analyses/results/other_statistics_gpm2gpf.R

In thednainus/senegalHIVmodel: Analysis of HIV data from Senegal

# Calculates the proportion of infections in gpf attributable to gpm.
# To understand how trajectories were solved see Trajectories.R or
# Trajectories_allSubtypes.R at /analyses/results/

library(senegalHIVmodel)
library(ggplot2)
library(purrr)

# SUBTYPE C
# Load solved objects
load("analyses/plots/solved_objects/FINAL/dmC_m1.2.rda")
load("analyses/plots/solved_objects/FINAL/dmC_m2.rda")

# MODEL 1
gpm2gpf_Cm1 <- deme2deme(births.p = dmC_m1.2$run[2,],
                         times = dmC_m1.2$run[[1]],
                         r = 1,
                         c = 2)
gpm2gpf_Cm1["Model"] <- "Model 1"

# MODEL 2
gpm2gpf_Cm2 <- deme2deme(births.p = dmC_m2.2$run[2,],
                         times = dmC_m2.2$run[[1]],
                         r = 1,
                         c = 2)
gpm2gpf_Cm2["Model"] <- "Model 2"

# PREVALENCE
# Load solved objects
load("analyses/plots/solved_objects/FINAL/dmC_m3.rda")
load("analyses/plots/solved_objects/FINAL/dmC_m4.2.rda")

# MODEL 3
gpm2gpf_Cm3 <- deme2deme(births.p = dmC_m3.1$run[2,],
                         times = dmC_m3.1$run[[1]],
                         r = 1,
                         c = 2)
gpm2gpf_Cm3["Model"] <- "Model 3"


# MODEL 4
gpm2gpf_Cm4 <- deme2deme(births.p = dmC_m4.2$run[2,],
                         times = dmC_m4.2$run[[1]],
                         r = 1,
                         c = 2)
gpm2gpf_Cm4["Model"] <- "Model 4"

# merge dataframes
gpm2gpf_C <- rbind(gpm2gpf_Cm1[1,], gpm2gpf_Cm2[1,],
                   gpm2gpf_Cm3[1,], gpm2gpf_Cm4[1,])

rm(dmC_m1.2, dmC_m2.1, dmC_m2.2, dmC_m3.1, dmC_m4.2)
###############################################################################
# SUBTYPE 02AG
# Load solved objects
load("analyses/plots/solved_objects/FINAL/dmAG_m1.rda")
load("analyses/plots/solved_objects/FINAL/dmAG_m2.rda")

# MODEL 1
gpm2gpf_AGm1 <- deme2deme(births.p = dmAG_m1.1$run[2,],
                          times = dmAG_m1.1$run[[1]],
                          r = 1,
                          c = 2)
gpm2gpf_AGm1["Model"] <- "Model 1"

# MODEL 2
gpm2gpf_AGm2 <- deme2deme(births.p = dmAG_m2.1$run[2,],
                          times = dmAG_m2.1$run[[1]],
                          r = 1,
                          c = 2)
gpm2gpf_AGm2["Model"] <- "Model 2"


# PREVALENCE
# Load solved objects
load("analyses/plots/solved_objects/FINAL/dmAG_m3.2.rda")
load("analyses/plots/solved_objects/FINAL/dmAG_m4.2.rda")

# MODEL 3
gpm2gpf_AGm3 <- deme2deme(births.p = dmAG_m3.2$run[2,],
                          times = dmAG_m3.2$run[[1]],
                          r = 1,
                          c = 2)
gpm2gpf_AGm3["Model"] <- "Model 3"

# MODEL 4
gpm2gpf_AGm4 <- deme2deme(births.p = dmAG_m4.2$run[2,],
                          times = dmAG_m4.2$run[[1]],
                          r = 1,
                          c = 2)
gpm2gpf_AGm4["Model"] <- "Model 4"

gpm2gpf_AG <- rbind(gpm2gpf_AGm1[1,], gpm2gpf_AGm2[1,],
                    gpm2gpf_AGm3[1,], gpm2gpf_AGm4[1,])


rm(dmAG_m1.1, dmAG_m2.1, dmAG_m3.2, dmAG_m4.2)
##############################################################################
# SUBTYPES COMBINED

# Load solved objects
load("analyses/plots/solved_objects/FINAL/dm_m2.2.rda")
load("analyses/plots/solved_objects/FINAL/dm_m3.2.rda")
load("analyses/plots/solved_objects/FINAL/dm_m4.2.rda")

#PREVALENCE
load("analyses/plots/solved_objects/FINAL/dm_m5.rda")
load("analyses/plots/solved_objects/FINAL/dm_m6.rda")
load("analyses/plots/solved_objects/FINAL/dm_m7.2.rda")

# Model 2
gpm2gpf_m2 <- deme2deme(births.p = dm_m2.2$run[2,],
                        times = dm_m2.2$run[[1]],
                        r = 1,
                        c = 2)
gpm2gpf_m2["Model"] <- "Model 2"


# Model 5 (prevalence) = Model 2
gpm2gpf_m5 <- deme2deme(births.p = dm_m5.1$run[2,],
                        times = dm_m5.1$run[[1]],
                        r = 1,
                        c = 2)
gpm2gpf_m5["Model"] <- "Model 5"


# Model 3
gpm2gpf_m3 <- deme2deme(births.p = dm_m3.2$run[2,],
                        times = dm_m3.2$run[[1]],
                        r = 1,
                        c = 2)
gpm2gpf_m3["Model"] <- "Model 3"


# Model 6 (prevalence) = Model 3
gpm2gpf_m6 <- deme2deme(births.p = dm_m6.1$run[2,],
                        times = dm_m6.1$run[[1]],
                        r = 1,
                        c = 2)
gpm2gpf_m6["Model"] <- "Model 6"


# Model 4
gpm2gpf_m4 <- deme2deme(births.p = dm_m4.2$run[2,],
                        times = dm_m4.2$run[[1]],
                        r = 1,
                        c = 2)
gpm2gpf_m4["Model"] <- "Model 4"


# Model 7 (prevalence) = Model 4
gpm2gpf_m7 <- deme2deme(births.p = dm_m7.2$run[2,],
                        times = dm_m7.2$run[[1]],
                        r = 1,
                        c = 2)
gpm2gpf_m7["Model"] <- "Model 7"


gpm2gpf_All <- rbind(gpm2gpf_m2[1,], gpm2gpf_m3[1,],gpm2gpf_m4[1,],
                     gpm2gpf_m5[1,], gpm2gpf_m6[1,],gpm2gpf_m7[1,])


save(gpm2gpf_C, gpm2gpf_AG, gpm2gpf_All, file="gpm2gpf.rda")

rm(dm_m2.2, dm_m3.2, dm_m4.2, dm_m5.1, dm_m6.1, dm_m7.2)