Forecasting COVID-19 pandemic: Unknown unknowns and predictive monitoring

During the current COVID-19 pandemic, there have been many efforts to forecast infection cases, deaths, and courses of development, using a variety of mechanistic, statistical, or time-series models. Some forecasts have influenced policies in some countries. However, forecasting future developments in the pandemic is fundamentally challenged by the innate uncertainty rooted in 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. Herein, grounded on our own COVID-19 forecasting experiences, I propose and advocate the "predictive monitoring" paradigm, which synthesizes prediction and monitoring, to make government policies, organization planning, and individual mentality heuristically future-informed despite the extreme uncertainty.