Selected article for: "distribution mean and Erlang distribution"

Author: Anatoly Zhigljavsky; Roger Whitaker; Ivan Fesenko; Yakov Kremnitzer; Jack Noonan
Title: Comparison of different exit scenarios from the lock-down for COVID-19 epidemic in the UK and assessing uncertainty of the predictions
  • Document date: 2020_4_14
  • ID: mipdahk4_32
    Snippet: In a severe case, the person stays infectious for τ S days. The continuous version of τ S has Erlang distribution with shape parameter k S and rate parameter λ S . The mean of this distribution is k S /λ S = 2 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Figure 1 : Flow-chart for the process of illness 1/σ S . We use values k S.....
    Document: In a severe case, the person stays infectious for τ S days. The continuous version of τ S has Erlang distribution with shape parameter k S and rate parameter λ S . The mean of this distribution is k S /λ S = 2 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Figure 1 : Flow-chart for the process of illness 1/σ S . We use values k S = 3 and λ S = 1/7 so that Eτ S = k S /λ S = 21, in line with the current knowledge, see e.g. [5, 6, 7] . The variance of τ S is var(τ S ) = k S /λ 2 S ; for k M = 3 and λ S = 1/7 the standard deviation of the chosen Erlang distribution is approximately 12, which is rather large and reflects the uncertainty we currently have about the period of time a person needs to recover (or die) from COVID-19.

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