Selected article for: "µ mortality rate and mortality rate"
Author: Rajesh Ranjan
Title: Estimating the Final Epidemic Size for COVID-19 Outbreak using Improved Epidemiological Models
  • Document date: 2020_4_16
    • ID: emyuny1a_37
    Snippet: The generalized SEIR Model as proposed in Reference [8] includes many more states than SIR model, namely susceptible cases (S), insusceptible cases (P ), exposed cases (E), infectious cases (I), quarantined cases (Q; confirmed and infected), recovered cases (R) and deceased cases (D). The governing equations for each of them are given in 2.1.4. The coefficients α, β, γ −1 , δ −1 , λ(t), κ(t) represent the protection rate, infection rate.....
    Document: The generalized SEIR Model as proposed in Reference [8] includes many more states than SIR model, namely susceptible cases (S), insusceptible cases (P ), exposed cases (E), infectious cases (I), quarantined cases (Q; confirmed and infected), recovered cases (R) and deceased cases (D). The governing equations for each of them are given in 2.1.4. The coefficients α, β, γ −1 , δ −1 , λ(t), κ(t) represent the protection rate, infection rate, average latent time, average quarantine time, cure rate, and mortality rate, separately. Among these the cure rate λ(t) = λ 0 (1 − exp(−λ 1 t)) and mortality rate κ(t) = κ 0 exp(−κ 1 t) are time dependent, with the former increasing with time while the latter first increases and then rapidly decreases. λ 0 , λ 1 , κ 0 , κ 1 are constant parameters.

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