Selected article for: "binomial distribution and negative mean binomial distribution"

Author: Corey M Peak; Lauren M Childs; Yonatan H Grad; Caroline O Buckee
Title: Containing Emerging Epidemics: a Quantitative Comparison of Quarantine and Symptom Monitoring
  • Document date: 2016_8_31
  • ID: 2j4z5rp8_67
    Snippet: The total number of infections ( ! ) generated by individual is drawn from a negative binomial distribution with mean equal to the total expected number of infections . CC-BY-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder.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/10.1101/072652 doi: bioRxiv preprint ( ! = ! ! ) generated by individual and dispersion factor . If = 1, the.....
    Document: The total number of infections ( ! ) generated by individual is drawn from a negative binomial distribution with mean equal to the total expected number of infections . CC-BY-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder.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/10.1101/072652 doi: bioRxiv preprint ( ! = ! ! ) generated by individual and dispersion factor . If = 1, the negative binomial distribution reduces to a Poisson distribution with rate = ! ! . If < 1, the number of infections generated per case will be overdispersed to simulate super-spreading (Fig S5) .

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