Breaking the resistance frontier: Integrative AI, pre-clinical, and clinical strategies for malaria eradication

Malaria research stands at a critical crossroads in global health, as the disease continues to cause over 600,000 deaths each year, mainly across sub-Saharan Africa. Despite sustained global eradication efforts, a convergence of biological and operational challenges is complicating progress. Among the most pressing are the rapid evolution of Plasmodium falciparum lineages with kelch13 mutations conferring artemisinin resistance, growing tolerance to traditional insecticides in vector populations, and widespread deletions of pfhrp2 and pfhrp3 genes that undermine the reliability of standard rapid diagnostic tests. Meanwhile, Plasmodium vivax remains a persistent reservoir of infection, particularly in co-endemic regions. Advances in whole genome sequencing (WGS) and artificial intelligence (AI)-driven epidemiology have unveiled new molecular insights, yet implementation gaps remain between genomic surveillance and real-world disease control. The global health community now faces the urgent task of translating molecular and computational discoveries into tangible policy and intervention frameworks that can adapt to the evolving biological threat.This Research Topic aims to catalyze cross-disciplinary collaboration at the frontier of malaria eradication by uniting pre-clinical, computational, AI-driven, and translational clinical research. The objective is to identify viable solutions that mitigate resistance to antimalarial therapies and insecticides, improve diagnostic reliability amid genetic variation, and advance the integration of machine learning models for real-time surveillance and treatment optimization. Key goals include testing the hypothesis that technology-driven insights can inform scalable intervention strategies adaptable to diverse epidemiological contexts. Furthermore, this initiative seeks to generate reproducible, high-impact data that support evidence-based policymaking, equitable resource allocation, and novel public health frameworks drawing on One Health principles.The scope of this Research Topic encompasses both laboratory and field-based investigations, with emphasis on interdisciplinary methods linking biology, computation, and clinical innovation. It excludes purely theoretical or narrowly focused studies without translational relevance.To gather further insights into the molecular, technological, and socio-environmental determinants of malaria eradication, we welcome articles addressing, but not limited to, the following themes:o Mechanisms and surveillance of antimalarial drug and insecticide resistance (Plasmodium falciparum, P. vivax, vector species)o AI and machine learning in genomic monitoring, pre-clinical modeling, and diagnostic innovationo pfhrp2/pfhrp3 gene deletions and their impact on point-of-care testing and case detectiono Computational and systems biology approaches to predict vector-parasite adaptationo Climate change, environmental shifts, and One Health perspectives on vector ecology and transmissiono Translational pipeline development from computational discovery to clinical validationo Policy integration, implementation science, and funding strategies for sustainable eradication effortsManuscript Types: AI-free concept papers, original complete pre-clinical studies, comprehensive and systematic reviews, case studies, vector management reports, randomized controlled trials, large-scale observational studies, implementation research with defined health policy relevance, and perspectives emphasizing global applicability. Topic editors: Dr. Varun Gorki, Neelima Mishra, NEHA SYLVIA WALTER Source: https://www.frontiersin.org/research-topics/83009/breaking-the-resistance-frontier-integrative-ai-pre-clinical-and-clinical-strategies-for-malaria-eradication