Selected article for: "binomial distribution and branching process"
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_1_1
    Snippet: contacts of infective individuals, or by other causes. We incorporated this 97 pattern into our model by estimating an initial reproduction number R initial and 98 quenching rate Ï„ for each outbreak by fitting an exponentially quenched curve to the 99 outbreak's estimates of R by day d (Fig S2 in S1 Supporting Information), and used 100 these pairs of parameters, one from each past outbreak, to construct a joint distribution 101 of initial repro.....
    Document: contacts of infective individuals, or by other causes. We incorporated this 97 pattern into our model by estimating an initial reproduction number R initial and 98 quenching rate Ï„ for each outbreak by fitting an exponentially quenched curve to the 99 outbreak's estimates of R by day d (Fig S2 in S1 Supporting Information), and used 100 these pairs of parameters, one from each past outbreak, to construct a joint distribution 101 of initial reproduction numbers and quenching rates for outbreak simulation. 102 We simulated EBOV transmission using a stochastic branching process model in 103 which the number of secondary cases caused by any given primary case is drawn from a 104 negative binomial distribution, whose mean is the reproduction number R as a function 105 of day of the outbreak, and variance is controlled by a dispersion parameter k [32, 33]. 106

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