Are We Ready for the Next Pandemic? Navigating the First and Last Mile Challenges in Data Utilization
By Stefaan Verhulst, Daniela Paolotti, Ciro Cattuto and Alessandro Vespignani
Public health officials from around the world are gathering this week in Geneva for a weeklong meeting of the 77th World Health Assembly. A key question they are examining is: Are we ready for the next pandemic? As we have written elsewhere, regarding access to and re-use of data, particularly non-traditional data, for pandemic preparedness and response: we are not. Below, we list ten recommendations to advance access to and reuse of non-traditional data for pandemics, drawing on input from a high-level workshop, held in Brussels, within the context of the ESCAPE program.
As the world continues to grapple with the aftermath of COVID-19, and the threat of a Highly Pathogenic Avian Influenza, a question looms large: Are we ready for the next pandemic? During and after COVID-19, the potential of data to help with pandemic preparedness and response was clear. In particular, non-traditional data sources–including data from mobility analytics, social media, and search trends–provided critical insights into population dynamics and health trends.
At the same time, the limitations and risks involved in accessing and re-using data were also evident. In particular, challenges were apparent at the “first mile” (access to data, e.g. for modeling and decision support) and “last mile” (translating data insights into action) of the data life cyle. A recent high-level workshop, held in Brussels, within the context of the ESCAPE program, brought together experts to address some of these challenges, and, more generally, to consider the role of data in responding to pandemics. Below, we share some of the key takeaways and recommendations that emerged from this workshop.
Yet, prior to detailing the areas needed to improve first and last mile capabilities, it is crucial to highlight a persistent challenge that underpins all others: underinvestment. Sustained financial investment should be a top priority to bolster data-driven public health responses. Participants at the workshop emphasized the importance of funding not only for responsiveness to ongoing pandemics, but also in preparedness for potential future pandemics. Among other recommendations, they suggested establishing a dedicated Data Fund to support the key issues listed below — including infrastructure, technologies, and human resources to promote robust and responsible data collaboration. Public-private partnerships are also important avenues to explore, and can help ensure the long-term viability of data sharing initiatives.
First Mile Challenges: Accessing and Using Non-Traditional Data
The “first mile” refers to the initial stages of data collection and transmission, typically involving front-line workers and patients, or sensors. The first mile of data is critical in battling pandemics as it involves the (often real-time) collection and reporting of health metrics, symptoms, and incident cases from healthcare facilities and communities. Participants at the workshop identified a number of key issues–both challenges and areas for possible improvement–with respect to the first mile.
Systematic Evidence Collection: During COVID-19, the use of data was often ad-hoc, driven more by existing, often inadequate for a pandemic, methodologies than by a standardized framework tailored for the timely gathering of the relevant information. Workshop participants agreed that there is a pressing need to develop a systematic approach to better understand how non-traditional data can be effectively harnessed in pandemic situations across countries and expertises. A systematic mapping project was proposed to critically evaluate how non-traditional data was utilized during the pandemic, its public health impact, and how to build upon these learnings.
Clarity on Data Needs: Participants also stressed the importance of identifying clear data needs for various stages of a pandemic. A better understanding of needs can help identify data sources, as well as facilitate ethical data sharing. The workshop highlighted the role of tabletop exercises (discussion- or role-based activities to simulate scenarios) to better understand data requirements at each stage of an unfolding pandemic.
Mapping the Data Supply: The pandemic highlighted the need not only to better understand demand for non-traditional data, but also the supply of such data (as well as various data sources). As such, participants suggested the need for a clear methodology to map the supply of non-traditional data. Comprehensive data audits and improved metadata structuring were some of the avenues suggested in pursuit of these goals.
Data Solidarity: The concept of “data solidarity” emerged as a key principle during the pandemic and its aftermath. Data solidarity refers to the collective commitment of data holders to share data systematically, responsibly, and ethically, especially during public health emergencies. The workshop agreed on the need to strengthen commitments and incentives for data holders to share data responsibly, especially across sectors. In addition, participants noted that establishing mutual commitment frameworks and promoting data solidarity can foster a more general collaborative environment, which can help address a wide range of public issues.
Standardization: The pandemic highlighted the importance of standardization across various dimensions of data handling and analysis. Standardization ensures consistency, reliability, and interoperability of data, all essential for effective data integration, analysis, and responsible sharing. The workshop emphasized the importance of establishing data standardization protocols, quality metrics, and reproducibility standards to enhance the effectiveness of data-driven public health interventions. Importantly, such steps should be taken both for data itself as well as associated metadata. It is also important that standards and best practices are driven also by the analytics and modeling needs that generate knowledge and intelligence from data.
Advanced Technologies: Not all challenges encountered at the first mile were limited to data. Some of them involved the underlying technologies for data collection and sharing, and in particular difficulties with the integration of advanced technologies such as privacy-enhancing tools, edge computing, data sandboxes, digital twin technologies, and mechanisms to create and use synthetic data. These innovations are increasingly essential for responsible reuse, offering the potential to safeguard privacy and enhance data security while improving data processing and analysis. Among other recommendations, participants emphasized the need to identify and integrate privacy-enhancing technologies (PETs), encourage edge computing and decentralization for real-time data processing, and make greater use of data sandboxes to foster secure collaboration and sharing. More generally, it is important to maintain an innovative culture, always responsive to the opportunities — and risks — of data collaboration.
Last Mile Challenges: Translating Data into Action
The “last mile” refers to the final stages of the data lifecycle, where information is translated into actionable insights. In the context of public health, this phase involves the dissemination of guidelines, treatment protocols, preventive measures, and real-time updates to relevant stakeholders. Overcoming challenges and bottlenecks at the last mile is essential to formulating policy and response strategies, and, more generally, to reaping the benefits of data. Participants at the workshop identified a number of priorities for better data use and reuse at the last mile.
Fusion Centers and Decision Accelerator Labs: The workshop highlighted the important role that can be played by various innovative approaches and institutions, such as Fusion Centers and Decision Accelerator Labs. These centers can serve as hubs for real-time data aggregation and analysis, providing comprehensive insights for decision-making. They play a key role in the rapid translation of scientific research into actionable policies.
Scientific Liaisons and Expert Networks: In addition, scientific liaisons and expert networks can play similar roles, helping to bridge the gap between science and public health practice, and integrating data insights into wider scientific and policymaking contexts. Also, scientific liaisons and expert networks (as well as the innovations mentioned above) can help ensure that the latest research findings are effectively communicated, a key step in any public health strategy.
Data Literacy: In the wake of the pandemic, clear gaps were evident in the capacity of both analysts and decision-makers to operationalize and effectively reuse non-traditional data. Addressing this gap requires a concerted effort to enhance data literacy among all stakeholders, especially decision-makers. Participants emphasized the need for a multifaceted approach to ensure that stakeholders are equipped with relevant skills and knowledge. The potential of educational modules and training programs for data literacy were highlighted.
Institutionalize Data Stewards: Data Stewards play a key role in facilitating data reuse and collaboration, ensuring these necessary processes are systematic, sustainable, and responsible. Among other functions, data stewards engage with partners, conduct data audits, and communicate findings, fostering a structured approach to managing data ecosystems. Institutionalizing the role of data stewards is therefore essential. Participants noted the role that can be played by formal job descriptions, recruitment strategies, comprehensive training curriculums, as well as various other steps and mechanisms.
Conclusion
The Covid-19 pandemic was a global catastrophic event, especially for already marginalized communities and individuals. It also offered a real-time, massive experiment to test the potential–and challenges–of using data to combat current and future public health emergencies (such as the antimicrobial resistance challenge). It is incumbent upon policymakers and others to glean a measure of meaning from the tragedy by building upon the lessons of that experiment.
The workshop represented an effort in such a direction. It highlighted the pressing need for a comprehensive and integrated approach to leveraging non-traditional data for pandemic preparedness and response, and it emphasized the particular importance of overcoming bottlenecks at the first and last mile of the data journey. The recommendations offered here are just a start, but if funded and implemented, they can help build a more resilient and adaptive public health system. Perhaps, too, they will help us respond more effectively during future public health emergencies–in the process, allowing us to avoid the tragic scale of this most recent pandemic.
About the Authors
Stefaan Verhulst is the Co-Founder of The Data Tank (Brussels, Belgium) and The GovLab (New York, USA), Research Professor at New York University and Researcher at the ISI Foundation, Italy;
Daniela Paolotti is Senior Research Scientist at the ISI Foundation, Italy
Ciro Cattuto is Scientific Director, of the ISI Foundation, Italy
Alessandro Vespignani is Sternberg Family Professor, Northeastern University, USA and President, ISI Foundation, Italy
