A learning approach to community response during the COVID-19 pandemic: Applying the Cynefin framework to guide decision-making

Introduction: The United States has been unsuccessful in containing the rapid spread of COVID-19. The complex epidemiology of the disease and the fragmented response to it has resulted in thousands of ways in which spread has occurred, creating a situation where each community needs to create its own local, context-specific learning model while remaining compliant to county or state mandates. Methods: In this paper, we demonstrate how cross sector collaborations can use the Cynefin Framework, a tool for decision-making in complex systems, to guide community response to the COVID-19 pandemic. Results: We explore circumstances under which communities can inhabit each of the four domains of systems complexity represented in the Cynefin framework: simple, complicated, chaotic, and complex, and describe the decision-making process in each domain that balances health, economic, and social well-being. Conclusion: This paper serves as a call to action for the creation of community learning systems to improve community resilience and capacity to make better-informed decisions to address complex public health problems during the pandemic and beyond.