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Decision making under deep uncertainty for pandemic policy planning
Sophie Hadjisotiriou1, Vincent Marchau2, Warren Walker3
1Radboud UMC, Faculty of Medical Sciences, Reinier Postlaan 4, 6525 GC Nijmegen, the Netherlands.
Health Policy (Amsterdam, Netherlands)
|May 8, 2023
Summary
Global policymakers were unprepared for COVID-19, leading to millions of deaths. A Decision Making under Deep Uncertainty (DMDU) approach, focusing on adaptation rather than prediction, is proposed for future pandemic policy.
Area of Science:
- Public Health
- Policy Analysis
- Epidemiology
Background:
- The COVID-19 pandemic exposed global policymaker unpreparedness, resulting in millions of cases and deaths.
- The scale of the pandemic's impact was theoretically avoidable, as evidenced by select countries' outcomes.
- Policymakers face Decision Making under Deep Uncertainty (DMDU) during large-scale crises.
Purpose of the Study:
- To explore the utility of a Decision Making under Deep Uncertainty (DMDU) framework for pandemic policy.
- To advocate for adaptive policy strategies in response to public health emergencies.
- To highlight the limitations of 'predict and act' approaches in uncertain environments.
Main Methods:
- Conceptual analysis of policy decision-making frameworks.
- Review of pandemic response strategies and outcomes.
- Discussion of the principles of DMDU in the context of public health crises.
Main Results:
- The 'predict and act' model proved insufficient for managing the COVID-19 pandemic.
- A DMDU approach emphasizes continuous monitoring and adaptation.
- Adaptive policies allow for adjustments as new information emerges during a crisis.
Conclusions:
- The DMDU approach offers a more robust strategy for pandemic preparedness and response.
- Implementing adaptive policies can mitigate the impact of future health disasters.
- Shifting from reactive to adaptive policymaking is crucial for global health security.
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