Unlocking data to drive action: Strengthening global arbovirus research

Participants identified significant fragmentation in existing surveillance systems and called for new technologies that enable real-time, interoperable data sharing across borders and disciplines. Key recommendations include further development of regional data hubs, embedding ethical and equitable data-sharing frameworks, and leveraging existing networks for multi-pathogen research.

Delegates emphasised that while technical tools exist, the greater challenge lies in building trust, reciprocity, and local capacity. As noted by one of the delegates, “Data sharing is relational, not just technical.”

These insights will guide future funding priorities and cross-sector collaborations to build stronger, community-centred data ecosystems for global health.

1. Why was this study and workshop needed now?

   Arbovirus data remains fragmented and unevenly distributed across regions. Surveillance systems and research infrastructure have not kept pace with the awareness of global risk. This report and workshop serve as a timely reminder to take stock of existing tools, identify data and governance gaps, and align efforts between researchers, funders, and public health agencies to strengthen preparedness and response to the significant threat posed by arboviruses. From a technical perspective, it was clear that while data volume is growing, the systems to manage, integrate, and reuse it are still lagging, particularly in the global south.

   Arboviruses represent a growing infectious disease threat, especially as climate change expands the range and intensity of mosquito-borne transmission. Approximately 40% of the global population is now at risk of infection from viruses transmitted by Aedes aegypti mosquitoes, with the greatest burden falling on low and middle-income countries (LMICs). This underscores the urgent need for collaboration and a multistakeholder approach to better understand how arbovirus-related data are accessed, shared, and stored, and to improve the availability of tools that strengthen preparedness. The study highlighted persistent gaps and challenges in data ownership, sharing, usage, and storage across sectors from academia to government. The workshop provided an important opportunity for stakeholders across these sectors and geographies to come together, discuss the challenges they face, and develop recommendations to better shape and strengthen the global arbovirus disease landscape.

2. What were the biggest data and infrastructure gaps identified?

   A number of gaps were identified. The largest across the current landscape of available data and infrastructure were around interoperability, completeness, and equity of data access. Much of the existing arbovirus data is siloed, lacks standardised metadata, and is difficult to integrate across platforms. Africa, despite high disease burden, remains relatively underrepresented in accessible data. Feedback from both the report and expert stakeholders at the workshop highlight that technical barriers, such as lack of shared data platforms and guidance, limited analytical infrastructure, and short-term platform funding and availability, compound these gaps.

   Additionally, a major gap identified across regions, is the limited availability of accessible, government-owned data platforms that provide real-time arbovirus information. In many countries, national systems for data collection, storage, and sharing are either absent or not publicly accessible, creating significant barriers to timely surveillance and coordinated response.

   While some of these limitations are partially offset by strong intraregional collaborative surveillance networks such as those observed in Latin America, the Caribbean, and parts of Asia, few African countries maintain well-represented arbovirus information on their health data platforms, and regional collaborative health networks remain weak despite the widespread endemicity and recurring outbreaks of arboviral diseases across the continent.

   This lack of digital infrastructure and interoperable tools severely hampers data-driven decision-making, cross-border coordination, and the ability to model and predict arbovirus transmission dynamics effectively.

3. Why is trust and equity just as important as technology in data sharing?

   Trust determines whether people are willing to share data at all and the importance of trust was highlighted frequently throughout interviews with experts' stakeholders and at the convening workshop. Data sharing depends on building and managing relationships, between researchers, institutions, decision makers, and the communities who contribute data. The skills needed to do this are different from the more technical and structured approaches that dominate discussions on data systems, and as a result, this relational work is often undervalued, both in terms of responsibility and resourcing. Without clear agreements on ownership, credit, and local benefit, even the most advanced platforms will not be used as intended. Equity ensures that those contributing data also gain from it, whether through access, visibility, or decision-making power. Data systems only work when they are built on respect and accountability. Technology can make sharing possible, but trust makes it sustainable.

   While advanced technology provides the infrastructure for data sharing, trust and equity are what make these systems function effectively. Advanced data-sharing platforms will be ineffective if data is not regularly updated or contributed. Additionally, when the individuals and communities who collect or provide data perceive that they are respected partners, and that data sharing leads to shared sustainable benefits, participation and data quality both improve. Underfunded or unfunded local researchers often feel compelled to maintain full ownership of their data because they fear exploitation or lack of recognition. Without building equitable partnerships, funding for continued research and trust, they are understandably reluctant to share, regardless of how sophisticated the platform is. Therefore, a sustainable data ecosystem depends not only on technological capability but also on creating systems of trust, fair recognition, and equitable benefit-sharing. This balance ensures optimal use of data platforms and strengthens disease surveillance and public health outcomes overall.

4. How can we better connect researchers, policymakers, and communities?

   Shared standards, shared language, and a shared sense of purpose are required to better connect researchers, policymakers and communities. Data must be robust and useful as well as easy to interpret and relevant to local users and decision-making. Researchers and policymakers should collaborate from the beginning of a study to ensure that data collection and management process are representative and ultimately useful. Regional data hubs, regular exchanges between research and government teams, and accessible tools can help bridge that divide. Local decision makers and communities need to be part of the design process so that their perspectives shape how data is collected and used.

   To optimize interconnectedness between researchers, policy makers, and the communities, there must be a continuing trans-relational effort. National, regional, and global funding frameworks must embed equitable research partnerships and participatory research practices. This ensures that all stakeholders; researchers, policymakers, and communities are actively involved in co-designing studies, generating data, and interpreting results. Funding structures should prioritize mechanisms that translate research outcomes back to the communities where data is collected, ensuring tangible benefits and reinforcing trust. By aligning equity, participation, and practical impact within funding and governance systems, research becomes more sustainable, inclusive, and actionable for public health and policy improvement.

5. What role can organisations like Arctech Innovation play in building sustainable, ethical data ecosystems?

   Arctech Innovation has had long standing a vision of supporting data sharing and community engagements. One of our initiatives, the Global Vector Hub, can be used to educate, share information and support programmes to reduce vector borne diseases in the global health community. With sustainable funding, the GVH can be further utilised to close the gaps in global exchange of data, information and access to networks, learning platforms and tools in the global public health vector control domain.

6. What are the top three actions funders and governments can take to make real progress?

   The report highlights a wealth of opportunities to improve data systems in the arbovirus space. The following could be implemented to enhance the efficiency and impact of these:

   1. Invest in developing improved data infrastructure and skills, including leveraging existing systems and framework across the health space

   2. Create data and research standards that are practical for both high level and local implementation through allowing local level input and embedding ethical standards at the start

   3. Build a community of engaged and informed data sharing and management, by embedding data sharing into project plans and grants. The use cases of data beyond primary research needs to be recognised and rewarded.