed decision-making and collaborative data sharing among researchers and healthcare providers globally. (AU)