Emphasis on technical specificity, systemic challenges, and actionable reform pathways.
One Health—defined as the collaborative, multisectoral, and transdisciplinary approach to achieving optimal health outcomes across human, animal, and environmental domains—offers a strategic framework for confronting zoonotic diseases, antimicrobial resistance, and ecosystem degradation. Yet in low-income African countries, entrenched structural limitations constrain its translation into effective public policy. Harnessing digital technologies presents an opportunity to reform policy formulation processes, but this requires targeted interventions in data systems, governance, workforce capacity, legal frameworks, and stakeholder coordination. This article examines the core technological reforms necessary to strengthen One Health policy-making in low-income African contexts.
Introduction and Context
Low-income African countries face a disproportionate burden of infectious diseases, frequent zoonotic outbreaks, constrained healthcare resources, and environmental stressors. The implementation of One Health strategies is impeded by weak governance structures, limited surveillance capacity, fragmented data systems, and inadequate cross-sector collaboration. These deficits result in delayed detection, inadequate response, and misaligned policy interventions across human, animal, and environmental health sectors.
While digital technologies such as electronic surveillance, machine learning analytics, mobile health (mHealth), and interoperable health information platforms have demonstrated value in disease monitoring and response in higher-resource settings, their systematic integration into policy formulation in low-income African contexts remains nascent. The necessary reforms must therefore address both technological adoption and the structural ecosystem that governs policy development.
Current Limitations in One Health Policy Systems
Fragmented Data and Health Information Systems
Low-income African countries often operate vertical, disease-specific surveillance programs with separate reporting tools and limited interoperability, leading to data silos that undervalue cross-sectoral insights. Fragmented systems hinder early detection of zoonotic threats where human and animal data converge.
Weak Institutional Coordination
Governance and leadership deficits, including the absence of formal One Health platforms and ineffective stakeholder networks across ministries (health, agriculture, environment), result in redundant efforts and weak policy alignment.
Workforce and Capacity Constraints
A shortage of skilled health informaticians, epidemiologists, veterinarians trained in digital surveillance, and environmental data analysts limits the ability to leverage modern technologies for policy analysis.
Legal and Regulatory Gaps
Policies specific to health data governance, digital identity frameworks, and information sharing are either absent or insufficient, constraining secure and equitable use of technology in public health decision-making.
Core Technology-Driven Reform Areas
To transition from reactive to proactive One Health policy paradigms, low-income African countries must pursue six principal reforms:
Development of Integrated, Interoperable Digital Platforms
Rationale:
Integrated digital platforms unify human, animal, and environmental health data into a common repository accessible to policy makers and analysts. Standardization of data formats and interoperability protocols is essential.
Action Steps:
- Adopt enterprise architecture approaches that enforce unified system design and data standards, reducing fragmentation and duplication.
- Expand national health information infrastructure (e.g., adoption of DHIS2 for multi-sector surveillance) to capture real-time inputs from public and community sources.
- Establish cross-sector Application Programming Interfaces (APIs) that allow secure exchange between veterinary, environmental, and human health datasets.
Policy Impact:
This reform enhances situational awareness, strengthens risk profiling of disease emergence, and informs policy decisions with comprehensive evidence.
Application of Advanced Analytics and Machine Learning
Rationale:
Machine learning (ML) and artificial intelligence (AI) can detect patterns and forecast disease threats using large and heterogeneous data sources—beyond human analytical capacity. (ScienceDirect)
Action Steps:
- Institutionalize analytic units within national health agencies with expertise in ML and predictive modeling.
- Deploy AI-enabled tools for early detection of anomalies (e.g., aberrant disease trends) and risk scoring across sectors.
- Integrate operational research models that quantify impact of policy interventions under different scenarios.
Policy Impact:
Predictive analytics accelerate evidence-based policymaking, enabling national authorities to prioritize preventive action over crisis response.
Embedded Spatial Analysis Using Geographic Information Systems (GIS)
Rationale:
GIS enables spatial mapping of disease prevalence, vector habitats, animal movement corridors, and environmental risk factors, yielding actionable insights for targeted interventions.
Action Steps:
- Integrate GIS modules with surveillance platforms to visualize outbreaks and risk gradients at sub-national levels.
- Train policy analysts in geospatial epidemiology to interpret maps for strategic planning.
- Establish policies requiring geotagged reporting from field surveillance teams.
Policy Impact:
Spatial analytics inform resource allocation (e.g., vaccination zones, vector control focus areas) and the formulation of geographically tailored policies.
Strengthened Data Governance and Legal Frameworks
Rationale:
Legal frameworks underpin trust and accountability in digital ecosystems, ensuring data privacy, quality, and equitable use of health information.
Action Steps:
- Enact comprehensive digital health acts defining data ownership, security standards, access rights, and ethical AI use.
- Create cross-sector data governance bodies that oversee standards across human, animal, and environmental health data streams.
- Implement policies that mandate data anonymization and risk assessment protocols before cross-sector data integration.
Policy Impact:
Such governance frameworks accelerate digital transformation while safeguarding citizens’ rights—critical for sustainable policy formulation.
Capacity Building and Workforce Development
Rationale:
Technological reforms require investment in human capital capable of operating and interpreting digital ecosystems.
Action Steps:
- Integrate One Health digital competencies (data science, health informatics, systems analysis) into national public health training institutions.
- Establish continuous professional development pathways for existing health workers to upskill in digital surveillance and analytics.
- Partner with regional technical institutes to expand access to specialized training modules.
Policy Impact:
A digitally literate workforce enhances policy analysis quality and ensures technology investments yield measurable public health returns.
Stakeholder Engagement Platforms and Collaborative Tools
Rationale:
Meaningful intersectoral policy formulation must be supported by digital platforms that facilitate dialogue, shared planning, and community input.
Action Steps:
- Deploy collaborative digital portals where ministries, researchers, and community health workers can share insights and coordinate policy development.
- Integrate mobile reporting tools that enable frontline practitioners to contribute real-time observations, enriching evidence bases for policy decisions.
Policy Impact:
Digital stakeholder engagement improves transparency, strengthens policy legitimacy, and promotes inclusive health governance.
Implementation Framework
A phased implementation is recommended:
- Assessment and Planning: Audit existing systems, identify gaps, and develop a national One Health digital transformation strategy aligned with broader health priorities.
- Pilot Projects: Implement interoperable surveillance platforms and predictive analytics pilots in select provinces to refine system design.
- Scale-Up and Institutionalization: Expand platforms nationwide and formalize governance structures.
- Monitoring and Evaluation: Establish indicators for data quality, policy responsiveness, and system performance.
- Sustainability and Financing: Embed digital health budgets into national planning cycles and explore public-private partnerships for long-term support.
In low-income African countries, the transition to technology-enabled One Health policy formulation is both necessary and attainable. The integration of digital surveillance, advanced analytics, spatial mapping, robust legal frameworks, skilled human resources, and collaborative tools forms the foundation of this transformation. Such reforms will not only improve early detection and response to health threats at the human-animal-environment interface but will also strengthen overall health system resilience. Pragmatic investments in technology, supported by coherent governance and workforce capacity building, will enable low-income African states to formulate data-driven policies that are proactive, equitable, and sustainable.