AI-Enabled One Health Surveillance for Emerging Arthropod Vector Ecology in Climate-Driven Urban Landscapes

Climate change, rapid urbanization, and the growth of smart city infrastructure are reshaping the ecology, distribution, and behaviour of arthropod vectors responsible for transmitting parasitic and vector-borne diseases. Rising temperatures, shifting rainfall, expanding urban heat islands, and increasing human mobility are pushing mosquitoes, ticks, mites, sandflies, and fleas into previously unsuitable habitats, including dense urban environments. Given the zoonotic significance of many vector-borne pathogens, these shifts carry consequences that extend well beyond any single region, placing rapidly urbanizing communities at growing risk as new ecological niches emerge.Vector surveillance has traditionally relied on labour-intensive, field-based entomological methods that scale poorly and struggle to detect the rapid, localized shifts driven by climate and urban change. Earlier geospatial approaches to this problem, largely GIS and remote sensing, have focused on retrospective risk mapping across parasitic disease epidemiology more broadly. Fragmented surveillance infrastructure across regions further limits early detection, leaving public health systems reacting to outbreaks rather than anticipating them.Recent advances in artificial intelligence, IoT, autonomous sensing, edge computing, and unmanned aerial systems are opening new opportunities to move arthropod surveillance beyond static risk mapping toward predictive, real-time monitoring. This Research Topic brings together computational, ecological, and applied approaches to arthropod vector dynamics under changing climatic and urban conditions, encouraging collaboration across parasitology, medical entomology, ecology, environmental science, computer science, and public health.We welcome original research, reviews, methods papers, perspectives, and technology reports on digital and computational strategies for arthropod vector surveillance under climate change, including but not limited to:• Climate-driven vector ecology: shifts in vector distribution, behaviour, and adaptation linked to urbanization and urban heat islands.• Emerging and invasive vectors: surveillance and characterization of range-expanding vectors linked to environmental change.• AI-driven vector identification: deep learning and computer vision for automated species identification.• Explainable and generative AI: XAI, foundation models, and generative AI for vector classification and decision support.• Remote sensing and geospatial analytics: satellite imagery and sensor fusion for mapping vector habitats and predicting risk.• Autonomous and IoT-based monitoring: UAVs, edge computing, and IoT platforms for real-time vector surveillance.• Predictive and agentic modelling: large-scale forecasting, LLMs, and agentic AI for outbreak prediction and knowledge extraction.• One Health and policy integration: linking intelligent vector surveillance to climate-resilient public health policy. Topic editors: Maria Anu Vensuslaus, Thaddeus Marzo Carvajal, J. Joshua Thomas Source: https://www.frontiersin.org/research-topics/85116/ai-enabled-one-health-surveillance-for-emerging-arthropod-vector-ecology-in-climate-driven-urban-landscapes