data/seir.ts01.R

In davidearn/fastbeta: Fast Approximation of Time-Varying Infectious Disease Transmission Rates

## NB: seir.ts02 differs mainly in setting of 'stochastic'
seir.ts01 <- if (requireNamespace("deSolve")) local({

stochastic <- FALSE

beta <- function (t, a = 1e-01, b = 1e-05) b * (1 + a * sinpi(t / 26))
nu   <- function (t) 1e+03
mu   <- function (t) 1e-03
environment(beta) <- environment(nu) <- environment(mu) <- globalenv()

sigma <- 0.5
gamma <- 0.5
delta <- 0

m <- 1L
n <- 1L
init <- fastbeta:::seir.ee(beta(0), nu(0), mu(0), sigma, gamma, delta, m, n)
init <- trunc(init)

length.out <- 52L * 55L
prob <- 0.1
delay <- diff(stats::pgamma(0L:8L, 2.5))

X <- fastbeta::seir(length.out,
                    beta, nu, mu,
                    sigma, gamma, delta,
                    m, n, init,
                    stochastic = stochastic,
                    prob = prob, delay = delay, useCompiled = FALSE)
X <- stats::window(X, start = length.out - 1L - 52L * 5L)
stats::tsp(X)[1L:2L] <- stats::tsp(X)[1L:2L] - stats::tsp(X)[1L]

structure(X,
          beta = beta, nu = nu, mu = mu,
          sigma = sigma, gamma = gamma, delta = delta,
          m = m, n = n, init = X[1L, seq_len(m + n + 2L)],
          stochastic = stochastic,
          prob = prob, delay = delay)

})