Grand challenge: building a digitally enabled one health future for pediatric infectious diseases

Pediatric infectious diseases are moving into a phase where the old boundaries of the specialty just don't hold up anymore. A febrile child might be the first visible point in a much longer chain.In 2024, a dengue outbreak in Fano, Italy, hit 199 locally acquired cases, and France logged 83 autochthonous infections-numbers that would have seemed out of place in continental Europe a decade ago [1]. For pediatric infectious disease specialists, these aren't just epidemiological oddities; they show how climate, competent vectors, human mobility, and delayed recognition can all converge fast. But the clinical encounter only helps with earlier outbreak detection, safer prescribing, and prevention if clinical data actually link up with microbiological, entomological, environmental, and public health signals. So the real challenge isn't just responding to more pathogens-it's building a child-centered learning system that connects human, animal, and environmental health through responsible digital transformation.One Health gives us the conceptual framework for this shift. It starts from the idea that the health of people, animals, plants, and ecosystems is all interdependent, and the Quadripartite One Health Joint Plan of Action flags zoonoses, vector-borne diseases, food safety, antimicrobial resistance (AMR), and environmental health as connected priorities [2,3]. Yet One Health often stays an aspiration rather than a routine part of pediatric practice. Digital transformation could supply the connective tissue: interoperable surveillance, geospatial analysis, pathogen genomics, environmental sensors, electronic health records, and artificial intelligence (AI) can turn fragmented observations into shared situational awareness. Technology, though, isn't the end goal. Its value lies in enabling earlier, more equitable, and more sustainable decisions for children.Children aren't just small adults, either biologically or in terms of exposure. Immune maturation, age-dependent clinical phenotypes, vaccine schedules, developmental behavior, and weightbased treatment all shape susceptibility and outcomes. Their environments are distinct too: households, schools, childcare facilities, playgrounds, farms, and urban transport determine contact with pathogens, animals, pollutants, and vectors. Infants and young children have higher ventilation relative to body size, frequent hand-to-mouth activity, and little control over their surroundings. Social deprivation, displacement, and unequal access to vaccination, sanitation, diagnostics, and digital infrastructure further concentrate risk.Climate change makes these interdependencies more visible by the year. Temperature, rainfall, drought, flooding, and ecosystem disruption influence the geography and seasonality of vectorborne, waterborne, foodborne, and respiratory infections. Europe's experience with locally acquired arboviruses shows how competent vectors, favorable weather, and imported index cases can converge to create autochthonous transmission, while pediatric infections may stay underdiagnosed because they're mild or nonspecific [1]. Air pollution adds another layer: it can damage epithelial barriers, modify immune responses, and raise the risk or severity of respiratory infections in children [4]. Environmental exposures may also reshape the respiratory microbiota and favor colonization by potential pathogens [5]. The gut and airway microbiomes, then, should be seen as dynamic interfaces between the child, antimicrobials, nutrition, pathogens, and the environment-not isolated laboratory curiosities.Current surveillance systems are usually separated by institution, discipline, and administrative level. Pediatric clinical data sit in hospital records; microbiological results in laboratory systems; antimicrobial consumption in pharmacy databases; vaccination in registries; animal infections in veterinary networks; and climate, pollution, wastewater, and vector data in other agencies. A digitally enabled One Health model wouldn't indiscriminately centralize everything. It would set up interoperable, privacy-preserving pathways that allow relevant signals to be combined across space and time.Such infrastructure could detect an unusual pediatric syndrome, connect it with a veterinary or environmental alert, map it against weather and mobility patterns, and quickly return guidance to clinicians. Wastewater and environmental sequencing could complement clinical testing, while genomic epidemiology could distinguish community transmission from healthcare-associated clusters. Federated analysis could let institutions collaborate without transferring identifiable child-level data. Dashboards could provide age-stratified incidence and resistance patterns, and automated alerts could support-rather than replace-public health judgment. This direction aligns with the WHO global strategy on digital health, but pediatric implementation needs explicit age-sensitive priorities [6].AI may enhance forecasting, image and signal interpretation, triage, diagnostic support, and antimicrobial decision-making. Still, the gap between model development and clinical benefit remains wide. A systematic review of predictive AI implemented in pediatric practice found few real-world implementations and inconsistent evaluation of clinical, workflow, and human outcomes [7]. In pediatric infectious diseases, digital technologies show promise for monitoring, access, communication, and timely intervention, but infrastructure and implementation gaps persist [8]. Future research has to move beyond retrospective accuracy. Models should be externally validated across ages, settings, ethnic and socioeconomic groups; compared with existing care; evaluated prospectively; monitored for performance drift; and designed with clinicians, families, and public health professionals.Digital transformation can be most immediately useful where uncertainty drives unnecessary treatment. Rapid molecular tests,