Pathogen surveillance: Making sense of a fragmented global system

Coronavirus superimposed over a computer image of the globe, photo by Image Flow/Adobe Stock

Photo by Image Flow/Adobe Stock

More evidence is needed to understand what gaps exist in the pathogen surveillance space and how these can be addressed.

What is the issue?

Pathogen surveillance for infectious diseases and antimicrobial resistance (AMR) is an important tool which can inform public health decision making and action. A large range of initiatives conduct pathogen surveillance internationally, varying in size, geographical coverage, pathogen coverage, the stakeholders involved, and the types of data collected and shared.

Although pathogen surveillance is critical to public health decision making, few studies look across surveillance initiatives to provide an overview of the space. Evidence is needed to understand what gaps exist and how they can be addressed.

How did we help?

In this context, the Novo Nordisk Foundation commissioned RAND Europe to conduct a study to:

  • Identify pathogen surveillance initiatives and the stakeholders involved;
  • Understand challenges that have been faced and how they have been overcome;
  • Assess the strengths and weaknesses of different approaches;
  • Describe how insights from pathogen surveillance initiatives have been used to inform public health decision making; and
  • Provide an overview of gaps in the pathogen surveillance space and how these might be addressed.

To do this, RAND Europe conducted a scoping review of pathogen surveillance initiatives. Ten priority initiatives were then selected as case studies, and additional desk research and interviews were conducted to support case study development. Lastly, experts in different aspects of pathogen surveillance were interviewed.

What did we find?

  • The pathogen surveillance space is highly fragmented, with poor coordination between efforts, varying approaches and methodologies, and a lack of clarity in how data flows.
  • Capacity constraints and logistical challenges, particularly in low-resource settings, limit the availability of data for surveillance. Healthcare and clinical data is also limited by the degree to which diagnostics and electronic health records are used.
  • The ability to conduct integrated and real-time surveillance is critical to public health decision making, but is currently lacking. Insufficient metadata, a lack of interoperability between data platforms, and varying case definitions, methodologies and data formats make it difficult to link and analyse data.
  • Wastewater surveillance provides a promising way to understand population-level health at a lower cost and avoids some of the challenges involved in individual-level data. However, data science is needed to understand how new and emerging data sources, such as wastewater surveillance and genomic data, should be incorporated into surveillance activities and what actions should be triggered by signals in different data types.

What can be done?

  • There is a need to build distributed and sustainable capacity, particularly in genomic surveillance, and to support the use of diagnostics and electronic health records across settings. Hub and spoke models, for example, have been used to build capacity, and can help improve harmonisation, whilst also allowing a degree of autonomy and local adaptation.
  • More harmonised approaches and methods are needed to improve integration between data streams and reduce siloed data. There is a need to bring together experts to agree on priorities and common approaches, improve interoperability and increase harmonisation between the different stakeholders and initiatives involved in pathogen surveillance.
  • There is potential to use artificial intelligence (AI) to help analyse integrated data and to use data science techniques to clarify which data streams are most informative for surveillance efforts. This may highlight potential for efficiencies and refine priorities around integrated surveillance.
  • Common approaches for pathogen surveillance would need to balance being simple enough to implement across settings, while also being sophisticated enough to capture granular and complex information. Step-wise approaches to building capacity may be helpful in achieving this balance.