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_2
Snippet: Our goal in this report is to provide a framework for evaluating the predictive validity of model forecasts for COVID19 outcomes as data become sequentially available, using the IHME prediction of daily deaths as an example. Given our goal is to provide an evaluation framework, we treat the IHME model as a "black box" and examine the projected numbers of deaths per day in light of the ground truth to help begin to understand the predictive accura.....
Document: Our goal in this report is to provide a framework for evaluating the predictive validity of model forecasts for COVID19 outcomes as data become sequentially available, using the IHME prediction of daily deaths as an example. Given our goal is to provide an evaluation framework, we treat the IHME model as a "black box" and examine the projected numbers of deaths per day in light of the ground truth to help begin to understand the predictive accuracy of the model. We do not provide a critique of the assumptions made by the IHME model, nor do we suggest any possible modifications to the IHME approach. Moreover, our analysis should not be misconstrued as an investigation of mitigation measures such as social distancing. As of April 5 2020, IHME released a new version of their model and we will examine the associated predictions in the coming days as more data become available.
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