Selected article for: "auto regression and date outbreak"
Author: Lee Worden; Rae Wannier; Nicole A. Hoff; Kamy Musene; Bernice Selo; Mathias Mossoko; Emile Okitolonda-Wemakoy; Jean Jacques Muyembe-Tamfum; George W. Rutherford; Thomas M. Lietman; Anne W. Rimoin; Travis C. Porco; J. Daniel Kelly
Title: Real-time projections of epidemic transmission and estimation of vaccination impact during an Ebola virus disease outbreak in Northeastern Democratic Republic of Congo
  • Document date: 2018_11_5
    • ID: 96arnumb_34
    Snippet: The performance of our short-term prediction models can be assessed on the 361 outbreak to date. The relatively simple auto-regression model we used performed more 362 consistently on the range of partial data sets used for scoring than the more complex 363 stochastic simulation model did. The stochastic model has tended to produce tighter 364 prediction distributions that are prone to extreme failure when they get it wrong, while 365 the auto-re.....
    Document: The performance of our short-term prediction models can be assessed on the 361 outbreak to date. The relatively simple auto-regression model we used performed more 362 consistently on the range of partial data sets used for scoring than the more complex 363 stochastic simulation model did. The stochastic model has tended to produce tighter 364 prediction distributions that are prone to extreme failure when they get it wrong, while 365 the auto-regression model's predictions are more tolerant of unpredictable outcomes. However, we can note that our early projections of final size (Figs S9, S10 in S1 375 Supporting Information) fell below the counts that have been observed as of February. 376

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