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Projection of epidemic outcomes and calculates expected reward using Deaths only

Epi_pred_est_D.Rd

This function projects epidemic progression based on given parameters, simulating infections and deaths over a prediction window. It incorporates intervention effects and computes expected reward values using Deaths only. Assumes infections are not seen/considered.

Usage

Epi_pred_est_D(
  episimdata,
  episettings,
  epi_par,
  noise_par,
  actions,
  pathogen,
  pred_days,
  r_dir,
  kk,
  jj,
  N,
  ndays = nrow(episimdata),
  pred_susceptibles = 0,
  gamma = 0.95
)

Arguments

episimdata

Data frame containing epidemic simulation data.

epi_par

Data frame of epidemiological parameters indexed by pathogen.

noise_par

Data frame of noise parameters (not used in function but included for consistency).

actions

Data frame of intervention actions with their corresponding effects.

pathogen

An integer specifying the pathogen to extract corresponding epidemiological parameters.

pred_days

Integer indicating the number of days to predict ahead.

r_dir

Integer controlling the reproductive number adjustment method (1, 2, or other).

kk

Integer indicating the current time step in the simulation.

jj

Integer indexing the intervention scenario.

N

Integer representing the total population.

ndays

Integer specifying the total number of days in the simulation (default: number of rows in episimdata).

pred_susceptibles

Integer flag (0 or 1) determining whether to adjust Re by the susceptible population.

gamma

Discount factor for calculating expected reward.

Value

Numeric value representing the expected reward over the prediction window.

Details

The function simulates epidemic spread using the gamma distribution to model the generation time and mortality time. It applies interventions, updates the effective reproduction number (Re), and predicts deaths via a Poisson process. The expected reward is calculated based on reward_fun_wd.

Examples

# Example usage (assuming required inputs are available):
result <- Epi_pred_est_D(episimdata, epi_par, noise_par, actions, "influenza",
                         pred_days = 14, r_dir = 1, kk = 50, jj = 2, N = 1e6)
#> Error in eval(expr, envir, enclos): object 'epi_par' not found