Simulate Epidemic Dynamics with Vaccination and a New Variants
Epi_MPC_run_V.RdThis function simulates epidemic dynamics using predefined parameters and incorporates vaccination and variant-specific adjustments to the reproduction number and population immunity. Policies are evaluated periodically to maximize rewards based on predicted outcomes.
Usage
Epi_MPC_run_V(
episimdata,
episettings,
epi_par,
noise_par,
actions,
pred_days,
n_ens = 100,
start_day = 1,
ndays = nrow(episimdata),
R_est_wind = 5,
pathogen = 1,
susceptibles = 1,
delay = 0,
ur = 0,
r_dir = 0,
N = 1e+06
)Arguments
- episimdata
A data frame containing simulation data. It should include columns for:
"I": Number of infected individuals."C": Reported cases."S": Number of susceptible individuals."R_coeff": Coefficient of reproduction number reduction by policy."vaccination_rate": Rate of vaccination."delta_prevalence": Prevalence of the Delta variant."immunity": Level of population immunity.
- epi_par
A data frame of epidemiological parameters, including:
"R0": Basic reproduction number."gen_time": Disease generation time."gen_time_var": Variance of the generation time.
- noise_par
A data frame containing noise parameters:
"repd_mean": Reporting delay mean."del_disp": Dispersion parameter for reporting delays."ur_mean": Mean under-reporting rate."ur_beta_a": Alpha parameter of Beta distribution for under-reporting.
- actions
A data frame containing policy actions with reproduction coefficients (
"R_coeff").- pred_days
An integer specifying the number of days to predict ahead during policy evaluation.
- n_ens
An integer specifying the number of ensemble runs for Monte Carlo simulations. Defaults to
100.- start_day
An integer indicating the start day of the simulation. Defaults to
1.- ndays
An integer specifying the total number of simulation days. Defaults to the number of rows in
episimdata.- R_est_wind
An integer specifying the rolling window size for reproduction number estimation. Defaults to
5.- pathogen
An integer or string identifying the pathogen for parameter selection. Defaults to
1.- susceptibles
A binary value (
0or1) indicating whether to simulate changes in susceptibles. Defaults to1.- delay
A binary value (
0or1) indicating whether to simulate reporting delays. Defaults to0.- ur
A binary value (
0or1) indicating whether to simulate under-reporting. Defaults to0.- N
A numeric value specifying the total population size. Defaults to
1e6.
Value
A data frame containing updated simulation data with computed reproduction numbers, estimated policies, daily infection incidents, cases, deaths, and other epidemic metrics.
Details
This function models the effects of vaccination and variants (e.g., Delta) on epidemic dynamics.
The vaccination rate is computed using vac(), while variant prevalence is determined
using delta(). Adjustments to the reproduction number and population immunity are
incorporated based on these factors.
Policies are evaluated periodically, and the optimal policy is selected based on expected rewards computed from ensemble simulations.
Examples
# Example data and parameters
episimdata <- data.frame(I = c(10, 20), C = c(10, 15), S = c(1000, 990), R_coeff = c(1.0, 0.9))
epi_par <- data.frame(
R0 = 2.5, gen_time = 5, gen_time_var = 1
)
noise_par <- data.frame(
repd_mean = 2, del_disp = 1.5, ur_mean = 0.8, ur_beta_a = 2
)
actions <- data.frame(R_coeff = c(1.0, 0.3))
results <- Epi_MPC_run_V(
episimdata = episimdata, epi_par = epi_par, noise_par = noise_par,
actions = actions, pred_days = 10, n_ens = 50, start_day = 1,
ndays = 20, R_est_wind = 5, pathogen = 1, susceptibles = 1,
delay = 0, ur = 0, N = 1e6
)
#> Error in Epi_MPC_run_V(episimdata = episimdata, epi_par = epi_par, noise_par = noise_par, actions = actions, pred_days = 10, n_ens = 50, start_day = 1, ndays = 20, R_est_wind = 5, pathogen = 1, susceptibles = 1, delay = 0, ur = 0, N = 1e+06): argument "episettings" is missing, with no default