MCMC
MCMC.RdCombined MCMC Multi-Region - series of MCMC iterations for one or more regions
Usage
MCMC(
params_data = data.frame(name = "FOI_var1", initial = 1, max = Inf, min = -Inf, mean =
0, sd = 1, estimate = TRUE),
input_data = list(),
obs_sero_data = NULL,
obs_case_data = NULL,
filename_prefix = "Chain",
Niter = 1,
mode_start = 1,
time_inc = 1,
n_reps = 1,
enviro_data_const = list(),
enviro_data_var = list(),
deterministic = FALSE,
mode_time = 1,
mode_parallel = FALSE,
cluster = NULL
)Arguments
- params_data
#Data frame of parameter information containing names, initial values, maximum and minimum values, mean and standard deviation (for prior calculation) and flag indicating whether parameter estimated or fixed
Parameters to include: coefficients of environmental covariates to calculate FOI_spillover and R0, reported vaccination effectiveness, probability of severe case reporting, probability of fatal case reporting, Brazil FOI_spillover multiplier, FOI_spillover and R0 (latter two never estimated, included only for priors)
TBA - instructions- input_data
List of population and vaccination data for multiple regions (created using data input creation code and usually loaded from RDS file)
- obs_sero_data
Seroprevalence data for comparison, by region, year & age group, in format no. samples/no. positives (TBA - instructions)
- obs_case_data
Annual reported case/death data for comparison, by region and year, in format no. cases/no. deaths (TBA - instructions)
- filename_prefix
Prefix of output RDS file name, e.g. "Chain.Rds"
- Niter
Total number of iterations to run
- mode_start
Flag indicating how to set initial population immunity level in addition to vaccination
If mode_start = 0, only vaccinated individuals
If mode_start = 1, shift some non-vaccinated individuals into recovered to give herd immunity (stratified by age)
If mode_start = 2, use SEIRV input in list from previous run(s)- time_inc
time increment in days (must be 1 or 5)
- n_reps
Number of times to repeat calculations to get average likelihood at each iteration
- enviro_data_const
Data frame of values of constant environmental covariates (columns) by region (rows)
- enviro_data_var
List containing time-varying environmental covariate data:
regions: Vector of region labels
env_vars: Vector of covariate names
values: Array of covariate values with dimensions (number of covariates, number of regions, number of time points). Number of time points must be correct for mode_time setting.- deterministic
TRUE/FALSE - set model to run in deterministic mode if TRUE
- mode_time
Type of time dependence of FOI_spillover and R0 to be used:
If mode_time = 0, no time variation (constant values)
If mode_time = 1, FOI/R0 vary annually without seasonality (number of values = number of years to consider)
If mode_time = 2, FOI/R0 vary with monthly seasonality without inter-annual variation (number of values = 12)
If mode_time = 3, FOI/R0 vary with daily seasonality without inter-annual variation (number of values = 365/dt)
If mode_time = 4, FOI/R0 vary annually with monthly seasonality (number of values = 12*number of years to consider)
If mode_time = 5, FOI/R0 vary annually with daily seasonality (number of values = (365/dt)*number of years to consider)- mode_parallel
TRUE/FALSE - indicate whether to use parallel processing on supplied cluster for speed
- cluster
Cluster of threads to use if mode_parallel = TRUE '