The need for a central repository of OH biosafety and biosecurity surveillance data including data aggregation, collation and visualization

Introduction

Emerging infectious diseases like COVID-19 pose significant threats to human health, animal health and the environment. Additionally, biological warfare, bioterrorism, biological accidents, and the misuse of dual-use biotechnology present serious challenges to global biosecurity. Enhancing early surveillance capabilities is crucial for establishing a unified biosecurity shield for global health. Effective surveillance can provide early warnings and situational awareness of biosecurity risks. 

Uganda faces critical challenges in managing biosafety and biosecurity threats, largely due to the fragmented data across public health, veterinary services, and environmental agencies. Currently, the lack of a centralised system for aggregating and visualising data significantly hampers the country's ability to detect, assess, and respond to biological threats that cut across the human, animal, and environmental health domains, and insufficient international cooperation.

This issue is paramount because timely and coordinated responses are essential to preventing disease outbreaks and mitigating environmental impacts (Hao et al., 2022). Addressing the need for a centralised data aggregation system is crucial for enhancing Uganda's public health security and ensuring its active participation in regional and global biosecurity efforts.

Globally, several countries have successfully implemented centralised systems for biosafety and biosecurity data aggregation, serving as models for Uganda. For example, the European Union's RAS-BICHAT system integrates data from the human, animal, and environmental health sectors to facilitate rapid communication and standardised reporting across member states (Kjellén & Olsson, 2009). This system has been instrumental during crises such as the anthrax attacks in the early 2000s and the COVID-19 pandemic, where swift data aggregation and sharing were vital for managing these threats.

Similarly, the United States' National Biosurveillance Integration Center (NBIC) consolidates biosurveillance data and serves as a bridge between federal, state, local, territorial, and tribal partners to integrate information from thousands of sources about biological threats to human, animal, plant, and environmental health, improving early warning and situational awareness (Novossiolova et al., 2020). This integration is crucial for national situational awareness and improves the ability to detect and respond to biological threats comprehensively. During the 2014 Ebola outbreak, the NBIC played a key role in coordinating the national response by integrating data from multiple sectors, highlighting the effectiveness of a One Health approach in biosecurity.

Canada's Global Public Health Intelligence Network (GPHIN) is another notable example, serving as an early warning system that gathers and disseminates information about potential public health threats globally. GPHIN's integration of data from human, animal, and environmental health sources allows for the early detection and response to biosafety incidents with One Health implications . The system's effectiveness was demonstrated during the SARS outbreak in 2003, when it was among the first to alert the world to the emerging threat’.

In Africa, South Africa also has mechanisms in place under the South African Nuclear Energy Corporation (NECSA), which works closely with various government agencies to monitor and control nuclear materials. These efforts are part of a broader strategy to counter the proliferation of weapons of mass destruction, particularly nuclear weapons, in the region  (Mangena, 2021)

In Uganda, although there is a National Task Force on Public Health Emergencies (NTFP), an interdisciplinary team that integrates health, veterinary, and environmental sectors to respond to public health emergencies, including biological threats that could be considered weapons of mass destruction, there are still notable challenges of data integration.

Challenges in Integration of data for Biosecurity 

Uganda faces significant challenges in applying integrated data systems across various sectors, such as public health, veterinary services, and environmental management. Unlike other regions where such systems have achieved success, Uganda’s efforts are hindered by the fragmented nature of data collection and analysis. Each sector operates independently, lacking a standardised reporting protocol, which creates silos and impedes accurate cross-sector risk assessments. This often results in delayed and uncoordinated responses to emerging biosecurity threats.

For instance, in the public health sector, data on zoonotic diseases like Ebola and Marburg is not consistently shared with veterinary services, despite the critical role animals play as vectors in these outbreaks. A study by the World Health Organization highlighted this issue, noting the delayed response to the Marburg virus outbreak in 2017 due to poor data sharing between sectors (WHO, 2018). Furthermore, the absence of real-time data-sharing mechanisms exacerbates the situation, as critical information is frequently unavailable when needed, further compromising the capacity to manage these threats effectively.

The fragmented approach to data management creates a "foggy" landscape, where biological threats are not fully understood or addressed promptly, leaving significant gaps in the country’s biosecurity framework. For example, the Uganda One Health Strategic Plan (2018-2022) recognized these gaps but has struggled to implement effective cross-sectoral collaboration due to the same systemic issues (Uganda Ministry of Health, 2018). These challenges highlight the urgent need for coordinated, real-time data-sharing mechanisms to enhance Uganda's biosecurity capabilities.

Proposed solutions for strengthening biosecurity through integrated data systems.

To effectively address the biosecurity challenges, a tailored approach to data aggregation and visualisation is essential. A critical first step is the establishment of a centralised repository for biosafety and biosecurity data, integrating information from a one health perspective to offer a comprehensive view of biological threats across human, animal, and environmental health domains. The one health platform states that the memorandum of understanding signed between sectors is implemented to steer collaboration from the Ministry of Agriculture, Animal Industry, and Fisheries, and the National Environment Management Authority to ensure cross-sector data integration. Securing financial resources is crucial, and the government could explore partnerships with international organisations like the World Health Organization (WHO) and the Food and Agriculture Organization (FAO), as well as NGOs and private sector partners, particularly in technology and capacity building.

Implementing standardised reporting protocols across sectors is vital for consistency and accuracy, with protocols developed through collaboration between involved agencies and supported by international experts experienced in similar systems. Additionally, deploying real-time data-sharing tools and visualisation platforms is critical for the rapid dissemination of information, enabling quicker, coordinated responses to potential threats. Uganda can draw inspiration from existing models like RAS-BICHAT and the National Biosurveillance Integration Center (NBIC), adapting their best practices to the country’s unique context (Centers for Disease Control and Prevention [CDC], 2021). Incorporating Geographic Information Systems (GIS) and machine learning algorithms can further enhance the system's capacity to analyse and visualise complex data sets, as GIS can map and analyse spatial data, and machine learning algorithms can identify patterns and predict potential biosecurity threats, enabling preemptive action and improving overall risk management (Talukdar et al., 2022).

However, several challenges must be addressed. Financial constraints, governance and coordination issues, and the need for capacity building could impede progress. A phased approach to implementation, starting with pilot programs in high-risk regions, could demonstrate the system’s efficacy and attract further funding. Establishing an inter-agency task force dedicated to biosecurity data management would help overcome governance challenges by providing clear leadership and accountability. Finally, training programs and workshops should be organised to build capacity within relevant agencies, ensuring the availability of skilled personnel to manage and interpret the data. By taking these steps, Uganda can significantly strengthen its biosecurity framework, transitioning from a fragmented system to one that is more integrated and responsive.

Impact for enhancing Uganda’s biosecurity 

Implementing a centralised data repository in Uganda would have a profound impact on the country’s biosafety and biosecurity framework. It would provide policymakers and public health officials with reliable, real-time data, facilitating more informed decision-making and quicker responses to emerging threats making progress towards the 7-1-7 target, which aims to enhance outbreak prevention and response to safeguard communities against future public health threats (Bochner et al., 2023). It also would be essential to save lives and/or improve people's health in the country improving overall wellbeing, productivity,  and economy Moreover, by aligning with global best practices, Uganda could strengthen its role in regional and global biosafety and biosecurity initiatives, contributing to broader pandemic preparedness and response strategies.

This centralised approach would also empower Uganda to engage more effectively in international collaborations, sharing data and insights with other countries and organisations involved in global health security efforts. The creation of this repository would represent a significant step forward in addressing the current gaps in Uganda's biosecurity landscape, ultimately leading to better health outcomes for people, animals, and the environment alike .

Tools and methodologies for integrating and visualising data 

The literature on data aggregation and visualisation methods for biosecurity highlights the importance of integrating data from multiple sectors and utilising advanced tools to enhance biosecurity efforts. Some of the key methods and tools discussed in the literature include:

1. Geographic Information Systems (GIS): GIS is widely used in biosecurity to map and analyse spatial data, providing visualisations of disease outbreaks and other threats across different regions. This technology allows for the integration of data from various sources, enabling a comprehensive view of biosecurity risks .
2. Surveillance Dashboards: Interactive dashboards are essential for presenting real-time data on potential threats. These tools aggregate data from multiple sources, allowing users to monitor trends and make informed decisions quickly. Dashboards have been particularly useful during pandemics, such as COVID-19, where they provided critical information for managing the spread of the virus .
3. Data Warehousing: Data warehousing involves consolidating large volumes of data from disparate sources into a centralised repository. This approach facilitates efficient storage, retrieval, and analysis of data, which is crucial for timely decision-making and risk assessment in biosecurity .
4. Machine Learning and Predictive Analytics: The application of machine learning in biosecurity has proven effective in identifying patterns and predicting potential threats. These tools are especially valuable for the early detection of emerging infectious diseases, allowing for preemptive measures to be taken .
5. Integrated Disease Surveillance and Response (IDSR): IDSR is a strategy used in many African countries, including Uganda, to improve the availability and use of surveillance data. The IDSR framework emphasises the integration of data from various health sectors and the use of standardised tools for data collection and analysis .

This project  has provided an analysis of biosecurity challenges, particularly focusing on the fragmented nature of data systems across public health, veterinary services, and environmental management. By examining successful global models, such as the EU’s RAS-BICHAT, the U.S. NBIC, and Canada’s GPHIN, I propose the establishment of a centralised data repository, standardised reporting protocols, and the use of advanced tools like GIS and machine learning to enhance Uganda's biosecurity framework. Moving this forward involves implementing these recommendations through pilot programs in high-risk regions, securing funding, and establishing an inter-agency task force. There is potential for a follow-up project to apply these solutions practically in Uganda, and I invite collaboration from professionals and organisations interested in advancing this critical work.

References:

1. European Centre for Disease Prevention and Control (ECDC). (2020). Rapid Alert System for Biological and Chemical Attacks and Threats (RAS-BICHAT).
2. World Health Organization (WHO). (2020). Joint external evaluation of IHR core capacities of the Republic of Uganda.
3. U.S. Department of Homeland Security. (2020). National Biosurveillance Integration Center (NBIC).
4. Hao, R., Liu, Y., Shen, W., Zhao, R., Jiang, B., Song, H., Yan, M., & Ma, H. (2022). Surveillance of emerging infectious diseases for biosecurity. Science China Life Sciences, 65(8), 1504–1516. https://doi.org/10.1007/s11427-021-2071-x
5.  
6. Public Health Agency of Canada. (2021). Global Public Health Intelligence Network (GPHIN).
7. Mboowa, G., et al. (2018). Strengthening integrated disease surveillance and response in Uganda.
8. Nyakarahuka, L., et al. (2017). Ebola Virus Disease Outbreaks in Uganda: Past and Present.
9. ECDC. (2020). Overview of COVID-19 surveillance.
10. Mangena, J. (2021). Development of a strategic decision-making model for the South African nuclear sector [Thesis, North-West University (South Africa)]. https://repository.nwu.ac.za/handle/10394/38401
11. WHO. (2020). Managing Epidemics: Key Facts About Major Deadly Diseases.
12. CDC. (2019). The One Health approach to biosafety and biosecurity.
13. DHS. (2020). The Role of NBIC in National Biosurveillance.
14. National Institutes of Health (NIH). (2019). The use of GIS in public health.
15. European Commission. (2018). Artificial Intelligence: A European Perspective.
16. Public Health England. (2020). The importance of real-time data sharing in public health emergencies.
17. WHO. (2020). Pandemic preparedness and response strategies.
18. Rogers, D. J., et al. (2019). Applications of GIS in disease surveillance and response.
19. Johns Hopkins University. (2020). COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE).
20. Oracle. (2020). Data Warehousing in Healthcare.
21. IBM. (2019). Predictive Analytics in Public Health.
22. Ministry of Health, Uganda. (2018). Integrated Disease Surveillance and Response (IDSR) Implementation Guidelines.
23. Kjellén, S. Z., & Olsson, S. (2009). Rapid Alerts for Crises at the EU Level. In S. Olsson (Ed.), Crisis Management in the European Union: Cooperation in the Face of Emergencies (pp. 61–82). Springer. https://doi.org/10.1007/978-3-642-00697-5_4
24. Novossiolova, T., Bakanidze, L., & Perkins, D. (2020). Effective and Comprehensive Governance of Biological Risks: A Network of Networks Approach for Sustainable Capacity Building. In B. D. Trump, C. L. Cummings, J. Kuzma, & I. Linkov (Eds.), Synthetic Biology 2020: Frontiers in Risk Analysis and Governance (pp. 313–349). Springer International Publishing. https://doi.org/10.1007/978-3-030-27264-7_14
25. Talukdar, S., Naikoo, M. W., Mallick, J., Praveen, B., Shah Fahad, Sharma, P., Islam, A. R. Md. T., Pal, S., & Rahman, A. (2022). Coupling geographic information system integrated fuzzy logic-analytical hierarchy process with global and machine learning based sensitivity analysis for agricultural