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fig3_total_eir_plots_no_resistance.R
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library(tidyverse)
library(readxl)
round.choose <- function(x, roundTo, dir = 1) {
if(dir == 1) { ##ROUND UP
x + (roundTo - x %% roundTo)
} else {
if(dir == 0) { ##ROUND DOWN
x - (x %% roundTo)
}
}
}
load("outputs/model_data_runs_new.RData")
params <- read_xlsx("data/parameter_sweep_new.xlsx")
params$run <- 1:length(params$RESISTANCE)
user_eir <- cbind(params$run, user_eir)
non_user_eir <- cbind(params$run, non_user_eir)
user_eir_1 <- user_eir[1,]
user_eir$resistance <- resistance[1:length(user_eir$varying_low)]
user_eir <- filter(user_eir, resistance == 0.0)
user_eir$resistance <- NULL
non_user_eir_1 <- non_user_eir[1,]
non_user_eir$resistance <- resistance[1:length(non_user_eir$varying_low)]
non_user_eir <- filter(non_user_eir, resistance == 0.0)
non_user_eir$resistance <- NULL
user_eir_long <- gather(user_eir, key = "run", value = "eir", -`params$run`)
user_eir_long <- filter(user_eir_long, ! run %in% c("varying_low_no_insect",
"varying_0.8_no_insect","varying_0.5_no_insect",
"varying_0.1_no_insect", "fixed_low",
"fixed_0.8","fixed_0.5",
"fixed_0.1", "indirect_low",
"indirect_0.1", "indirect_0.5",
"indirect_0.8", "varying_low_insect" ,
"varying_0.1_insect",
"varying_0.5_insect", "varying_0.8_insect"))
user_eir_long$run <- factor(user_eir_long$run,
levels = rev(c("fixed_low_no_insect", "fixed_0.1_no_insect",
"fixed_0.5_no_insect", "fixed_0.8_no_insect",
"indirect_low_no_insect", "indirect_0.1_no_insect",
"indirect_0.5_no_insect", "indirect_0.8_no_insect",
"fixed_low_insect" , "fixed_0.1_insect",
"fixed_0.5_insect", "fixed_0.8_insect",
"indirect_low_insect", "indirect_0.1_insect",
"indirect_0.5_insect", "indirect_0.8_insect",
"varying_low", "varying_0.1", "varying_0.5",
"varying_0.8")),
labels = rev(c("One user",
"10% usage",
"50% usage",
"80% usage",
"One user ",
"10% usage ",
"50% usage ",
"80% usage ",
"One user ",
"10% usage ",
"50% usage ",
"80% usage ",
"One user ",
"10% usage ",
"50% usage ",
"80% usage ",
"One user ",
"10% usage ",
"50% usage ",
"80% usage ")))
user_eir_long$scenario = ifelse(user_eir_long$run %in% c("One user", "10% usage","50% usage","80% usage"), "A",
ifelse(user_eir_long$run %in% c("One user ", "10% usage ", "50% usage ", "80% usage "), "B",
ifelse(user_eir_long$run %in% c("One user ", "10% usage ", "50% usage ", "80% usage "), "C",
ifelse(user_eir_long$run %in% c("One user ", "10% usage ", "50% usage ", "80% usage "), "D", "E"))))
user_eir_long$fill <- ifelse(user_eir_long$run %in%
c("One user",
"10% usage",
"50% usage",
"80% usage",
"One user ",
"10% usage ",
"50% usage ",
"80% usage "), "Direct",
ifelse(user_eir_long$run %in%
c("One user ",
"10% usage ",
"50% usage ",
"80% usage "), "Total", "Indirect"))
user_eir_long$cat = "Net user"
non_user_eir_long <- gather(non_user_eir, key = "run", value = "eir", -`params$run`)
non_user_eir_long <- filter(non_user_eir_long, ! run %in% c("varying_low_no_insect",
"varying_0.8_no_insect","varying_0.5_no_insect",
"varying_0.1_no_insect", "fixed_low",
"fixed_0.8","fixed_0.5",
"fixed_0.1", "indirect_low",
"indirect_0.1", "indirect_0.5",
"indirect_0.8", "varying_low_insect" ,
"varying_0.1_insect",
"varying_0.5_insect", "varying_0.8_insect"))
non_user_eir_long$run <- factor(non_user_eir_long$run,
levels = rev(c("fixed_low_no_insect", "fixed_0.1_no_insect",
"fixed_0.5_no_insect", "fixed_0.8_no_insect",
"indirect_low_no_insect", "indirect_0.1_no_insect",
"indirect_0.5_no_insect", "indirect_0.8_no_insect",
"fixed_low_insect" , "fixed_0.1_insect",
"fixed_0.5_insect", "fixed_0.8_insect",
"indirect_low_insect", "indirect_0.1_insect",
"indirect_0.5_insect", "indirect_0.8_insect",
"varying_low", "varying_0.1", "varying_0.5",
"varying_0.8")),
labels = rev(c("One user",
"10% usage",
"50% usage",
"80% usage",
"One user ",
"10% usage ",
"50% usage ",
"80% usage ",
"One user ",
"10% usage ",
"50% usage ",
"80% usage ",
"One user ",
"10% usage ",
"50% usage ",
"80% usage ",
"One user ",
"10% usage ",
"50% usage ",
"80% usage ")))
non_user_eir_long$scenario = ifelse(non_user_eir_long$run %in% c("One user", "10% usage","50% usage","80% usage"), "A",
ifelse(non_user_eir_long$run %in% c("One user ", "10% usage ", "50% usage ", "80% usage "), "B",
ifelse(non_user_eir_long$run %in% c("One user ", "10% usage ", "50% usage ", "80% usage "), "C",
ifelse(non_user_eir_long$run %in% c("One user ", "10% usage ", "50% usage ", "80% usage "), "D", "E"))))
non_user_eir_long$fill <- ifelse(non_user_eir_long$run %in%
c("One user",
"10% usage",
"50% usage",
"80% usage",
"One user ",
"10% usage ",
"50% usage ",
"80% usage "), "Direct",
ifelse(user_eir_long$run %in%
c("One user ",
"10% usage ",
"50% usage ",
"80% usage ") , "Total", "Indirect"))
non_user_eir_long$cat = "Non net user"
eir_data_long <- rbind(user_eir_long, non_user_eir_long)
dat = eir_data_long
dat$usage = ifelse(dat$run %in% dat$run[grep("One user", dat$run)] , 1e-5,
ifelse(dat$run %in% dat$run[grep("10% usage", dat$run)], 0.1,
ifelse(dat$run %in% dat$run[grep("50% usage", dat$run)], 0.5, 0.8)))
dat$contribution = ifelse(dat$cat == "Net user", dat$eir*dat$usage, dat$eir*(1-dat$usage))
dat$scenario = ifelse(dat$run %in% c("One user", "10% usage","50% usage","80% usage"), "A",
ifelse(dat$run %in% c("One user ", "10% usage ", "50% usage ", "80% usage "), "B",
ifelse(dat$run %in% c("One user ", "10% usage ", "50% usage ", "80% usage "), "C",
ifelse(dat$run %in% c("One user ", "10% usage ", "50% usage ", "80% usage "), "D", "E"))))
overall = dat %>% group_by(`params$run`, run, fill, scenario) %>%
summarise("eir" = sum(contribution))
overall$cat = "Community"
eir_data_long = rbind(eir_data_long, overall)
#--------------------------------------------------------------
# Numbers in text
a <- eir_data_long %>% filter(cat == "Net user", run == "One user")
print(sprintf("eir direct barrier 1 user:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025), 0.01, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Net user", run == "80% usage")
print(sprintf("eir direct barrier 80%% user:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025), 0.01, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Community", run == "One user")
print(sprintf("eir direct barrier 1 user community:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025), 0.01, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Community", run == "80% usage")
print(sprintf("eir direct barrier 80%% user community:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025), 0.01, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Net user", run == "One user ")
print(sprintf("eir indirect barrier 1 user:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025), 1, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Non net user", run == "One user ")
print(sprintf("eir indirect barrier 1 non-user:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025), 1, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Community", run == "One user ")
print(sprintf("eir indirect barrier 1 user community:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025), 1, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Net user", run == "80% usage ")
print(sprintf("eir indirect barrier 80%% user:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025), 1, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Non net user", run == "80% usage ")
print(sprintf("eir indirect barrier 80%% non-user:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025), 1, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Community", run == "80% usage ")
print(sprintf("eir indirect barrier 80%% community:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025), 1, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Net user", run == "One user ")
print(sprintf("eir direct insect 1 user:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025), 1, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Net user", run == "80% usage ")
print(sprintf("eir direct insect 80%% user:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025),1, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Community", run == "One user ")
print(sprintf("eir direct insect 1 community :%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025), 1, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Community", run == "80% usage ")
print(sprintf("eir direct insect 80%% community:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025),1, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Net user", run == "One user ")
print(sprintf("eir indirect insect 1 user:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025), 1, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Non net user", run == "One user ")
print(sprintf("eir indirect insect 1 non-user:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025), 1, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Community", run == "One user ")
print(sprintf("eir indirect insect 1 community:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025), 1, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Net user", run == "80% usage ")
print(sprintf("eir indirect insect 80%% user:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025), 1, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Non net user", run == "80% usage ")
print(sprintf("eir indirect insect 80%% non-user:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025), 1, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Community", run == "80% usage ")
print(sprintf("eir indirect insect 80%% community:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025), 1, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Net user", run == "One user ")
print(sprintf("total eir 1 user:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025), 1, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Non net user", run == "One user ")
print(sprintf("total eir 1 non-user:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),2),
round.choose(quantile(a$eir, probs= 0.025), 1, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Community", run == "One user ")
print(sprintf("total eir 1 community:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),2),
round.choose(quantile(a$eir, probs= 0.025), 1, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Net user", run == "80% usage ")
print(sprintf("total eir 80%% user:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025), 1, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Non net user", run == "80% usage ")
print(sprintf("total eir 80%% non-user:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025), 1, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
a <- eir_data_long %>% filter(cat == "Community", run == "80% usage ")
print(sprintf("total eir 80%% community:%.0f [95%% CI: %.0f - %.0f]", round(quantile(a$eir, probs= 0.5),0),
round.choose(quantile(a$eir, probs= 0.025), 1, 0), round.choose(quantile(a$eir, probs=0.975), 1, 1)))
saveRDS(a, "outputs/community_total.RDS")
#--------------------------------------------------------------
# Reformat for plot
eir_data_long$run[grep("One user", eir_data_long$run)] <- "One user"
eir_data_long$run[grep("10% usage", eir_data_long$run)] <- "10% usage"
eir_data_long$run[grep("50% usage", eir_data_long$run)] <- "50% usage"
eir_data_long$run[grep("80% usage", eir_data_long$run)] <- "80% usage"
#-------------------------
# Plots
scenario_names <- c(
"A" = "A: Direct benefit of barrier (control - a)",
"B" = "B: Indirect benefit of barrier (a - b)",
"C" = "D: Additional direct benefit of insecticide (a - c)",
"D" = "D: Additional indirect benefit of insecticide (b - d - (a - c))",
"E" = "E: All (control - d)"
)
p <- ggplot(eir_data_long) +
geom_boxplot(aes(eir/100, run, col = cat, fill = fill)) +
facet_wrap(~scenario, ncol = 1, labeller = as_labeller(scenario_names)) +
scale_x_continuous(minor_breaks = seq(0 , 100/100, 10/100), breaks = seq(0, 100/100, 20/100), labels = scales::percent) +
ylab("Scenario") + xlab("Relative reduction in EIR") + scale_colour_discrete(name = "User type") +
scale_fill_manual(name = "Protection type", values = c("Direct" = "grey", "Indirect" = "yellow",
"Total"= "purple")) +
theme_bw()
print(p)
ggsave("figures/fig_3.pdf", p, height = 6, width = 8)
ggsave("figures/fig_3.png", p, height = 6, width = 8)
overall = dat %>% filter(fill == "Total") %>% group_by(`params$run`, run, fill, usage) %>%
summarise("eir" = sum(contribution))
com_data = overall %>% filter(fill == "Total") %>%
group_by(usage) %>%
summarise("mean_eir" = mean(eir))
new_data = data.frame("usage" = seq(0, 0.8, by = 0.1),
"mean_eir" = rep(NA, length(seq(0, 0.8, by = 0.1))))
model<-lm(mean_eir ~ poly(usage, 2, raw=TRUE), data = com_data)
new_data$mean_eir <- predict(model, newdata = new_data)
com_data
p2 = ggplot(com_data) +
geom_point(aes(usage, mean_eir)) +
geom_line(data = new_data, aes(usage, mean_eir )) +
xlab("Community usage") + ylab("EIR") +
theme_bw()