Author: Roman Marchant; Noelle I Samia; Ori Rosen; Martin A Tanner; Sally Cripps
Title: Learning as We Go: An Examination of the Statistical Accuracy of COVID19 Daily Death Count Predictions Document date: 2020_4_17
ID: ijac68gh_17
Snippet: Our results suggest that the IHME model substantially underestimates the uncertainty associated with COVID19 death count predictions. We would expect to see approximately 5% of the observed number of deaths to fall outside the 95% prediction intervals. In reality, we found that the observed percentage of death counts that lie outside the 95% PI to be in the range 49% -73%, which is more than an order of magnitude above the expected percentage. Mo.....
Document: Our results suggest that the IHME model substantially underestimates the uncertainty associated with COVID19 death count predictions. We would expect to see approximately 5% of the observed number of deaths to fall outside the 95% prediction intervals. In reality, we found that the observed percentage of death counts that lie outside the 95% PI to be in the range 49% -73%, which is more than an order of magnitude above the expected percentage. Moreover, we would expect to see 2.5% of the observed death counts fall above and below the PI. In practice, the observed percentages were asymmetric, with the direction of the bias fluctuating across days.
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