Selected article for: "model performance and real time"

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_20
    Snippet: We would like to thank the authors of IHME for making their predictions and data publicly available. We agree with the statement on their website http://www.healthdata.org/covid/updates Having more timely, high-quality data is vital for all modeling endeavors, but its importance is dramatically higher when trying to quantify in real time how a new disease can affect lives. Without access to the data and predictions this analysis would not have be.....
    Document: We would like to thank the authors of IHME for making their predictions and data publicly available. We agree with the statement on their website http://www.healthdata.org/covid/updates Having more timely, high-quality data is vital for all modeling endeavors, but its importance is dramatically higher when trying to quantify in real time how a new disease can affect lives. Without access to the data and predictions this analysis would not have been possible. We look forward to evaluating the performance of the newer versions of the IHME model.

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