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_18
Snippet: We note that there are two ways in which the accuracy of the model, as measured by the percentage of states with death counts which fall within the 95% PI, can improve. Either the estimated uncertainty increases and therefore the prediction intervals become much wider, or the point of the estimated expected value improves. The latter is preferable but much harder to achieve in practice. The former can potentially lead to prediction intervals that.....
Document: We note that there are two ways in which the accuracy of the model, as measured by the percentage of states with death counts which fall within the 95% PI, can improve. Either the estimated uncertainty increases and therefore the prediction intervals become much wider, or the point of the estimated expected value improves. The latter is preferable but much harder to achieve in practice. The former can potentially lead to prediction intervals that are too wide to be useful to drive the development of health, social, and economic policies.
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