H) Translating modelling into public health gains
Ensuring that emerging infectious disease modelling is translated into public health gains.

There are two distinct mechanisms for modelling and, more broadly, for science to directly inform policy in a pandemic or similar emergency:

(i) close and direct collaboration, likely led by government or public health institutions and likely focused on building tools to answer specific research-to-policy questions, and

(ii) consensus building at a wider scale with a broader community of academics including modellers and others.

In the close collaboration model for knowledge translation, public health epidemiologists, scientists, and modellers work side-by-side with academic partners either in virtual or physical spaces (or a combination of the two) to ensure seamless flow of public health needs, data, innovations, and modelling solutions. This creates outputs of great direct use for both public health and academia and also provides impetus for innovations in modelling.

In the consensus building model for knowledge translation, modellers develop multiple models and viewpoints in response to emerging threats, current focal questions and requests from decision-makers. This is a key part of the scientific and analytic process. For their part, researchers need to recognize that policymakers need a platform for timely decision-making based on a synthesis of models that enjoys consensus support within the academic community. Such a synthesis is not the endpoint of inquiry: rather it is a consensus view at a point in time that is both scientifically valid and “serviceable” for the purposes of informing policymaking (Jasanoff 2015). [1]

This means that the process for achieving this consensus needs to be inclusive of a range of modelling centres and networks. In contrast to close and direct collaboration, this approach needs to be sufficiently independent of government that it has credibility within the scientific community and with the public. However, scientists should expect that ongoing conversations with policymakers will inform and enrich the boundary between science and policy. Policymakers, on their part, need to recognize that there will be considerable uncertainty, particularly on short time frames, and differences of opinion and dissent will occur as the science continues to develop. However, they should be able to expect a synthesis of advice that flows from models, relevant to a particular question. The network we envision will support these activities.

More broadly, models play a role in building and communicating scientific consensus and “serviceable truths”, because of modelling’s ability to synthesize diverse data, explore the consequences of assumptions, quantitatively test assumptions against data, and incorporate information as it emerges. In this context, the modelling and scientific communities can support decision-makers by coming together to create platforms for decision-making, rapidly assessing these through formal or informal peer review and robust debate, and by coming to agreed conclusions that are informed by this evidence and transparent process.

Workshops and training are essential ingredients for knowledge translation in EIDM. They operate in both directions: training public health epidemiologists to build knowledge of modelling and training modellers to build knowledge of epidemiology and public health. This needs to be ongoing and provide core training for HQP. Activities include development of simulated pandemic exercises to underpin greater pandemic preparedness and mutual understanding of modelling for public health in day-to-day and emergency outbreak situations. They also need to include development of skills in communication to the public for both academics and public health professionals.

There are some essential organizational ingredients that are predicated by the above framework for knowledge translation to ensure long-term success in translating EIDM into public health gains.

  • Public health and researchers must collaborate to develop a secretariat function that facilitates “evergreen collaboration” with academia (e.g., regular communication activities) and to ensure decision-makers obtain clear and consensus messages from the modelling community.
  • Productive interactions with the EIDM community require that there are people embedded in public health and government who understand and appreciate the strengths and limitations of modelling.
  • Federal public health institutions need to provide the opportunity for links with the global community in public health and international benchmarking of methods.
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Link[1] Serviceable Truths: Science for Action in Law and Policy

Zitieren: Sheila Jasanoff
Publication date: 1 June 2015
Publication info: Texas Law Review, 93, 1723
Zitiert von: David Price 12:23 PM 6 November 2023 GMT
Citerank: (1) 714608Charting a FutureCharting a Future for Emerging Infectious Disease Modelling in Canada – April 2023 [1] 2794CAE1
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[Texas Law Review, 93, 1723]

As the articles in this symposium issue attest, the relationship between law and science has begun to attract attention as an autonomous field of study, generating its own bodies of expertise and specialized scholarship. It is less obvious how the perspectives arising from within the community of legal practitioners and thinkers relate to a largely separate, but parallel, body of research and understanding from Science and Technology Studies (STS), a cross-disciplinary field that has for several decades been producing its own analyses of the relations between science, technology, and other authoritative institutions in society—including, of course, the law. Perhaps predictably, intersections between STS and legal studies have occurred most frequently around questions of evidence, since both fields share an interest in the nature and credibility of facts. Another area of topical convergence is intellectual property law, where authors may have dual training in law and STS. The shared interests of the two fields, however, bear on more fundamental questions of legal and political theory: questions about the nature of legitimacy and lawfulness in the modern world, where the actions of those in power must be held accountable to epistemic as well as normative standards—in short, to facts as well as to values. How to orchestrate that deeper engagement between STS and legal scholarship is one aim of this Article.

The road there can be charted in different ways. This symposium offers a pragmatic map. One can begin with cross-cutting topics at the intersections of science and law, especially criminal justice, bioethics, and the environment. In each of these areas, one approach is to pose questions aimed at improving the quality of scientific inputs to the legal process. Specifically, what evaluative standards should apply in conflicts over substance? Who should decide when experts disagree? And how should the results of knowledge processes be implemented? Under each of these headings, legal processes could benefit from a fuller grasp of relevant insights from STS, just as STS scholarship would gain depth and relevance by addressing more directly the kinds of issues and questions that seem most challenging from the standpoint of the law. In that sense, the pragmatic map may be as useful a starting point for future STS research as for legal studies.

This Article, however, departs from the topic–theme structure of the symposium to offer a more conceptual, indeed critical, perspective on law– science interactions. Here the concerns are not so much with making good decisions and hence with developing practical guidance on how the law should use or rely on scientific evidence and expert advice. Rather, the aim is to put society’s needs in the driver’s seat and explore how the two institutions could operate more effectively as partners in the central projects of governance in modern democracies: how to exercise power with reason, how to make good decisions in the face of epistemic as well as normative uncertainty, and how to strike an accountable balance between the sometimes conflicting pressures of knowledge and norms. In what follows, I sketch how STS understandings might help advance this kind of socially responsible collaboration between law and science…
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