Challenges and Opportunities in Implementing One Health Surveillance Systems

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The Need for One Health Surveillance

Recent health crises such as COVID-19, Ebola, Zika, and avian influenza have underscored the need for an integrated surveillance system that accounts for the interconnectedness of human, animal, and environmental health. One Health surveillance aims to bridge these sectors through data sharing, collaborative monitoring, and early warning systems to prevent and mitigate outbreaks.

Despite its clear benefits, implementing One Health surveillance remains a challenge due to fragmented data collection, lack of leadership, and insufficient integration between different sectors. This article explores the key challenges in operationalizing One Health surveillance systems and highlights the opportunities for improvement, particularly through digital health innovations and global collaboration.

Challenges in Implementing One Health Surveillance

1. Fragmented Governance and Lack of Coordination

One of the primary obstacles to implementing One Health surveillance is the lack of a unified governance structure. Human health, animal health, and environmental monitoring are typically managed by separate agencies with distinct regulatory frameworks, priorities, and funding sources.

Why This Matters

Without clear coordination, different sectors may collect redundant or conflicting data, leading to inefficiencies in response efforts. Additionally, the absence of a centralized authority means that responsibilities for disease surveillance and outbreak response may be unclear, resulting in delays in decision-making.

Real-World Example

• European One Health Surveillance: The OH-EpiCap study evaluating multi-sectoral surveillance systems in Europe found that most countries lacked a formal mechanism for cross-sector coordination. As a result, responses to zoonotic disease outbreaks were often fragmented, with individual agencies focusing only on their respective domains.
• H5N1 Avian Influenza Outbreak: During the global outbreak of H5N1 avian influenza, delayed coordination between veterinary and human health sectors contributed to the virus’s spread. The lack of a unified approach meant that surveillance efforts in poultry populations were not efficiently linked to human health response systems.

Consequences

• Delayed response times: Without a coordinated approach, valuable time is lost in responding to disease outbreaks.
• Inconsistent policies: Different sectors may implement conflicting policies, leading to gaps in surveillance.
• Inefficient resource allocation: Duplication of efforts results in wasted funding and resources.

2. Data Silos and Inconsistent Sharing Practices

For One Health surveillance to be effective, data from different sectors must be findable, accessible, interoperable, and reusable (FAIR). However, health data is often stored in silos, with limited mechanisms for integration across disciplines.

Why This Matters

A lack of data-sharing agreements means that critical information about zoonotic disease outbreaks may not reach relevant stakeholders in time. Additionally, different sectors use varying data formats and reporting standards, making interoperability difficult.

Real-World Example

• COVID-19 Pandemic: During the early stages of the COVID-19 outbreak, delays in sharing genomic sequencing data led to difficulties in tracking viral mutations. The lack of standardized data-sharing platforms between countries hindered global response efforts.
• Antimicrobial Resistance (AMR) Surveillance in Portugal and France: A study on AMR surveillance found that while data was collected from human hospitals, veterinary clinics, and environmental monitoring agencies, it was not harmonized or shared efficiently. This resulted in an incomplete picture of AMR trends, delaying appropriate interventions.

Consequences

• Inability to detect outbreaks early: When data is not shared across sectors, early warning signs may be missed.
• Redundant data collection efforts: Lack of interoperability leads to inefficient use of resources.
• Misinformed decision-making: Policy recommendations may be based on incomplete or outdated data.

3. Limited Technological Integration

While digital technologies such as artificial intelligence (AI), big data analytics, and remote sensing have transformed many areas of health surveillance, their application in One Health remains limited. Many countries still rely on traditional surveillance methods, such as manual reporting and paper-based data collection.

Why This Matters

Modern disease surveillance requires real-time data integration from multiple sources, including satellite imagery, mobile health applications, and electronic health records. However, many One Health initiatives lack the necessary digital infrastructure to support such integration.

Real-World Example

• AI in Disease Prediction: Some countries have successfully integrated AI for disease prediction, such as using machine learning to track dengue fever outbreaks based on weather and mosquito population data. However, most One Health surveillance systems do not have access to such predictive tools.
• Blockchain for Secure Data Sharing: Blockchain technology offers a solution for ensuring secure and tamper-proof data sharing across different sectors. Despite its potential, few One Health initiatives have adopted blockchain-based surveillance systems due to financial and technical constraints.

Consequences

• Slow adaptation to emerging threats: Without advanced technologies, One Health surveillance systems struggle to detect new pathogens in real time.
• Higher operational costs: Manual data processing is labor-intensive and prone to errors.
• Limited scalability: Traditional surveillance methods cannot handle the increasing complexity of global disease monitoring.

4. Resistance to Cross-Sector Collaboration

Different professional cultures, priorities, and incentives between human health, veterinary, and environmental scientists create barriers to effective collaboration.

Why This Matters

One Health surveillance requires an integrated approach, but professionals from different fields may have conflicting views on data sharing, budget allocation, and decision-making authority.

Real-World Example

• Hantavirus Outbreaks in the Americas: A lack of collaboration between ecologists (who monitor rodent populations) and human health professionals led to delays in recognizing the links between environmental changes and hantavirus outbreaks.
• Competing Priorities in One Health: A study found that while veterinarians prioritized food safety and zoonotic disease prevention, environmental scientists focused more on biodiversity conservation. These differing priorities often led to misalignment in One Health strategies.

Consequences

• Reduced effectiveness of surveillance programs: Without cooperation, data and expertise remain isolated.
• Slower response times during outbreaks: Agencies may hesitate to act if they do not have full confidence in data from other sectors.
• Missed opportunities for preventive interventions: Collaboration is crucial for addressing emerging health threats before they escalate.

5. Lack of Clear Impact Evaluation Metrics

One of the biggest gaps in One Health surveillance is the lack of standardized impact evaluation frameworks to measure the effectiveness of integrated surveillance efforts.

Why This Matters

Governments and funding agencies need concrete evidence that One Health surveillance is cost-effective and leads to better health outcomes. Without proper evaluation, decision-makers may be reluctant to invest in One Health initiatives.

Real-World Example

• Foodborne Disease Monitoring in Europe: A review found that while collaboration between human and veterinary sectors improved data collection, there was little evidence to show whether it led to reduced disease incidence or improved policy decisions.
• Cost-Benefit Analysis of One Health Programs: Few studies have conducted comprehensive economic assessments of One Health surveillance, making it difficult to demonstrate financial returns on investment.

Consequences

• Difficulty in securing long-term funding: Without clear metrics, governments and donors may be hesitant to support One Health initiatives.
• Unclear policy direction: Decision-makers may not prioritize One Health approaches if their benefits are not well-documented.
• Limited ability to improve programs: Without ongoing evaluation, it is difficult to refine and optimize surveillance strategies.

Opportunities for Improvement

1. Establishing Formal Governance Structures

Why This Matters

A major hurdle in One Health surveillance is the absence of a centralized governing body that ensures coordination between human, animal, and environmental health sectors. Without structured oversight, surveillance efforts remain fragmented, leading to delays in outbreak response and ineffective resource allocation.

Proposed Solutions

Creation of National and International One Health Governance Bodies:

• Governments can establish dedicated One Health agencies that oversee intersectoral disease surveillance, ensuring that different ministries work in sync.
• Example: The U.S. One Health Office, established by the Centers for Disease Control and Prevention (CDC), has successfully facilitated coordination between human and veterinary health agencies to improve zoonotic disease monitoring.

Legislative and Policy Integration:

• Policymakers should formalize One Health approaches into national disease control strategies by integrating regulations across ministries of health, agriculture, and the environment.
• Example: In Thailand, the government enacted policies mandating One Health collaboration for zoonotic disease control, which has strengthened outbreak response efforts.

Cross-Sectoral Committees:

• Governments can form One Health task forces composed of representatives from public health, veterinary medicine, and environmental science to ensure a unified response.

Potential Benefits

• Faster decision-making during disease outbreaks
• Better resource allocation and reduced duplication of efforts
• Clear accountability and responsibilities across sectors

2. Leveraging Digital Technologies

Why This Matters

Traditional surveillance systems rely on slow, manual reporting methods that are prone to delays and data loss. Modern digital health tools—including artificial intelligence (AI), machine learning, blockchain, and big data analytics—can transform One Health surveillance by enabling real-time disease monitoring and predictive modeling.

Proposed Solutions

Artificial Intelligence (AI) and Big Data Analytics

• AI can be used to analyze vast datasets from multiple sources (hospitals, veterinary clinics, environmental monitoring) to detect disease patterns early.
• Example: Google’s AI-based "Arbovirus Prediction Model" uses climate data to predict dengue outbreaks, improving early warning systems.

Blockchain for Secure and Transparent Data Sharing

• Blockchain technology provides a tamper-proof system for securely sharing data across different health sectors while maintaining privacy and accountability.
• Example: The EU Blockchain Observatory is exploring ways to integrate blockchain into health data systems to facilitate secure One Health data exchanges.

Remote Sensing and Geographic Information Systems (GIS)

• GIS technology allows real-time mapping of disease hotspots, helping authorities track environmental and zoonotic risks.
• Example: In Africa, remote sensing is being used to monitor Rift Valley fever outbreaks by analyzing rainfall patterns that affect mosquito populations.

Potential Benefits

• Faster and more accurate disease detection
• Secure and transparent cross-sectoral data sharing
• Improved predictive analytics for outbreak prevention

3. Implementing FAIR Data Principles

Why This Matters

Data from human, veterinary, and environmental sectors are often stored in separate, incompatible formats that make integration difficult. Ensuring that data is Findable, Accessible, Interoperable, and Reusable (FAIR) is essential for effective One Health surveillance.

Proposed Solutions

Standardized Data Collection Frameworks

• Governments and international organizations should establish harmonized data collection standards to ensure consistency.
• Example: The Global Burden of Animal Diseases (GBADs) Initiative has developed data-sharing frameworks that allow the seamless integration of veterinary and human health data.

Open-Access Platforms for Data Sharing

• Publicly available databases should be created to facilitate real-time data exchange between One Health stakeholders.
• Example: The Global Initiative on Sharing All Influenza Data (GISAID) enables open-access sharing of influenza genomic data, allowing for faster response to emerging flu strains.

Integration of Digital Health Records Across Sectors

• Healthcare and veterinary systems should be interconnected so that zoonotic disease cases are reported in a unified system.
• Example: The African Union’s One Health Data Platform integrates human and animal health records for real-time disease tracking.

Potential Benefits

• Enhanced cross-sectoral collaboration and trust
• Faster outbreak detection and response
• Reduction in duplicated efforts and data inconsistencies

4. Strengthening Cross-Sector Training and Collaboration

Why This Matters

One of the greatest barriers to implementing One Health surveillance is professional silos, where human, animal, and environmental health professionals work in isolation. Training programs that promote interdisciplinary collaboration can break down these barriers.

Proposed Solutions

Interdisciplinary Training Programs

• Universities and professional organizations should develop One Health curricula to train professionals across disciplines.
• Example: The University of California, Davis runs a One Health PhD program that integrates human, animal, and environmental health education.

Joint Simulation Exercises and Workshops

• Cross-sectoral stakeholders should participate in joint outbreak response simulations to improve coordination.
• Example: The World Health Organization (WHO) has conducted global One Health training exercises to prepare for future pandemics.

International One Health Fellowships and Exchange Programs

• Governments and research institutions can fund global exchange programs to encourage knowledge-sharing between professionals.
• Example: The One Health European Joint Programme (OHEJP) supports international research collaborations on zoonotic diseases.

Potential Benefits

• Improved collaboration between human, animal, and environmental health experts
• Increased awareness of One Health concepts in public health policy
• Better-prepared workforce for responding to global health threats

5. Establishing Clear Impact Evaluation Metrics

Why This Matters

Many One Health surveillance initiatives lack clear criteria to measure their success, making it difficult to demonstrate their value to policymakers and funders.

Proposed Solutions

Development of Standardized Metrics for One Health Success

• Governments and research institutions should define key performance indicators (KPIs) for measuring the effectiveness of One Health surveillance.
• Example: The Global Health Security Index (GHSI) ranks countries based on their ability to prevent and respond to health threats.

Economic Cost-Benefit Analyses of One Health Programs

• Demonstrating the financial benefits of integrated surveillance can encourage policymakers to invest in One Health.
• Example: A World Bank study found that investing in One Health approaches for zoonotic disease control could save billions in outbreak response costs.

Regular One Health Surveillance Audits and Reporting

• Countries should conduct periodic evaluations of their One Health surveillance systems to identify areas for improvement.
• Example: The European Centre for Disease Prevention and Control (ECDC) regularly reviews One Health collaboration effectiveness in EU member states.

Potential Benefits

• Stronger advocacy for One Health funding
• Clearer demonstration of health and economic benefits
• Improved long-term sustainability of surveillance programs

Conclusion

Thank you for taking the time to engage with this article. GBL4 is committed to shedding light on critical topics around zoonosis, patient care, and the broader implications of One Health. We invite you to delve deeper into these subjects by visiting GBL4.org. Stay updated with our latest insights by subscribing to our newsletter and following us on LinkedIn. Your thoughts and feedback are invaluable to us, so please feel free to comment on the article and join the conversation. Together, we can drive meaningful change and foster a better understanding of the interconnectedness of our world.

References

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