Selected article for: "gamma distribution and serial interval"

Author: Sang Woo Park; David Champredon; Joshua S. Weitz; Jonathan Dushoff
Title: A practical generation interval-based approach to inferring the strength of epidemics from their speed
  • Document date: 2018_5_2
  • ID: jry46itn_39
    Snippet: The approximation is within 1% of the pseudo-realistic distribution it is approximating across the range of country estimates, and within 5% across the range shown. It is also within 2% of the World Health Organization (WHO) estimates derived from the observed time series assuming a poisson process with a known serial interval distribution. We also applied the moment approximation to a pseudo-realistic generation-interval distribution based on in.....
    Document: The approximation is within 1% of the pseudo-realistic distribution it is approximating across the range of country estimates, and within 5% across the range shown. It is also within 2% of the World Health Organization (WHO) estimates derived from the observed time series assuming a poisson process with a known serial interval distribution. We also applied the moment approximation to a pseudo-realistic generation-interval distribution based on information about measles from 12 . CC-BY 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It . https://doi.org/10.1101/312397 doi: bioRxiv preprint [19] , [22] , and [5] . Incubation periods were assumed to follow a lognormal distribution [19] . Infectious periods were assumed to follow a gamma distribution with coefficient of variation of 0.2 [38, 22, 17] . Since variation in infectious period is relatively low [38, 17] , and infectious period is short compared to incubation period, this choice is reasonable (and our results are not sensitive to the details).

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