Selected article for: "determine bandwidth and extended tail"

Author: Worden, Lee; Wannier, Rae; Hoff, Nicole A.; Musene, Kamy; Selo, Bernice; Mossoko, Mathias; Okitolonda-Wemakoy, Emile; Muyembe Tamfum, Jean Jacques; Rutherford, George W.; Lietman, Thomas M.; Rimoin, Anne W.; Porco, Travis C.; Kelly, J. Daniel
Title: Projections of epidemic transmission and estimation of vaccination impact during an ongoing Ebola virus disease outbreak in Northeastern Democratic Republic of Congo, as of Feb. 25, 2019
  • Document date: 2019_8_5
  • ID: 1lg2203q_26
    Snippet: To generate forecasts, from each data set 320 to 330 simulated outbreaks were collected that passed the step of filtering on approximate agreement with DRC case counts. The simulated outbreaks that were retained after filtering were continued until they generated no further cases. Rare outlying simulated outbreaks that exceeded 300,000 cases were capped at the first value reached above that number, to limit unnecessary computation. We used this e.....
    Document: To generate forecasts, from each data set 320 to 330 simulated outbreaks were collected that passed the step of filtering on approximate agreement with DRC case counts. The simulated outbreaks that were retained after filtering were continued until they generated no further cases. Rare outlying simulated outbreaks that exceeded 300,000 cases were capped at the first value reached above that number, to limit unnecessary computation. We used this ensemble to derive a distribution of final outbreak sizes, and of cumulative counts at specific forecasting dates. Projection distributions were constructed using kernel density estimation with leaveone-out cross-validation to determine bandwidth, using a log-normal kernel for final sizes, due to the extended tail of the values, and a normal kernel for all other estimates. We calculated median values and 95% prediction intervals using the 2.5th and 97.5th percentiles of simulated outbreak size.

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