The One Health approach, which integrates human, animal, and environmental health systems, is widely recognized as essential for addressing complex public health challenges such as zoonotic diseases, antimicrobial resistance (AMR), food safety threats, and climate-driven health risks. Low-income African countries are particularly dependent on effective One Health systems due to high levels of human–animal interaction, reliance on livestock-based livelihoods, weak environmental protection mechanisms, and recurrent infectious disease outbreaks. However, the practical implementation of One Health in these contexts is fundamentally constrained by persistent data deficiencies. Inadequate, fragmented, and low-quality data undermine evidence-based decision-making and significantly weaken policy development processes.
This article examines the nature of data gaps in public One Health implementation in low-income African countries and analyzes their systemic impacts on policy formulation, prioritization, and effectiveness.
The Centrality of Data in One Health Policy Development
Effective One Health policy formulation depends on timely, accurate, and interoperable data across three domains:
- Human health (disease surveillance, morbidity and mortality data, laboratory diagnostics)
- Animal health (livestock and wildlife disease surveillance, veterinary reporting, production systems)
- Environmental health (land use, climate variability, water quality, ecosystem degradation)
Data enable governments to identify risks, quantify disease burden, assess transmission pathways, allocate resources, and evaluate policy outcomes. In the absence of robust data systems, policies are often reactive, poorly targeted, and inefficient.
Nature and Sources of Data Gaps in Low-Income African Countries
- Fragmented and Siloed Data Systems
One of the most critical challenges is the fragmentation of data systems across ministries and agencies. Human health, veterinary services, wildlife authorities, and environmental agencies typically operate independent surveillance and reporting platforms with limited interoperability. This fragmentation prevents integrated analysis of zoonotic risks and obscures linkages between environmental drivers and disease emergence.
- Weak Surveillance Infrastructure
Many low-income African countries rely on passive, facility-based surveillance systems that underreport disease events, particularly in rural and pastoralist communities where formal health and veterinary services are limited. Wildlife and environmental surveillance systems are especially underdeveloped, resulting in significant blind spots in early warning mechanisms.
- Limited Laboratory and Diagnostic Capacity
Inadequate laboratory infrastructure limits confirmation of disease events in both humans and animals. As a result, suspected zoonotic outbreaks often remain unverified, leading to incomplete datasets and unreliable epidemiological profiles. This weakens the empirical foundation for policy formulation.
- Inconsistent Data Quality and Standardization
Data that are collected frequently suffer from inconsistencies in case definitions, reporting formats, spatial resolution, and temporal frequency. Lack of standardized indicators across sectors prevents aggregation and comparative analysis, reducing the utility of data for policy design.
- Underinvestment in Environmental Data Collection
Environmental health data—such as ecosystem health indicators, climate variables, land-use change, and pollution levels—are often missing or outdated. This gap severely limits understanding of upstream determinants of zoonotic spillover and undermines preventive policy development.
Impact of Data Deficiencies on One Health Policy Development
- Weak Evidence Base for Policy Decisions
The absence of integrated and reliable data forces policymakers to rely on anecdotal evidence, external studies, or international risk models that may not reflect local contexts. This results in policies that are poorly adapted to national or sub-national realities and less effective in addressing actual risk drivers.
- Delayed and Reactive Policy Responses
Data gaps reduce the capacity for early detection of emerging health threats. Policies are often developed only after outbreaks have escalated into crises, leading to emergency-driven responses rather than preventive or risk-based strategies. This reactive approach increases human, economic, and ecological costs.
- Misallocation of Limited Resources
In low-income settings, public health resources are extremely constrained. Without accurate data on disease burden, geographic risk distribution, and transmission dynamics, resource allocation decisions are frequently inefficient. High-risk areas may be underfunded, while low-impact interventions receive disproportionate investment.
- Inadequate Cross-Sector Policy Integration
One Health policies require coordinated action across multiple sectors. Data fragmentation reinforces institutional silos, making it difficult to design coherent policies that align human health, animal health, agricultural development, and environmental protection objectives. As a result, policies may conflict or operate in parallel rather than synergistically.
- Limited Policy Evaluation and Learning
Data deficiencies also impede monitoring and evaluation of One Health policies. Without baseline data and reliable indicators, governments cannot assess policy effectiveness, identify unintended consequences, or adapt strategies over time. This weakens institutional learning and perpetuates ineffective policy cycles.
Structural Drivers of Data Gaps
- Governance and Institutional Constraints
Weak governance frameworks limit coordination, data sharing, and accountability across sectors. In many countries, there are no formal mandates or legal instruments requiring intersectoral data integration for One Health purposes.
- Human Resource and Technical Capacity Gaps
Shortages of skilled epidemiologists, data scientists, health informaticians, and environmental analysts constrain data generation and analysis. Existing personnel often lack training in integrated data systems and One Health analytics.
- Financial Limitations
Data systems require sustained investment in infrastructure, maintenance, and human capacity. In low-income African countries, donor-driven, project-based funding leads to fragmented and unsustainable data initiatives that collapse once external funding ends.
- Digital Infrastructure Deficits
Limited connectivity, unreliable power supply, and inadequate digital infrastructure in rural areas restrict real-time data collection and transmission, exacerbating reporting delays and data loss.
Policy Implications and Strategic Consequences
The lack of data fundamentally undermines the credibility, effectiveness, and sustainability of One Health policies in low-income African countries. It contributes to:
Reduced ability to attract domestic and international investment due to weak evidence frameworks
Persistent vulnerability to zoonotic outbreaks
Ineffective AMR containment strategies
Poor integration of climate and environmental risks into health planning