| Abstract: |
| The results of an alternative methodology for making predictions about the COVID-19 pandemic are presented. The methodology is based on the Computational Singular Perturbation method, which delivers algorithmically singular perturbation analysis results for complex multiscale systems. Instead of focusing on the various population profiles (subjected to instabilities introduced by the fitting process), this methodology focuses on the time scale that characterizes the intensity and duration of the outbreak phase. The analysis leads to the conclusion that the prediction of the inflection point in the profile of the active cases is much more robust and accurate than the prediction of the. Since the inflection point precedes the peak, this methodology can serve as an early warning of the peak. In addition, CSP diagnostics are shown to provide significant information regarding the paths between the various populations (healthy, exposed, infected, etc) that contribute the most to the outbreak phase. The accuracy of the new methodology in analyzing pandemic outbreaks is demonstrated for the 4th, 5th and 6th outbreak waves of COVID-19 in Greece. |
|