Projection of epidemic outcomes and calculating expected reward -- use when death data is not available
Epi_pred.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.
Usage
Epi_pred(
episimdata,
episettings,
epi_par,
noise_par,
actions,
pathogen,
pred_days,
kk,
jj,
N,
ndays = nrow(episimdata),
pred_susceptibles = 0,
gamma = 0.95
)Arguments
- episimdata
A data frame containing epidemic simulation data.
- epi_par
A data frame of epidemiological parameters, with rows corresponding to different pathogens.
- noise_par
A parameter related to stochastic noise in the epidemic simulation (not used in the function).
- actions
A data frame of control actions, where the second column modifies the estimated reproduction number.
- pathogen
An integer specifying the pathogen to extract corresponding epidemiological parameters.
- pred_days
The number of days to predict forward.
- kk
The starting day of prediction.
- jj
The index of the action scenario being simulated.
- N
The total population size.
- ndays
The total number of days in the epidemic simulation (default: `nrow(episimdata)`).
- pred_susceptibles
Logical (0 or 1), indicating whether susceptible population dynamics should be accounted for.
- gamma
The discount factor for future rewards (default: 0.95).
Details
This function updates the effective reproduction number (`Re`), computes the expected number of new cases (`C`), and applies a discount factor to rewards computed using `reward_fun`. The function assumes that cases follow a Poisson process and uses a gamma-distributed generation time to estimate the infection dynamics.