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## Getting age distribution of the population for a defined geographical area
## (metropolitan France or specific region)
get_populationVector <- function(name_area){
# region name is the name of the area of interest
# National for metropolitan France or the short abbreviations of the regions otherwise
if(name_area == 'Metro'){
population_data <- read.csv('Data/population_data/PopByRegionAndAge_MetropolianFrance.csv')
vect_name_region <- c('ARA', 'BFC', 'BRE', 'CVL', 'COR', 'GES', 'HDF', 'IDF', 'NAQ', 'NOR', 'OCC', 'PAC', 'PDL')
PopGeographicalArea <- apply(population_data[, vect_name_region], 1, sum)
} else{
population_data <- read.csv('Data/population_data/PopByRegionAndAge_EntireFrance.csv')
PopGeographicalArea <- population_data[, name_area]
}
names(PopGeographicalArea) <- population_data$Age
return(PopGeographicalArea)
}
## Function used to adjust the matrices for different infectivity/susceptibility
adjust_matrix_infectivity_susceptibility <- function(contactMatrix, susceptibility, infectivity){
# contactMatrix is of size nAge*nAge
# susceptibility and infectivity are vectors of length nAge
contactMatrix_corr<- matrix(0, ncol = ncol(contactMatrix), nrow = nrow(contactMatrix))
for(i in 1:nrow(contactMatrix_corr)){
for(j in 1:ncol(contactMatrix_corr)){
contactMatrix_corr[i,j] <- contactMatrix[i,j]*infectivity[j]*susceptibility[i]
}
}
return(contactMatrix_corr)
}
## Function used to symmetrize the matrices
## Symmetrization well explained in Funk et al., 2019 (BMC Medicine)
symmetrize_contact_matrix <- function(contactMatrix,
popSize.ageG){
n.ageG <- length(popSize.ageG)
tmp_mat_pop <- matrix(rep(popSize.ageG, n.ageG), ncol = n.ageG, nrow = n.ageG)
MatPop <- contactMatrix*tmp_mat_pop
NormalizedMat <- (MatPop + t(MatPop))/(2*tmp_mat_pop)
return(NormalizedMat)
}
## Getting the maximum eigenvalue of a matrix
get_max_eigenval <- function(M){
eigenvalues <- eigen(M)$values
max_eigenval <- max(Re(eigenvalues[abs(Im(eigenvalues)) < 1e-6]))
return(max_eigenval)
}
## Compute the reduced matrix from a matrix of reduction of contacts for all the age groups
compute_matrix_reduction_all_contacts <- function(CM,
vect_alpha){
mat_min_alpha <- sapply(vect_alpha, FUN = function(tmp_alpha){
sapply(vect_alpha, FUN = function(tmp_alpha2){
min(tmp_alpha, tmp_alpha2)
})
})
mat_res <- CM*mat_min_alpha
return(mat_res)
}
compute_normalized_matrix_reduction_all_contacts <- function(CM,
vect_alpha){
mat_res <- compute_matrix_reduction_all_contacts(CM, vect_alpha)
mat_res <- mat_res/get_max_eigenval(mat_res)
return(mat_res)
}
get_vect_alpha_from8 <- function(vect_8 # vector of size 8
){
return(c(vect_8[1:2], #0-9 ; 10-17
1, #18-29
vect_8[3], #30-39
rep(vect_8[4], 2), #40-49
rep(vect_8[5], 2), #50-59
rep(vect_8[6], 2), #60-69
rep(vect_8[7], 2), #70-79
vect_8[8] #80p
))
}