Selected article for: "bayesian approach and gaussian distribution"

Author: Jose Lourenco; Robert Paton; Mahan Ghafari; Moritz Kraemer; Craig Thompson; Peter Simmonds; Paul Klenerman; Sunetra Gupta
Title: Fundamental principles of epidemic spread highlight the immediate need for large-scale serological surveys to assess the stage of the SARS-CoV-2 epidemic
  • Document date: 2020_3_26
  • ID: n10abfvd_17
    Snippet: Model output on cumulative death counts ( ) is fitted to the reported time series of deaths Λ (see Data) using a Bayesian MCMC approach previously implemented in other modelling studies [7] [8] [9] [10] . Model variables are summarized in Table 1 . [11] [12] [13] infectious period (days) 1/ σ Gaussian distribution G(M=4.5, SD=1) [11, [14] [15] [16] transmission coefficient β R β = σ 0 --time (days) between infection and death ψ Gaussian dis.....
    Document: Model output on cumulative death counts ( ) is fitted to the reported time series of deaths Λ (see Data) using a Bayesian MCMC approach previously implemented in other modelling studies [7] [8] [9] [10] . Model variables are summarized in Table 1 . [11] [12] [13] infectious period (days) 1/ σ Gaussian distribution G(M=4.5, SD=1) [11, [14] [15] [16] transmission coefficient β R β = σ 0 --time (days) between infection and death ψ Gaussian distribution G(M=17, SD=2) [14] probability of dying with severe disease θ Gaussian distribution G(M=0.14, SD=0.007) [1, 2, 11, 17] proportion of population at risk of severe disease ρ

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