One Digital Health

The following chapter was authored by the regional project, One Health Data Alliance Africa (OHDAA), funded by the German Federal Ministry of Economic Cooperation and Development (BMZ).

Why One Digital Health ?

The One Digital Health is often unknown in practice. It is an emerging field that leverage data and digital tools to opertionalize the One Health approach. Therefore, it addresses:

Fragmented/disintegrated digital systems and siloed solutions
Lack of standardized reporting and data formats across different sources
Poor implementation of new innovative tools e.g. AI, Big data, analytics
Lack of analytical skillsets for One Health professionals

“One Digital Health is a multidisciplinary approach that crosslinks various disciplines including public health, veterinary medicine, environmental science, informatics, and technology, among others. It calls for physicians, nurses, veterinarians, biologists and environmental sciences to work in close collaboration” (Benis et al. 2021).

Why are Data and Digitalisation important for One Health?

A. As a Foundation for the One Digital Health Approach

Data flows between insitutions dealing with human, animal, and environmental health, offering a comprehensive view of health interrelated interactions
Data enables root cause discovery, early detection, tracking, prevention, and prediction of emerging health threats

B. Act as an essential Tool for Zoonotic Disease Control

Facilitates tracking and prediction of zoonotic hotspots (e.g., rabies, avian flu) where diseases can jump from animals to humans (and vice versa)
Support targeted, data-driven interventions to prevent cross-species disease spread

C. Inform Evidence-Based Policymaking

Provides policymakers with insights based on data to prioritize and allocate resources effectively, focusing on high-risk regions
Ensures impactful and cost-effective interventions

D. Foster Interdisciplinary Collaboration

Data serves as a shared framework for human, animal, and environmental health professionals to work together
Promotes a coordinated response to complex health challenges through unified,and integrated data-supported actions

E. Support Evaluation and Research

Enable a more rapid assessment of intervention effectiveness, identifying successful practices and areas for improvement
Accelerate research on emerging health threats, including antimicrobial resistance and zoonotic spillover risks.

F. Advocate for Public Awareness and Engagement

Data improves transparency, helping to communicate health risks related to wildlife, environmental degradation, and zoonoses
Empowers communities to understand and engage in One Health initiatives, strengthening public health resilience.

How new Technology trends can be applied for One Health?

Environmental factors play a central role in One Health, generating critical data on air quality, land use, climate conditions, and ecological changes. Integrating environmental data with surveillance data from animals, humans, agriculture, and socio-economic data, into One Digital Health platforms offers enormous potential for data-driven decision-making. These large datasets allow the application of data models and artificial intelligence (AI) to uncover the root causes of health risks, identifiy the interconnecteness of One Health sectors and foster cross-sector collaboration. These new technologies improve prediction and prevention of health threats, early response and mangement.

Practical Examples

There are already One Digital Health use cases that have proved how digitalization and data can mobelize the One Health approach.

I- Real-Time notification of zoonotic cases detection to central-level directorates, laboratories, and the Center for Health Emergency Response Operations (CORUS) in Burkina Faso through the OH Electronic Platform in Burkina Faso

II- Enhance the preparadness of the Uganda health system to respond to the expected increased occurrences of climate-sensitive diseases through the Machine Learning Predictive Analytics Model. A model that enables the prediction of trends in disease occurence as a result in changes in climate variables and was piloted in Uganda.

III- Early disease detection, timely reporting, and prompt response at the community, national, regional, and global levels through AfyaData. A smartphone App for One Health disease surveillance from community to national level in africa.

How does One Digital Health look like in Practice?

One Health Data Alliance Africa (OHDAA) project is a regional project in Africa that addresses the topic of One Digital Health in collaboration with various partners. It has identified key challenges related to sharing data and information across sectors when tackling One Health threats. Based on recommendations from the Quadripartite, the project emphasizes the importance of use case-based data sharing to integrate One Health-related systems effectively.

To address these challenges, OHDAA has identified concrete use cases in three countries; Cameroon, Malawi, and Rwanda – as well as in the Intergovernmental Development Authority (IGAD) region. Additionally, the project supports the Africa Union´s Inter-African Bureau for Animal Resources (AU-IBAR) in developing and deploying a continental Digital One Health Platform, which promotes a decentralized Data Mesh architecture to respect data sovereignty.

The following slides showcase the different case studies, highlighting the identified use cases:

Cameroon: “Implementation of a Cameroon One Health Information System”

Project / Institution	One Health Data Alliance Africa, Republic of Cameroon, GIZ
Problem statement	The absence of an integrated platform for human, animal, plant, and environmental health data made it difficult for the different Ministries and Institutions to jointly manage and monitor One Health diseases e.g. zoonotic diseases, malaria.
Approach	– Regional and national evaluation of potential platforms that meet the specified needs – Validation and deployment of the Cameroon One Health Information System (COHIS) to integrate One Health data – Training of partners in data engineering and formalization of the needs that the One Health Digital System will address – Training of administrators and users of the COHIS system – Collection of use cases from the different One Health sectors and implementation of pipelines and dashboards
Data Methods/Platform	– Examined sector priorities and map with available data – Conducted descriptive and predictive anaylses based on the defined use cases
Results and impact (March-June 2025)	Results: – Market anaylsis to decide for system and implementation (according to the evaluation criteria) – Sectoral use cases implementation including Zoonosis/Integrated Disease Surveillance Report System (IDSR), Phytostatica residues, Earth observation based disease prediction, Ecological management of toxic and hazardous waste Impact: – Early response and management of diseases across sectors – Inform policies and decision making on potential disease outbreaks – Monitor effectivenss of One Health interventions – Strengthened IT and Analytical capacities across One Health sectors
Collaboration and partnerships	Cameroon One Health Platform (OHP), Ministry of Health IT Unit, National Observatory on Climate Change (ONACC)
Lessons learned and recommendations	– Data governance is essential to overcome political economy bottlenecks and ensure data security and quality – Incremental implementation is necessary in the absence of a strong top-down leadership from top government-level – Technically ambitious approaches instrinsically motivate implementers and users

Malawi: “Rabies Use Case, Semantic interoperability”

Project/ institution	One Health Data Alliance Africa-The Republic of Malawi
Problem statement	The lack of a unified and integrated rabies data system in Malawi hampers accurate detection, monitoring, and response to rabies cases in both humans and animals. Fragmented data collection, limited digitization, and inconsistent data definitions across human, animal, and environmental sectors make it difficult to understand the true burden of rabies, track outbreaks, risk mapping and implement timely interventions.
Approach	Stakeholder consultation resulted in a proposed use case on Semantic Interoperability. This means having a common shared vocabulary and terminology definitions for data elements based on established standards. This enable sectors to exchange information more efficiently by ensuring that there is the same interpretation and understanding of the rabies data. In parallel, One Health governance has been also supported.
Data Methods/ Platforms	-Foster collaboration among stakeholders to align on key rabies indicators, data definitions, and standards -Integrate rabies data into an existing government-owned platform (One Health Surveillance Platform (OHSP)) -Explore advanced analytics and modeling of the integrated data for better decision-making
Result and impact (March-June 2025)	Results: – Clearly define rabies terminology definitions for data elements -Improved quality and accessibility of data to support decision-making -Enhanced rabies data interoperability to enable seamless integration and data use -Fostered collaboration among stakeholders through data sharing agreements -Strengthened data analysis Impact: – Public Health Insititute in Malawi tracks accurately rabies cases in humans – Reduce human rabies fatalities through rapid interventions and prohylactic measurements – Foster the developement of AI & ML models using the structured rabies databases – Prioritize high-risk areas for vaccination campaigns and programs, and targeted rabies control measures
Collaboration and partnerships	Ministry of Agriculture (DAHLD) Ministry of Wildlife and forestry, Ministry of Health (DHD), Public Health Institute Malawi ( PHIM), Mission Rabies, Community Empowement initiative, Lilongwe Society for the Protection and Care of Animals
Lessons learned and Recommendations	Rabies eradication and control require robust One Health data ecosystem with defined standards and terminologies by all partners and stakeholders involved

Rwanda: “AMR and Zoonotic Diseases-Robust Disease surveillance”

Project / Institution	One Health Data alliance Africa, Rwanda, GIZ
Problem statement	Rwanda’s disease surveillance system lacks full integration of human, animal, and environmental health data, making it difficult to accurately track and respond to antimicrobial resistance (AMR) and zoonotic diseases including MPOX and Marburg. While the Electronic-Integrated Disease Surveillance Response (eIDSR) system provides a foundation for disease reporting, it does not support: – Seamless data exchange across sectors, leading to gaps in surveillance, delayed outbreak detection, and fragmented responses. – Advanced analytics which limits Rwanda’s ability to implement a proactive, One Health-driven approach.
Approach	– Enhance AMR surveillance through improved integration of multiple data sources – Strengthen wildlife sector participation in disease surveillance – Implement advanced analytics for better prediction and response – Build sustainable capacity across all participating sectors
Collaboration & Partnership	Rwanda Biomedical Center (RBC), Ministry of Health
Data methods/ Platform	Electronic Infectious Disease Surveillance and Response (eIDSR), Sentinel Sites Open Clinics and DHI2 based systems.
Result & Impact	Impact • Better management of AMR cases in clinical facilities through enhanced predictive capabilities and the implementation of advanced analytics. This will enable more accurate forecasting of disease trends, allowing for timely and targeted responses to emerging health threats • Reduction of diseases outbreaks with wildlife origin through sustainable Surveillance Capacity • Strengthened infrastructure, training, and collaboration across sectors will ensure a long-term, resilient disease surveillance system, supporting Rwanda’s One Health approach
Lessons Learned and Recommendations

IGAD: “Strengthening Malaria Prediction in Ethiopia through Advanced Hybrid Models”

Project /Institution	One Health data Alliance Africa-IGAD
Problem statement	Significant report of morbidity and mortality rates related to malaria, especially among vulnerable populations like pregnant women and children in the Intergovernmental Authority on Development (IGAD) region. These epidemics are frequently made worse by socioeconomic conditions, climate change, and inadequate health systems. As per the report: “More than one in five children aged under five years were infected with malaria. This suggests the rate of under-five malaria is far off the 2030 national malaria elimination programme of Ethiopia”.
Approach	Use of a disease surveillance platform that leverages comprehensive satellite datasets, epidimiological, socio-economic data using AI tools to predict malaria outbreaks in Ethiopia.
Collaboration & partnerships	Ministry of Health, Ethiopia Public Health Institute, Malaria Control Program, IGAD
Data methods / Platform	Time Series Analysis: Analyzing historical malaria case data to identify trends, seasonality, and potential outbreak patterns. Geographic Information Systems (GIS): Map malaria cases, environmental factors, and social determinants of health to identify high-risk areas. Remote Sensing: Utilize satellite imagery and remote sensing data to monitor environmental factors like vegetation, land use, and water bodies that influence mosquito breeding.
Results & Impact	Result: Strengthening Malaria prediction in Ethiopia through advanced hybrid models Impact: – Proactive Interventions: By enabling the pre-positioning of resources (such as medications, bed nets, and staff) in high-risk locations, early alerts would guarantee a prompt and efficient response. – Targeted Resource Allocation: By more effectively allocating resources to regions with the highest anticipated risk, initiatives can have a greater impact. – Decreased Morbidity and Mortality: Malaria cases, hospitalizations, and fatalities can be considerably decreased with early detection and management, especially for susceptible groups.
Lessons Learned & Recommendations	Through One Health approach, a robust and effective malaria prediction model that contributes significantly to the reduction of malaria burden in the country can be developed.

AU-Digital One Health Platform (DOHP)

Project/Institution	Africa Union-One Health Data Alliance Africa (AU-OHDAA)
Objective	Enhances the digital transformation of One Health governance and management across Africa
Problem	Inefficencies of diseases control, response coordination, resource management and outbreak prediction across One Health sectors due to lack of data sharing and collaboration
Approach	– Development of principles for exchange and use of One Health Data in Africa, One Health information architecture and information policy – Supports the Continental AU-Digital One Health Platform which ensures data sovereignty, scalability, and cross-sectoral collaboration, aligning with the African Union’s One Health priorities.
Collaboration & Partnership	African Union Inter-African Bureau for Animal Resources (AU-IBAR), Africa Center for Disease Control and Prevention (A CDC), Regional Economic Communities (RECs), Member States
Data methods / Platform	Pandemic analytics, granular data, federated decentralized platform powered by open standards; which allows different sectors (diverse data sources) to work together through sharing data securely while keeping control over their own information.
Results	Development of AU-Digital One Health Platform (nominated for global public good) with the following features: 1. Domain-Oriented Data Ownership • Each team or department that work with data take ownership of their data 2. Data as a Product • Raw data is transformed into a polished, usable product tailored to meet specific needs • Each dataset comes with clear documentation—like definitions, schemas, and usage guidelines— • The team responsible tracks quality metrics, like accuracy and timeliness 3. Self-Serve Data Infrastructure • Non-technical teams can manage and use data without relying on specialized IT support, thanks to intuitive tools 4. Federated Computational Governance •Establishes overarching policies (like security standards and naming conventions) while giving each team the flexibility to manage their own data