As we emerge from the SARS-CoV-2 pandemic commissions will be convened to dissect the decisions that were made by organizations, and governments. Decision-makers have both the opportunity and responsibility to meet the critical challenges of public health emergencies. Past inquiries, including the one conducted following SARS-CoV-1 have drawn attention to the need for robust critical thinking and the use of the precautionary principle during times of crisis.
Behavioural economics has demonstrated that all decision-makers are influenced by their beliefs, biases, and other constraints when making choices. Yet, our primary response has been to call for improved transparency around decision-making. I would argue that transparency alone is not enough. We need to embrace more structured approaches that can be used to better engineer the decision-making environment when communicating modelling results to decision-makers and knowledge users. The role of infectious disease modelling in Canadian pandemic preparedness and response activities in 2003 (SARS-CoV-1), 2009 (pandemic influenza A, H1N1), and 2022 (SARS-CoV-2) represents a unique case study with which to consider how forecasts inform decision-making with a specific focus on successes and failures.
Results/Conclusions
As we head into the post-mortem pandemic period, reviews of our pandemic modelling and response across all sectors will clearly demonstrate that we must better support and structure decision-making environments that mitigate the impact of cognitive bias. We must consider the possibility that employing a more structured framework for decision environments would improve processes and policy decisions. Public health relies heavily on risk communication within organizations and between leaders and the public. The integration of the best available empirical evidence from epidemiology, public health, mathematics, and statistics combined with psychology and behavioural economics can support the creation of an institutional culture of transparency around decision-making that will better enable models to guide policy change.
