Predictive Monitoring of COVID-19
COVID-19 predictions are fundamentally important for rationalizing decisions, planning and mentality, but also challenging due to the innate uncertainty of the complex, dynamic and global COVID-19 pandemic as a typical wicked problem. There are many unknown unknowns, not just about the contagious virus itself but also about the intertwined human, social, and political factors, which co-evolve and keep the future of the pandemic open-ended. These unknown unknowns make the accuracy-oriented forecasting misleading. To address the extreme uncertainty of the pandemic, a heuristic approach and exploratory mindset is needed. On this basis, the predictive monitoring paradigm is proposed to detect the changes in the consecutive predictions of macro patterns and long-term variables and make sense of such changes as meaningful signals of uncertainty and changes in the real-world scenarios. In turn, such signals from the predicted theoretical future may inform, initiate, and guide actions now to influence the real future. By contrast, such prediction changes would be traditionally viewed as errors in traditional prediction exercises. The predictive monitoring paradigm synthesizes prediction and monitoring to make government policies, organization planning, and individual mentality heuristically future-informed despite the extreme uncertainty. Motivation, theory, tactics, cases, and caution are in this paper.
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