Selected article for: "growth rate and quasi Poisson family"
Author: Lorenzo Pellis; Francesca Scarabel; Helena B Stage; Christopher E Overton; Lauren H K Chappell; Katrina A Lythgoe; Elizabeth Fearon; Emma Bennett; Jacob Curran-Sebastian; Rajenki Das; Martyn Fyles; Hugo Lewkowicz; Xiaoxi Pang; Bindu Vekaria; Luke Webb; Thomas A House; Ian Hall
Title: Challenges in control of Covid-19: short doubling time and long delay to effect of interventions
  • Document date: 2020_4_15
    • ID: k5q07y4b_40
    Snippet: A natural mathematical model to derive the estimate of r is a Poisson family generalised linear model (GLM) with a log link. Given the over-dispersed noise inherent in both disease dynamics and surveillance data, a quasi-Poisson family is considered here. The growth rate r is more intuitively reported as a doubling time (the time taken to double case numbers) and so t D = ln(2)/r. The log-linear analysis from a Poisson GLM defined formally below .....
    Document: A natural mathematical model to derive the estimate of r is a Poisson family generalised linear model (GLM) with a log link. Given the over-dispersed noise inherent in both disease dynamics and surveillance data, a quasi-Poisson family is considered here. The growth rate r is more intuitively reported as a doubling time (the time taken to double case numbers) and so t D = ln(2)/r. The log-linear analysis from a Poisson GLM defined formally below is restricted to datasets (or time windows) with clear exponential growth, or when additional explanatory variables, which are rarely available in real time, exist.

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