Selected article for: "cumulative number and incubation period"
Author: Zhanwei Du; Ling Wang; Simon Cauchemez; Xiaoke Xu; Xianwen Wang; Benjamin J Cowling; Lauren Ancel Meyers
Title: Risk for Transportation of 2019 Novel Coronavirus (COVID-19) from Wuhan to Cities in China
  • Document date: 2020_1_30
    • ID: mbdj3r0m_30
    Snippet: We directly estimated the number of initial cases, i 0 , on December 1, 2019, and the epidemic growth rate, λ, during December 1, 2019-January 22, 2020. We infer the epidemic parameters in a Bayesian framework by using the Markov Chain Monte Carlo (MCMC) method with Hamiltonian Monte Carlo sampling and noninformative flat prior. From these, we derive the doubling time of incident cases as d T = log(2)/λ and the cumulative number of cases and of.....
    Document: We directly estimated the number of initial cases, i 0 , on December 1, 2019, and the epidemic growth rate, λ, during December 1, 2019-January 22, 2020. We infer the epidemic parameters in a Bayesian framework by using the Markov Chain Monte Carlo (MCMC) method with Hamiltonian Monte Carlo sampling and noninformative flat prior. From these, we derive the doubling time of incident cases as d T = log(2)/λ and the cumulative number of cases and of reported cases by January 22, 2020. We also derived the basic reproduction number by assuming a susceptible-exposed-infectious-recovery (SEIR) model for COVID-19, in which the incubation period is exponentially distributed with mean L in the range of 3 -6 days and the infectious period is also exponentially distributed with mean Z in the range of 2 to 7 days. The reproduction number is then given by

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