Abstract
Mathematical models are increasingly relied upon as decision support tools, which estimate risks and generate recommendations to underpin public health policies. However, there are no formal agreements about what constitutes professional competencies or duties in mathematical modeling for public health. In this article, we propose a framework to evaluate whether mathematical models that assess human and animal disease risks and control strategies meet standards consistent with ethical “good practice” and are thus “fit for purpose” as evidence in support of policy. This framework is derived from principles of biomedical ethics: independence, transparency (autonomy), beneficence/non-maleficence, and justice. We identify ethical risks associated with model development and implementation and consider the extent to which scientists are accountable for the translation and communication of model results to policymakers so that the strengths and weaknesses of the scientific evidence base and any socioeconomic and ethical impacts of biased or uncertain predictions are clearly understood. We propose principles to operationalize a framework for ethically sound model development and risk communication between scientists and policymakers. These include the creation of science–policy partnerships to mutually define policy questions and communicate results; development of harmonized international standards for model development; and data stewardship and improvement of the traceability and transparency of models via a searchable archive of policy-relevant models. Finally, we suggest that bespoke ethical advisory groups, with relevant expertise and access to these resources, would be beneficial as a bridge between science and policy, advising modelers of potential ethical risks and providing overview of the translation of modeling advice into policy.
Highlights
Access to “big data” and new computing technologies has increased the utility of mathematical models and algorithms, including the potential to “fast-track” decision-making to mitigate risks
Independence conceptual design, coding, verification, peer-review processes is compromised if conflicts of interest arise?
To operationalize mathematical models so that they provide useful evidence for policy purposes, the following initiatives might be considered: 1. Creation of partnerships between scientists, funding bodies, policymakers, and public stakeholders to mutually define the range of questions legitimately answerable via modeling, transparently communicate model outputs, and support interpretation of results and post hoc evaluation of the effectiveness of model recommendations as implemented
Summary
Access to “big data” and new computing technologies has increased the utility of mathematical models and algorithms, including the potential to “fast-track” decision-making to mitigate risks. We propose four major criteria that offer a framework to determine the suitability of scientific outputs as evidence for policy and examine the degree to which mathematical models meet these standards These criteria are derived from the four fundamental principles of biomedical ethics [5]: autonomy (i.e., the right to make informed decisions, which in turn requires sufficient independent and transparent information), beneficence (i.e., being of benefit to the end user), non-maleficence (i.e., doing no harm), and justice (i.e., fair distribution of benefits, risks, and costs). Transparency refers to the clear documentation of the scientific approach so that methods are robust, repeatable, and reproducible, and outcomes are clearly communicated and understood This documentation includes information about conflicts of interest, constraints, or biases affecting data collection and interpretation (e.g., intersectional analysis of the effect of class, racial, ethnic, gender, or sexual categorizations) and any assumptions or uncertainties inherent in the modeling process.
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