Deep uncertainty in pandemic decision-making A different approach to policymaking

During the COVID-19 pandemic, policymakers' faced the challenge of Decision Making under Deep Uncertainty (DMDU). Pandemic decisionmaking is confronted with deep uncertainty. DMDU approaches acknowledges that uncertainty cannot be reduced through predictions or a few plausible scenarios but emphasizes preparedness, monitoring, and adaptability. In this paper we specify an adaptive approach to manage the deep uncertainty of pandemics.

Predictive models have proven to be unreliable in making decisions during the COVID-19 pandemic, leading to poor outcomes in various regions and countries. Deep uncertainty arises when experts lack knowledge about the external context, system behavior, and outcomes of interest. The article ‘Decision making under deep uncertainty for pandemic policy planning’, published in Health Policy in July 2023, highlights the need for a shift from traditional ‘predict and act’ policymaking approach to a ‘prepare, monitor, and adapt’ approach to help reduce policy failure and specify actions to be taken in anticipation. DMDU approaches have been successfully applied in other domains, such as climate change policymaking and water management. Consequently, we aim to broaden the scope of application by exploring the potential of these approaches for other societal domains confronted with deep uncertainty such as pandemic policymaking.

The article details the use of the Dynamic Adaptive Planning (DAP) approach to develop an adaptive plan to handle future pandemics. Such a plan includes the implementation of a basic policy (e.g. social distancing), identifying possible (uncertain) vulnerabilities and opportunities of this policy (e.g. low or high acceptance and adherence), a monitoring program to monitor these uncertainties as well as a preparing anticipatory actions that can be implemented if needed. For instance if in the future an infection reproduction value > 0.4 is monitored, a  lockdown might be implemented.

The article was written in collaboration with the Radboud UMC Geriatrics department and Radboud University Methods Department bringing a new approach to the conversation of pandemic management.

As part of the PanAdapt project (Adaptive Pandemic Management), including experts in complexity science, management sciences, medicine and (de)central policy makers, the researchers aim to develop a range of tools and approaches to tackle pandemic policymaking in an adaptive manner.

Read the publication here

Hadjisotiriou, S., Marchau, V., Walker, W., & Rikkert, M. O. (2023). Decision making under deep uncertainty for pandemic policy planning. Health policy (Amsterdam, Netherlands), 133, 104831. https://doi.org/10.1016/j.healthpol.2023.104831