Children bear outsized risks from antimicrobial resistance, climate change and emerging zoonoses because immaturity, behaviour and ecosystem dependence concentrate exposure; yet One Health surveillance and policy remain fragmented and adult‑centric. Building integrated, child‑labelled datasets and equity‑aware AI/economic models—with targeted governance reforms—will improve forecasting and resource allocation, particularly in low‑ and middle‑income countries.

Main Finding

Children are uniquely vulnerable within the One Health nexus: physiological immaturity, developmental sensitivity, behavior-driven exposures, and ecosystem dependence make them disproportionately affected by antimicrobial resistance (AMR), climate change, and emerging zoonotic/vector-borne infections. The review argues that protecting child health requires embedding an explicit, child-centered lens into One Health research, surveillance, governance, and interventions—particularly in low- and middle-income countries (LMICs) where data and policy gaps are largest.

Key Points

• Children’s vulnerability stems from multiple, interacting factors:
  • Physiological and immunological immaturity (including neonatal risks).
  • Developmental windows with long-term consequences from early exposures.
  • Behavior (hand-to-mouth, play, outdoor exposure) increasing contact with environmental and animal reservoirs.
  • Dependence on caregivers and local ecosystems for nutrition, shelter, and sanitation.
• AMR:
  • Children are disproportionately exposed to resistant pathogens via clinical care, community transmission, food chains, and environmental contamination.
  • Pediatric and neonatal AMR pose distinct clinical and surveillance challenges.
  • Antibiotic use in humans and animals, plus environmental antibiotic residues, create converging selection pressures.
• Climate change:
  • Intensifies direct (heat, extreme weather injury) and indirect (food insecurity, mental health, shifting disease ecologies) harms to children.
  • Extreme weather events amplify exposure to pathogens and degrade health infrastructure and services.
• Emerging infectious and vector-borne diseases:
  • Geographic ranges of many vectors and zoonoses are shifting, increasing children’s exposure in new areas.
  • Urbanization and biodiversity loss alter host–pathogen dynamics affecting pediatric risk.
• Systemic gaps:
  • Sparse child-specific surveillance across human, animal, and environmental domains.
  • Limited integrated One Health research and policy implementation—particularly in LMIC contexts.
  • Fragmented governance and funding hinder cross-sectoral prevention and response.
• Policy imperative:
  • A child-centered One Health approach can strengthen prevention, resilience, and equity; it requires integrated data, targeted interventions, and governance reforms.

Data & Methods

• Study type: Narrative (qualitative) review synthesizing evidence across human, animal, and environmental health literatures.
• Sources: Interdisciplinary peer-reviewed studies, surveillance reports, and policy literature (no single pooled dataset or meta-analysis reported).
• Methods: The authors integrate findings across domains to illustrate convergent pathways affecting children (AMR, climate impacts, zoonoses), highlighting epidemiological, environmental, and social mechanisms.
• Limitations noted:
  • Lack of standardized, child-specific surveillance data limits quantitative synthesis.
  • Potential under-representation of LMIC contexts and longitudinal child-health outcome data.
  • Heterogeneous evidence across disciplines makes causal attribution and effect-size estimation difficult.

Implications for AI Economics

• Implications for modeling and forecasting
  • Data gaps (especially child-specific, cross-sectoral One Health data) reduce the reliability and fairness of AI-driven disease prediction and economic models.
  • Integrating human–animal–environmental datasets is necessary for accurate, child-sensitive forecasts of AMR spread, vector expansion, and climate-driven health shocks.
  • Econometric and ML models should incorporate developmental timing and lifetime human-capital impacts (not just short-term morbidity) to value interventions properly.
• Implications for cost-effectiveness and investment priorities
  • Economic evaluations should internalize cross-sector externalities (e.g., antibiotic use in agriculture affecting pediatric AMR) and long-term costs to child health and productivity.
  • AI can support value-of-information and dynamic resource-allocation analyses to prioritize interventions (e.g., stewardship, vaccination, environmental controls) that most benefit children.
  • Investment cases for surveillance and One Health infrastructure must account for intergenerational returns (reduced chronic disease burden, preserved cognitive development).
• Market design, incentives, and policy tools
  • AMR and environmental externalities create classic market failures; mechanism-design and incentive-based policies (subsidies for stewardship, taxes on harmful practices, pay-for-performance for One Health outcomes) can be evaluated with AI-enabled simulations.
  • AI-enabled pricing and tendering for antibiotics, vaccines, and diagnostics can be used but must be informed by equity constraints to avoid disadvantaging LMICs and children.
• Equity, fairness, and distributional concerns
  • AI systems trained on incomplete, adult-centric, or high-income–biased data risk perpetuating inequities in prediction, resource allocation, and policy recommendations for children and LMICs.
  • Economic models should include fairness constraints and stratify outcomes by age, socioeconomic status, and geography.
• Operational and governance considerations
  • Building integrated One Health data platforms (human, veterinary, environmental) is a priority for both AI applications and economic evaluation; public investment and international cooperation will be required.
  • Data governance must balance utility for AI-driven policy with privacy, consent (especially for children), and cross-sectoral data-sharing rules.
• Recommended AI-economics research directions
  • Develop child-specific predictive models for AMR and vector-borne disease risk that fuse clinical, veterinary, environmental, and climate data.
  • Use causal ML and agent-based simulations to evaluate intervention strategies and incentive mechanisms, explicitly modeling developmental and long-term economic impacts.
  • Conduct value-of-information analyses to prioritize surveillance investments that reduce uncertainty most relevant to child-health outcomes.
  • Design equitable allocation algorithms for scarce resources (vaccines, diagnostics, climate-adaptive infrastructure) with constraints to protect children and LMICs.
  • Quantify macroeconomic and human-capital losses from childhood exposures (AMR, climate shocks, zoonoses) to inform long-term policy planning.
• Practical policy recommendations for economists and AI practitioners
  • Prioritize funding for integrated, child-labeled One Health datasets and interoperable metadata standards.
  • Include pediatric outcomes and developmental timing in cost-effectiveness and planning models.
  • Co-develop models with public-health, veterinary, and environmental experts and stakeholders in LMICs to ensure relevance and equity.
  • Build transparency and fairness checks into AI tools used for resource allocation and surveillance to prevent amplifying existing disparities.

Summary: From an AI-economics perspective, the review highlights a high-value opportunity—and a pressing need—to build integrated, child-centered One Health data and models. Doing so will improve forecasting, optimize scarce-resource allocation, and enable economic policies that internalize cross-sector externalities and protect children’s long-term human capital.