Simulate a stochastic epidemic model path via Gillespie's direct method and returns a matrix containing a simulated path from a stochastic epidemic model.

simulate_gillespie(
  flow,
  parameters,
  constants,
  tcovar,
  t_max,
  init_states,
  rate_adjmat,
  tcovar_adjmat,
  tcovar_changemat,
  init_dims,
  forcing_inds,
  forcing_tcov_inds,
  forcings_out,
  forcing_transfers,
  rate_ptr
)

Arguments

flow: Flow matrix
parameters: Vector of parameters
constants: vector of constants
tcovar: matrix of time-varying covariates
init_states: vector of initial compartment counts
rate_adjmat: adjacency matrix for updating rates after each event
tcovar_adjmat: adjacency matrix for updating rates after each time a covariate changes
tcovar_changemat: indicator matrix identifying which covariates change at each time
init_dims: initial estimate for dimensions of the bookkeeping matrix, calculated as sum_strata(stratum size x number states x 3), rounded to the next greatest power of 2.
forcing_inds: logical vector of indicating at which times in the time-varying covariance matrix a forcing is applied.
rate_ptr: external function pointer to the lumped rate functions.
forcing_matrix: matrix containing the forcings.

Value

matrix with a simulated path from a stochastic epidemic model.