Pandemic Metric with Confidence (PMC) Model to Predict Trustworthy Probability of Utilized COVID-19 Pandemic Trajectory across the Global

Abstract

Lots of works aim to reveal the driving factors of COVID-19 pandemic trajectory yet ignore the confidence of utilized trajectory data, making consequent results suspicious. Hereby, we proposed a pandemic metric with confidence (PMC) model in the hypothesis of Bernoulli Distribution of nine trajectories reported from 113 countries. Results exhibit the average confidence of trajectories across the global not in excess of 12.1% with the error threshold configuration of 1E-5. In contrast, the 95% high confidence setting also failed to predict the trajectory containing the acceptable error not beyond 1E-3. Thus, a proposed trade-off strategy between two contradictory expections (>50% confidence, <1E-3 error) supports 61% of investigated countries to predict the varying trajectory with confidence beyond 50%. Moreover, PMC model recommend the remanent 39% countries to extend the proportion of populaces in COVID-19 detecting-pool to a suggested-value (>1% of populations), ensuing the average confidence up to 70%.