Outpacing Pandemics: Solving the First and Last Mile Challenges of Data-Driven Policy Making

By Stefaan Verhulst, Daniela Paolotti, Ciro Cattuto and Alessandro Vespignani

Data & Policy Blog
Data & Policy Blog
9 min readFeb 2, 2024


How to address the first and last mile challenges of data driven policy making is the focus of a forthcoming workshop focusing on mobility data for pandemics — within the context of the ESCAPE EU project. In the below, we describe the rationale for seeking to address first and last mile challenges related to data for pandemics.

As society continues to emerge from the legacy of COVID-19, a dangerous complacency seems to be setting in. Amidst recurrent surges of cases, each serving as a reminder of the virus’s persistence, there is a noticeable decline in collective urgency to prepare for future pandemics. This situation represents not just a lapse in memory but a significant shortfall in our approach to pandemic preparedness. It dramatically underscores the urgent need to develop novel and sustainable approaches and responses and to reinvent how we approach public health emergencies.

Image: UN OCHA/Gema Cortes

Among the many lessons learned from previous infectious disease outbreaks, the potential and utility of data, and particularly non-traditional forms of data, are surely among the most important lessons. Among other benefits, data has proven useful in providing intelligence and situational awareness in early stages of outbreaks, empowering citizens to protect their health and the health of vulnerable community members, advancing compliance with non-pharmaceutical interventions to mitigate societal impacts, tracking vaccination rates and the availability of treatment, and more. A variety of research now highlights the particular role played by open source data (and other non-traditional forms of data) in these initiatives.

Although multiple data sources are useful at various stages of outbreaks, we focus on two critical stages proven to be especially challenging: what we call the first mile and the last mile.

We argue that focusing on these two stages (or chokepoints) can help pandemic responses and rationalize resources. In particular, we highlight the role of Data Stewards at both stages and in overall pandemic response effectiveness.

The First Mile: Data Readiness and Accessibility

The concept of the “first mile”, in the context of pandemic response, emphasizes the importance of having rapid access to traditional and non-traditional data for re-use. In this crucial initial phase, the ability to rapidly access, share, and analyze existing and new data sets are key. The rapid mobilization of data plays a pivotal role in the early detection of potential pandemics, accelerating our understanding of the pathogen’s characteristics and spread. This immediate data availability is crucial also for generating essential situational awareness during the initial, often chaotic stages of a pandemic. In these early phases, characterized by a ‘fog of war’ — lack of knowledge and the confusion that arises when combating a new pathogen — reliable data fed into appropriate models can cut through this uncertainty.

By ensuring data is readily available for re-use, authorities and health organizations can make informed decisions faster, leading to more effective containment strategies and potentially mitigating the impact of the pandemic. And that is especially important, yet challenging, as it relates to non-traditional data. The COVID-19 pandemic, for instance, demonstrated the power of non-traditional data sources, such as those from telecom operators and location intelligence companies (see: Repository of and Report on Mobility Data Collaboratives). The invaluable contribution to tracking and modeling the spread of the virus should not be overlooked. Yet, as society moves on, these lessons are being neglected.

A reflection published in Health Affairs (2023) reinforces this point, highlighting the need for immediate and accessible data at the onset of a health crisis. We must learn from our recent past and establish systems that ensure data is not only available but also ready for use when the next pandemic strikes.

Equally crucial is the readiness to scale up and extend already existing collections of traditional and non-traditional data sources. Recent studies have highlighted the importance of establishing sustainable and readily scalable data collections for information that can be used during the various phases of a pandemic.

Finally, the importance of implementing sustainable data collections should also be stressed in non-emergency times, in between pandemics. For example, monitoring the co-circulation of several respiratory viruses (namely flu, RSV, and COVID-19) could have benefited from the enhanced data collection created for the first phases of the COVID-19 pandemic and then recently discontinued.

The Last Mile: Implementing Data-Driven Decisions

The “last mile” in pandemic responsiveness involves the process of translating data intelligence into actionable decision intelligence and ensuring its practical use by policymakers and public health officials. Data, no matter how accurately collected and analyzed, is only as good as its application in real-world decision making. How do we avoid the “a book on the shelf” syndrome that too often affects not only research but also data as well?

In the last mile, the focus shifts from the acquisition and analysis of data to its effective utilization in guiding responses to the pandemic, such as deploying resources, implementing public health measures, and communicating risks to the public. It represents the bridge between data analysis and the tangible impact of the insight on pandemic management and mitigation efforts, highlighting the importance of collaboration between data analysts, modelers, and decision-makers to ensure that the data is not only accurate and comprehensive but also effectively used to guide public health responses.

The transition from data to decision intelligence during a pandemic is a multifaceted and complex process. This transition involves not just the analysis of data but also using the data to inform adequate, realistic models that can provide timely insight on the evolution of crises and whose results can be disseminated through effective communication and application in making critical decisions that impact public health and safety. However, the COVID-19 pandemic highlighted gaps and challenges in this process. Despite having access to significant non-traditional data, there was often a disconnect in efficiently translating and using this information into ethically and scientifically robust models, actionable policies, and timely responses. A review of the use of digital tools in public health responses, specifically mobile phone applications for contact tracing, during the COVID-19 pandemic published in the European Journal of Risk Regulation (2020) underscored the difficulties in leveraging vast amounts of non-traditional data for scientific and policymaking purposes. The review also emphasized the importance of establishing robust frameworks that ensure the effective, ethical, and transparent use of digital tools in public health and beyond.

This disconnect underlines the need for innovative approaches in decision-making.

One such approach is the concept of Decision Accelerator Labs. These labs represent a novel framework for decision-making, combining data analysis, expert knowledge, and diverse stakeholder perspectives to expedite and enhance decision-making processes. Their goal is to bridge the divide between data collection and practical application, creating a collaborative, dynamic setting for enhanced decision intelligence. In these labs, experimentation with new methodologies and technologies is encouraged to improve the responsiveness and effectiveness of decisions in real-time. Furthermore, the importance of coordinating modeling efforts in designated ‘hubs’ is integral to this approach. Such hubs synthesize uncertainties across various modeling approaches, integrating different data sources and enabling comparisons between them. This consensus-building enhances the reliability of modeling predictions and guides more effective policy-making and response strategies. By incorporating these hubs into Decision Accelerator Labs, we can further enhance our capacity to navigate the response to epidemic emergencies with more accurate, consensus-driven data analysis.

Decision Accelerator Labs could mitigate some of the deficiencies observed during the COVID-19 pandemic by enabling swifter, more informed decision-making processes. These labs can ensure that data-driven insights are quickly and efficiently transformed into actionable public health measures.

Furthermore, implementing the following proactive actions during non-crisis periods may also contribute to overcoming last mile challenges:

  • Collaborating with national, interregional, and global agencies to highlight the significance of data-driven modeling and decision-making,
  • Involving the general public to emphasize the value of crowdsourced data collection and to educate on the methodologies behind data-driven decision-making,
  • Engaging media representatives to improve the flow of information between policymakers and the public, fostering better understanding and transparency.

The Interdependence between the First and Last Mile

The design of the “first mile” in pandemic response — ensuring access to data for re-use–has a significant impact on the “last mile,” as it will determine what data and subsequent insight is available that can inform decision-making and action based on that data. Conversely, the needs and challenges encountered in the last mile must inform and shape the data access priorities of the first mile. This interdependence highlights the importance of a cohesive and well-integrated approach to pandemic data stewardship. By ensuring that the initial data collection is tailored to meet the practical requirements of decision-makers, and by adapting data access strategies based on feedback from the last mile, a more effective and responsive pandemic management system can be established. This synergistic relationship between the first and last miles underscores the need for continuous collaboration and alignment between data providers, analysts, modelers, and decision-makers throughout the entire process of pandemic response.

Institutionalizing Data Stewardship: A Path Forward

Data Stewards play a pivotal role in facilitating these relationships and addressing the challenges of re-using data responsibly and effectively, especially in the context of pandemic preparedness and response. Data stewardship comprises a set of skills and competencies to advance access to data for re-use in a systematic, sustainable, and responsible way. This involves not only stewarding data but also stewarding relationships, internal and external resources, and insights. These include technical skills in data management and analysis, as well as competencies in ethical considerations, communication, and collaboration.

In the context of a pandemic, Data Stewards are instrumental in making sure that data is not only available but also relevant, accurate, and timely, thereby enabling it to be effectively used for decision-making. They help bridge the gap between data collection (the first mile) and its application in decision intelligence (the last mile). By establishing and applying standards for data quality and sharing, Data Stewards provide a critical human infrastructure that can accelerate access to the data needed for rapid and informed responses to public health crises.

Building a robust network of Data Stewards for pandemic response and institutionalizing their role within public and private organizations can significantly enhance pandemic preparedness. With trained and dedicated stewards, organizations can be better equipped to manage their data assets in a way that during a pandemic. Data Stewardship, as re-imagined in the context of pandemic response, involves developing specific capacities and competencies that align with the unique challenges of managing health-related and epidemiological data.

A Call to Remember and Act

As society shifts its focus away from the pandemic, it is imperative that we do not let our collective guard down. The lessons of COVID-19 should not be relegated to the annals of history but should actively inform our approach to future public health crises. By addressing the first and last mile challenges in data-driven pandemic response and institutionalizing the role of data stewards, we can transform our approach from reactive to proactive. We do not need to wait for another pandemic to remind us of what we should already know and do.

The workshop on mobility data for pandemics will take place in Brussels on March 22nd 2024. If you are a private data holder, public health official, or modeler and want to be involved, visit https://datatank.link/ESCAPEworkshop.

Citable Version

A citation version of this blog is available on the Data for Policy Zenodo community site:

Verhulst, S., Cattuto, C., Paolotti, D., & Vespignani, A. (2024). Outpacing Pandemics Solving the First and Last Mile Challenges of Data-Driven Policy Making. Zenodo. https://doi.org/10.5281/zenodo.10610765

About the Authors

Stefaan Verhulst is the Co-Founder of The Data Tank (Brussels, Belgium) and The GovLab (New York, USA), Editor-in-Chief of Data & Policy, 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


This is the blog for Data & Policy (cambridge.org/dap), a peer-reviewed open access journal published by Cambridge University Press in association with the Data for Policy Community. Interest Company. Read on for ways to contribute to Data & Policy.



Data & Policy Blog
Data & Policy Blog

Blog for Data & Policy, an open access journal at CUP (cambridge.org/dap). Eds: Zeynep Engin (Turing), Jon Crowcroft (Cambridge) and Stefaan Verhulst (GovLab)