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 at the human–animal–environment interface: antimicrobial resistance (AMR), climate change, and emerging infectious diseases interact to disproportionately threaten pediatric health. A One Health, child-centered approach—integrating human, animal, and environmental surveillance, policy, and interventions—is essential to reduce immediate harms and long-term intergenerational burdens, especially in low- and middle-income countries (LMICs) where data, infrastructure, and governance gaps are largest.

Key Points

• Children’s heightened vulnerability stems from physiology (immature immunity, higher intake per body weight), behavior (hand-to-mouth, outdoor play), developmental windows, and dependence on ecosystem services.
• AMR:
  • Pediatric antibiotic use (frequent, often empiric) and high exposure pathways (household, food chain, environment, animal contact) drive resistance in children.
  • Neonates and preterm infants are particularly at risk (high colonization and infection rates with multidrug-resistant Gram-negatives).
  • Environmental reservoirs (wastewater, soil, agricultural run-off) and animal antibiotic use contribute substantially to the pediatric “resistome.”
  • Pediatric-specific surveillance and stewardship are limited, especially in LMICs; community-based stewardship and integration with existing child-health platforms are promising.
• Climate change & emerging infections:
  • Warming, precipitation shifts, urbanization, biodiversity loss, and extreme weather events expand vector ranges and create new zoonotic spillover opportunities, disproportionately affecting children.
  • Climate impacts include direct physical harms (heat, malnutrition, infectious disease exposure) and mental-health/developmental effects from displacement and stress.
• Evidence gaps: limited child-specific, age-disaggregated One Health surveillance, few multisector longitudinal studies, scarce multicenter pediatric stewardship evaluations, and weak integration of environmental/animal data into public health decision-making.
• Policy needs: child-centered One Health governance, cross-sector surveillance integration (human/animal/environmental), investment in diagnostics/labs, adapted stewardship for LMICs, and equity-focused interventions.

Data & Methods

• Study type: Narrative review.
• Search: PubMed through August 2025; combined keywords for vectors, pathogens, climate/environmental drivers, and pediatric/epidemiologic terms; Boolean operators used.
• Inclusion: Peer‑reviewed original studies, systematic reviews, meta-analyses, surveillance reports; English language.
• Exclusion: Non–peer-reviewed literature, editorials, commentaries; some emerging infections (e.g., mpox) excluded for scope reasons.
• Screening: Titles/abstracts screened independently by two reviewers; full-text review with dispute resolution by a third reviewer.
• Scope limits: Focus on arboviral infections and selected parasitic diseases (malaria, leishmaniasis); qualitative thematic synthesis across disciplines.

Implications for AI Economics

The paper identifies cross-sector risks, data gaps, and policy needs that have direct implications for economic modeling, AI-driven decision tools, and policy design. Below are actionable implications and recommendations for researchers and practitioners in AI economics.

• Valuing long-term and intergenerational externalities

  • Incorporate child-specific long-term health and developmental impacts (e.g., stunting, neurodevelopmental harms, lifelong AMR consequences) into cost-benefit and integrated assessment models.
  • Use discounting and sensitivity analyses that reflect ethical choices about intergenerational equity; explore non-standard social discounting for child-focused investments.

• Data priorities for modelers and AI systems

  • Require age-disaggregated, cross-sectoral datasets: human clinical/AMR isolates, animal surveillance, environmental resistome measures (water/soil), geospatial climate and vector data, socioeconomic and behavioral indicators.
  • Invest in standardized metadata and ontologies linking health, veterinary, and environmental data to enable interoperable AI models.
  • Where raw cross-sector data sharing is constrained, use federated learning and privacy-preserving ML to train integrated models without centralized data pooling.

• Modeling and forecasting approaches

  • Hybrid models: combine mechanistic epidemiological models (vector dynamics, transmission) with ML for local calibration and short-term forecasting under climate scenarios.
  • Bayesian hierarchical models to borrow strength across regions and to quantify uncertainty for policy decisions, particularly where pediatric surveillance is sparse.
  • Agent-based models (ABMs) to capture household/behavioral interactions (child play, animal contact) that drive exposure and AMR transmission pathways.
  • Value of information (VoI) analyses to prioritize investments (diagnostics, lab capacity, environmental monitoring) where uncertainty reduction yields the greatest policy value.

• Policy optimization and resource allocation

  • Use constrained optimization and reinforcement learning to design adaptive allocation of scarce resources (e.g., diagnostics, antibiotics, vaccines, IPC supplies) that explicitly incorporate equity objectives for children and LMIC settings.
  • Cost-effectiveness analyses should compare traditional siloed interventions vs. integrated One Health interventions; include indirect benefits (reduced AMR spillover, avoided future costs).

• Surveillance & early warning systems

  • Deploy ML-based anomaly detection across integrated One Health streams (clinical, veterinary, environmental sensors) to flag emerging hotspots affecting children.
  • Develop regionally calibrated risk scores that combine climatic projections, vector suitability, socioeconomic vulnerability, and pediatric demographics to prioritize prevention efforts.

• Dealing with data scarcity and heterogeneity

  • Use transfer learning and synthetic data generation (with careful validation) to extend models from data-rich to data-poor settings, while quantifying domain-shift uncertainty.
  • Implement ensemble approaches and robust uncertainty quantification to avoid overconfident policy recommendations in LMICs.

• Causal evaluation and impact assessment

  • Apply causal inference (targeted maximum likelihood, synthetic controls) and causal ML to evaluate stewardship, IPC, and One Health interventions, accounting for confounding and interference across sectors.
  • Design AI-assisted pragmatic trials and stepped-wedge implementations to produce policy-relevant evidence under real-world constraints.

• Equity, governance, and implementation

  • Embed fairness constraints in optimization and predictive models to avoid perpetuating inequalities—e.g., prioritize reductions in pediatric AMR burden in marginalized communities.
  • Model governance and institutional constraints: include transaction costs, feasibility, and capacity limits in economic simulations of One Health policies.
  • Promote participatory modeling with local stakeholders to ground AI-economic tools in contextual realities and improve adoption.

• Research & investment agenda for AI economists

  • Build open, curated One Health pediatric datasets and benchmarks with clear licensing for model development and evaluation.
  • Develop standardized metrics for pediatric One Health outcomes to facilitate cross-study comparisons and meta-analyses.
  • Fund interdisciplinary pilots that test AI-driven resource allocation, surveillance integration, and policy evaluation in LMICs, with pre-specified economic endpoints.

Limitations and cautions for AI economics work - Data gaps, measurement error, and reporting biases are substantial—models must report uncertainty transparently. - Ecological and transmission processes are nonstationary under climate change; models need continual recalibration and scenario analysis. - Ethical considerations (privacy, consent, implications of prioritizing interventions) must be addressed, especially when modeling child populations.

Summary recommendation For economic modeling and AI applications to effectively inform One Health policies that protect children, prioritize building integrated, age-disaggregated datasets; adopt hybrid epidemiological–economic models with robust uncertainty quantification; explicitly value long-term intergenerational impacts; and design equity-aware optimization and evaluation frameworks tailored to LMIC constraints.