Transmission Fitness and Data-Driven Surveillance of Antimalarial Drug Resistance in Plasmodium falciparum

The global effort to control and eliminate malaria is increasingly threatened by the emergence and spread of drug-resistant Plasmodium falciparum strains. While the genetic determinants of resistance have been well characterized, critical gaps remain in understanding both the biological trade-offs that allow resistant lineages to persist and transmit, and the surveillance capacity needed to detect and respond to these threats in real time. Resistant parasites must not only withstand pharmacological pressure but also remain efficient at producing infectious gametocytes and completing the sporogonic cycle within the Anopheles vector. As resistant clones continue to expand across Southeast Asia and Sub-Saharan Africa, understanding this balance between fitness costs and transmissibility has become an increasingly urgent public health priority.This Research Topic aims to address a central and largely unresolved question: how do drug-resistant P. falciparum populations sustain their spread despite the biological costs of resistance, and how can we detect and respond to this spread earlier and more accurately? Current surveillance approaches are often limited by fragmented data sources, siloed reporting systems, and delayed detection, leaving gaps between when resistance emerges and when it is recognized as a threat. At the same time, advances in genomics, artificial intelligence, and digital health now make it possible to integrate genomic, clinical, epidemiological, and public health data in ways that were previously impractical. By bringing together the biological drivers of resistance persistence and the computational tools used to track and predict it, this Research Topic seeks to build a more complete, actionable understanding of how resistant parasite populations emerge, spread, and can ultimately be contained.We welcome contributions bridging bench-side genomic and experimental research with field-based epidemiological data and computational surveillance methods. Relevant topics include, but are not limited to: - Fitness costs and transmission biology of resistant P. falciparum lineages- Gametocytogenesis and sporogonic development in resistant strains- Genomic and molecular epidemiology of resistance spread- The application of machine learning or AI-driven tools to malaria surveillance and early warning systems.We particularly encourage submissions that integrate biological and computational perspectives, or that translate findings into practical recommendations for next-generation intervention and monitoring strategies. Topic editors: Leen N. Vanheer, Daniel Muthui Kiboi, Victor Oluwatomiwa Ajekiigbe Source: https://www.frontiersin.org/research-topics/84584/transmission-fitness-and-data-driven-surveillance-of-antimalarial-drug-resistance-in-plasmodium-falciparum