Selected article for: "infectious rate and time infectious rate"
Author: Qihui Yang; Chunlin Yi; Aram Vajdi; Lee W Cohnstaedt; Hongyu Wu; Xiaolong Guo; Caterina M Scoglio
Title: Short-term forecasts and long-term mitigation evaluations for the COVID-19 epidemic in Hubei Province, China
  • Document date: 2020_3_30
    • ID: kcb68hue_13
    Snippet: Where β denotes the infection rate and γ denotes the removing rate. In the SIR model, we assume that the infection process of each infectious individual is Poisson and statistically independent and that the removing process for every infected individual is also Poisson. In this case, the infection rate β quantifies the probability that each susceptible individual can be infected by each infectious individual in a time unit, and the recovering .....
    Document: Where β denotes the infection rate and γ denotes the removing rate. In the SIR model, we assume that the infection process of each infectious individual is Poisson and statistically independent and that the removing process for every infected individual is also Poisson. In this case, the infection rate β quantifies the probability that each susceptible individual can be infected by each infectious individual in a time unit, and the recovering rate γ quantifies the average portion of the infectious population that will clear infection in a time unit. Considering that the population flow, prevention measures (like wearing N95 mask) and selfquarantine could affect the infection rate, we take the infection rate β as a time-varying parameter. The removing rate is set as a constant 1/7 based on the estimation from Huang et al. (2020) .

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