Selected article for: "incubation time and log normal distribution"

Author: Ally Bi-zhu Jiang; Richard Lieu; Siobhan Quenby
Title: Significantly longer Covid-19 incubation times for the elderly, from a case study of 136 patients throughout China
  • Document date: 2020_4_18
  • ID: gxk5t1yp_12
    Snippet: w.r.t. the model parameters α, β, γ, · · ·, where n k ≡ n k (α, β, γ, · · ·) is the num-50 ber of cases for incubation time interval k as expected by the model,n k is the observed number of cases, and σ 2 k = n k is the expected model variance assuming Poisson fluctuation in the case counts. In the case of the currently scrutinized log normal model distribution consisting of three parameters α = µ, β = σ, and γ = n 0 (where µ.....
    Document: w.r.t. the model parameters α, β, γ, · · ·, where n k ≡ n k (α, β, γ, · · ·) is the num-50 ber of cases for incubation time interval k as expected by the model,n k is the observed number of cases, and σ 2 k = n k is the expected model variance assuming Poisson fluctuation in the case counts. In the case of the currently scrutinized log normal model distribution consisting of three parameters α = µ, β = σ, and γ = n 0 (where µ, σ, and n 0 are as in (3)), 95 % confidence intervals δµ, δσ, and 55 δn 0 for the best-fit parameters may be inferred from the ∆χ 2 criterion discussed below. For a log normal quantile q = q(m) = e µ+mσ , ∆χ 2 also yields δq. Yet an alternative method is to take advantage of the independence of µ and σ in the model by writing

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