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Worked Example 3 - Generating Epidemiological Data Sets

BWorkedExampleCDataGenerate.Rmd

This worked example demonstrates how to use the package to generate a set of epidemiological data (serological and/or annual reported severe/fatal cases) for specified years and regions (and age ranges, in the case of serological data).

This can be set up to either:

  • Match a set of observed data supplied in appropriate formats
  • Create a hypothetical set of data with no existing counterpart by matching to “dummy” data which presents dates, regions and/or age ranges in the appropriate formats

We first load the input data set, regional environmental covariate values, coefficients of environmental covariate values used to calculate spillover FOI and R0 values, and observed serological and annual case data (here generated by the model itself):

library(YEP)
input_data <- readRDS(file = paste(path.package("YEP"), 
                                  "/exdata/input_data_example.Rds", sep = ""))
enviro_data <- read.csv(file = paste(path.package("YEP"), 
                                  "/exdata/enviro_data_example.csv", sep = ""),
                        header = TRUE)
enviro_coeffs <- read.csv(file = paste(path.package("YEP"), 
                                       "/exdata/enviro_coeffs_example.csv",
                                       sep = ""), header = TRUE)
# Seroprevalence data for comparison, by region, year & age group, in format 
# no. samples/no. positives
obs_sero_data <- read.csv(file = paste(path.package("YEP"), 
                                    "/exdata/obs_sero_data_example.csv",
                                       sep = ""), header = TRUE)  
# Annual reported case/death data for comparison, by region and year, in format 
# no. cases/no. deaths  
obs_case_data <- read.csv(file = paste(path.package("YEP"), 
                                    "/exdata/obs_case_data_example.csv",
                                       sep = ""), header = TRUE)

We calculate values of spillover FOI and R0 as described in [TBA]:

n_regions <- nrow(enviro_data)
FOI_values=R0_values=rep(NA,n_regions)
for(n_region in 1:n_regions){
 FOI_R0_values <- param_calc_enviro(enviro_coeffs = enviro_coeffs[1, ], 
                  enviro_covar_values = enviro_data[n_region,c(2:ncol(enviro_data))])
 FOI_values[n_region] <- FOI_R0_values$FOI
 R0_values[n_region] <- FOI_R0_values$R0
}

[TBA]

vaccine_efficacy <- 1.0 
p_rep_severe <- 0.05
p_rep_death <- 0.1
mode_start <- 1     
start_SEIRV <- NULL       
dt <- 1.0            
n_reps <- 1    
deterministic <- TRUE
mode_parallel <- "none" #TBA
cluster <- NULL #TBA

[TBA]

dataset1 <- Generate_Dataset(input_data,FOI_values,R0_values,obs_sero_data,obs_case_data,vaccine_efficacy,
                            p_rep_severe,p_rep_death,mode_start,start_SEIRV,dt,n_reps,deterministic,mode_parallel,cluster)

[TBA]