Selected article for: "case exponential growth and exponential growth"
Author: Justin D Silverman; Alex D Washburne
Title: Using ILI surveillance to estimate state-specific case detection rates and forecast SARS-CoV-2 spread in the United States
  • Document date: 2020_4_3
    • ID: 17oac3bg_50
    Snippet: Each simulation was initialized with (S, E, I, R, t) = (3.27 × 10 8 , 0, 1, 0, 0) where time 0 was January 15. To simulate the stochastic time it took from the first case to the onset of regular exponential growth, a Gillespie algorithm was used from the initial conditions until either t = 50 (March 5, 2020) or E(t) + I(t) = 100. The initial Gillespie algorithm was implemented on the assumption that a large amount of variation in the epidemic tr.....
    Document: Each simulation was initialized with (S, E, I, R, t) = (3.27 × 10 8 , 0, 1, 0, 0) where time 0 was January 15. To simulate the stochastic time it took from the first case to the onset of regular exponential growth, a Gillespie algorithm was used from the initial conditions until either t = 50 (March 5, 2020) or E(t) + I(t) = 100. The initial Gillespie algorithm was implemented on the assumption that a large amount of variation in the epidemic trajectory stems from uncertainty in 285 trajectory of early transmission chains. The output from Gillespie simulations was input as an initial value into the system of differential equations and integrated until the August 5, 2020. The number of infected individuals on a given day was the last observed I(t) for that day, and a weekly pool of infected patients was computed by a moving sum over the number of infected individuals every day for the past week, I w (t) = k=6 k=0 I t−k .

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