Modelling the Cost-Effectiveness, Budget and Equity Impacts of Malaria Vaccine Introduction alongside other Malaria Prevention Strategies
Kenya, Uganda, and Burkina Faso
James Oguta- Jalorche Consulting Inc.
2026-06-15
Executive Summary
This analysis presents a Markov model for evaluating malaria intervention strategies in children under 5 years across urban and rural settings in sub-Saharan Africa. The model compares four intervention strategies across urban and rural settings in children under 5 years old. The distributional impacts are also stratified by wealth quintile.
NOTE: The parameters to be updated with country-specific data.
Interventions Being Compared The analysis evaluates four intervention strategies (all layered on top of ITNs as baseline prevention):
Strategy Target Population Efficacy Cost Key Features Baseline (ITNs only) Universal 45% against transmission $2.50 per net Current standard of care SMC + ITNs Children 2-5 years 85% against clinical malaria $6.00 per child/year 4 cycles during rainy season Vaccine + ITNs Children 0-24 months 30% infection, 70% severe $26.00 for 4 doses RTS,S/AS01 vaccine Combined Strategy All ages (layered) Synergistic effect Rural multiplier: 1.5× Maximal intervention
Model Overview
Time horizon: 10 years
Cohort size: 10,000 children (scaled from birth to age 5+)
Discount rate: 3% per annum for both costs and health outcomes (DALYs)
Perspective: Healthcare system (direct medical costs + intervention delivery)
Outcomes: Clinical cases, severe cases, deaths, DALYs (discounted), costs (discounted)
Equity Stratifiers: Residence (Rural/Urban) and Wealth quintile
# Clear environment to prevent conflicts from previous runs
rm(list = ls())
# Force garbage collection to free memory
gc()## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 619201 33.1 1419171 75.8 NA 700251 37.4
## Vcells 1180965 9.1 8388608 64.0 49152 1963393 15.0# Load required packages
library(ggplot2)
library(dplyr)
library(tidyr)
library(knitr)
library(kableExtra)
library(readxl)
library(reshape2)
library(gridExtra)
library(scales)
# Set seed for reproducibility
set.seed(2026)
# Helper function for formatted numbers
format_number <- function(x) {
result <- ifelse(is.na(x) | is.infinite(x), "NA",
format(round(x, 0), big.mark = ",", scientific = FALSE))
return(result)
}
cat("Environment setup complete\n")## Environment setup complete## ✓ Environment setup complete## ✓ All packages loaded successfullyModel Structure
# ===================================================================
# MARKOV MODEL DIAGRAM
# ===================================================================
# Create Markov model diagram
if(!require(diagram)) {
install.packages("diagram")
library(diagram)
}
# Create a new plot
plot.new()
par(mar = c(2, 2, 3, 2), bg = "white")
# Define state positions
pos <- rbind(
c(0.5, 0.85), # Susceptible
c(0.5, 0.60), # Clinical
c(0.30, 0.35), # Severe
c(0.70, 0.35) # Death
)
# Define colors
state_colors <- c("Susceptible" = "#A8E6A3", "Clinical" = "#FFD3B6",
"Severe" = "#FF8B94", "Death" = "#AAAAAA")
transition_colors <- c("progression" = "#E41A1C", "mortality" = "#000000",
"recovery" = "#377EB8")
states <- c("Susceptible\n(Uninfected)", "Clinical Malaria\n(Non-severe)",
"Severe Malaria", "Death")
# Draw transitions
straightarrow(from = pos[1,], to = pos[2,], lwd = 2.5, arr.pos = 0.6,
arr.type = "triangle", lcol = transition_colors["progression"],
arr.col = transition_colors["progression"])
textrect(mid = c(0.55, 0.72), radx = 0.10, rady = 0.025,
lab = expression(bold(paste("Infection x", delta[clinical]))),
cex = 0.85, shadow.size = 0, box.col = "white",
col = transition_colors["progression"])
straightarrow(from = pos[2,], to = pos[3,], lwd = 2.5, arr.pos = 0.6,
arr.type = "triangle", lcol = transition_colors["progression"],
arr.col = transition_colors["progression"])
textrect(mid = c(0.35, 0.47), radx = 0.08, rady = 0.025,
lab = expression(bold(delta[severe])), cex = 0.85,
shadow.size = 0, box.col = "white",
col = transition_colors["progression"])
straightarrow(from = pos[3,], to = pos[4,], lwd = 2.5, arr.pos = 0.6,
arr.type = "triangle", lcol = transition_colors["mortality"],
arr.col = transition_colors["mortality"])
textrect(mid = c(0.5, 0.25), radx = 0.10, rady = 0.025,
lab = expression(bold(paste("Case fatality = CFR"))), cex = 0.85,
shadow.size = 0, box.col = "white",
col = transition_colors["mortality"])
curvedarrow(from = pos[2,], to = pos[1,], lwd = 2.5, arr.pos = 0.6,
curve = -0.4, lcol = transition_colors["recovery"],
arr.col = transition_colors["recovery"], arr.type = "triangle")
textrect(mid = c(0.30, 0.78), radx = 0.12, rady = 0.025,
lab = "Recovery +\nImmunity", cex = 0.80, shadow.size = 0,
box.col = "#A8E6A3", col = transition_colors["recovery"], font = 2)
curvedarrow(from = pos[3,], to = pos[1,], lwd = 2.5, arr.pos = 0.6,
curve = -0.5, lcol = transition_colors["recovery"],
arr.col = transition_colors["recovery"], arr.type = "triangle")
textrect(mid = c(0.12, 0.60), radx = 0.10, rady = 0.025,
lab = "Recovery", cex = 0.85, shadow.size = 0,
box.col = "#A8E6A3", col = transition_colors["recovery"], font = 2)
# Draw states
for(i in 1:4) {
textrect(mid = pos[i,], radx = 0.15, rady = 0.07, lab = states[i],
cex = 1.0, shadow.size = 0.01, box.col = state_colors[i],
col = "white", lwd = 3, font = 2)
}
title(main = "Markov Model Structure for Malaria Progression",
cex.main = 1.4, line = 0.8, font.main = 2)
text(x = 0.5, y = 0.96,
labels = "Monthly transitions with time-dependent discounting (3% annually)",
cex = 0.85, font = 3, col = "#555555")
Part 1: Country-Specific Parameters Kenya Parameters
# Kenya-specific parameters
kenya_params <- list(
country_name = "Kenya",
geo_params = list(
Urban = list(
itn_coverage = 0.40,
vaccine_coverage = 0.79,
smc_coverage = 0.71,
eir_multiplier = 0.4,
cfr_multiplier = 1.0,
access_delay_days = 0.5,
care_seeking = 0.60,
malaria_positive = 0.13
),
Rural = list(
itn_coverage = 0.57,
vaccine_coverage = 0.62,
smc_coverage = 0.71,
eir_multiplier = 2.0,
cfr_multiplier = 1.6,
access_delay_days = 4.5,
care_seeking = 0.65,
malaria_positive = 0.28
)
),
age_params = data.frame(
Age_Group = c("0-6m", "6-24m", "2-5y", "5+"),
Population_Weight = c(0.13, 0.32, 0.55, 0.00),
Clinical_Risk = c(0.15, 0.16, 0.31, 0.04),
Severe_Risk = c(0.08, 0.06, 0.02, 0.005),
CFR_Base = c(0.037, 0.037, 0.037, 0.037)
),
base_params = list(
base_eir = 25,
smc_efficacy = 0.71,
vaccine_ve_infection = 0.38,
vaccine_ve_severe = 0.32,
itn_efficacy = 0.25,
cost_vaccine_full = 37.39,
cost_smc_per_child_per_year = 6.00,
cost_act = 1.50,
cost_outpatient = 8.00,
cost_hospital = 38.00,
cfr_base = 0.037,
dw_clinical = 0.006,
dw_severe = 0.133,
life_expectancy = 65,
discount_rate = 0.03
),
geo_pop_weights = c(Urban = 0.30, Rural = 0.70)
)
# Display Kenya parameters
kable(kenya_params$age_params, caption = "Kenya: Age-Specific Parameters",
digits = 3, format = "html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"))| Age_Group | Population_Weight | Clinical_Risk | Severe_Risk | CFR_Base |
|---|---|---|---|---|
| 0-6m | 0.13 | 0.15 | 0.080 | 0.037 |
| 6-24m | 0.32 | 0.16 | 0.060 | 0.037 |
| 2-5y | 0.55 | 0.31 | 0.020 | 0.037 |
| 5+ | 0.00 | 0.04 | 0.005 | 0.037 |
Uganda Parameters
# Uganda-specific parameters
uganda_params <- list(
country_name = "Uganda",
geo_params = list(
Urban = list(
itn_coverage = 0.74,
vaccine_coverage = 0.78,
smc_coverage = 0.92,
eir_multiplier = 0.4,
cfr_multiplier = 1.0,
access_delay_days = 0.5,
care_seeking = 0.86,
malaria_positive = 0.51
),
Rural = list(
itn_coverage = 0.71,
vaccine_coverage = 0.78,
smc_coverage = 0.92,
eir_multiplier = 2.0,
cfr_multiplier = 1.6,
access_delay_days = 4.5,
care_seeking = 0.83,
malaria_positive = 0.54
)
),
age_params = data.frame(
Age_Group = c("0-6m", "6-24m", "2-5y", "5+"),
Population_Weight = c(0.14, 0.30, 0.56, 0.00),
Clinical_Risk = c(0.40, 0.57, 0.53, 0.10),
Severe_Risk = c(0.08, 0.06, 0.02, 0.005),
CFR_Base = c(0.027, 0.027, 0.027, 0.027)
),
base_params = list(
base_eir = 25,
smc_efficacy = 0.94,
vaccine_ve_infection = 0.36,
vaccine_ve_severe = 0.32,
itn_efficacy = 0.60,
cost_vaccine_full = 40.20,
cost_smc_per_child_per_year = 6.00,
cost_act = 1.20,
cost_outpatient = 8.00,
cost_hospital = 19.60,
cfr_base = 0.027,
dw_clinical = 0.006,
dw_severe = 0.133,
life_expectancy = 65,
discount_rate = 0.03
),
geo_pop_weights = c(Urban = 0.25, Rural = 0.75)
)
# Display Uganda parameters
kable(uganda_params$age_params, caption = "Uganda: Age-Specific Parameters",
digits = 3, format = "html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"))| Age_Group | Population_Weight | Clinical_Risk | Severe_Risk | CFR_Base |
|---|---|---|---|---|
| 0-6m | 0.14 | 0.40 | 0.080 | 0.027 |
| 6-24m | 0.30 | 0.57 | 0.060 | 0.027 |
| 2-5y | 0.56 | 0.53 | 0.020 | 0.027 |
| 5+ | 0.00 | 0.10 | 0.005 | 0.027 |
Burkina Faso Parameters
# Burkina Faso-specific parameters
burkina_params <- list(
country_name = "Burkina Faso",
geo_params = list(
Urban = list(
itn_coverage = 0.82,
vaccine_coverage = 0.65,
smc_coverage = 0.36,
eir_multiplier = 0.4,
cfr_multiplier = 1.0,
access_delay_days = 0.5,
care_seeking = 0.77,
malaria_positive = 0.35
),
Rural = list(
itn_coverage = 0.80,
vaccine_coverage = 0.55,
smc_coverage = 0.49,
eir_multiplier = 2.0,
cfr_multiplier = 1.6,
access_delay_days = 4.5,
care_seeking = 0.73,
malaria_positive = 0.40
)
),
age_params = data.frame(
Age_Group = c("0-6m", "6-24m", "2-5y", "5+"),
Population_Weight = c(0.13, 0.30, 0.57, 0.00),
Clinical_Risk = c(0.20, 0.35, 0.40, 0.06),
Severe_Risk = c(0.08, 0.06, 0.02, 0.005),
CFR_Base = c(0.013, 0.013, 0.013, 0.013)
),
base_params = list(
base_eir = 25,
smc_efficacy = 0.69,
vaccine_ve_infection = 0.39,
vaccine_ve_severe = 0.29,
itn_efficacy = 0.23,
cost_vaccine_full = 26.08,
cost_smc_per_child_per_year = 6.00,
cost_act = 1.50,
cost_outpatient = 8.00,
cost_hospital = 25.00,
cfr_base = 0.013,
dw_clinical = 0.006,
dw_severe = 0.133,
life_expectancy = 65,
discount_rate = 0.03
),
geo_pop_weights = c(Urban = 0.28, Rural = 0.72)
)
# Display Burkina Faso parameters
kable(burkina_params$age_params, caption = "Burkina Faso: Age-Specific Parameters",
digits = 3, format = "html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"))| Age_Group | Population_Weight | Clinical_Risk | Severe_Risk | CFR_Base |
|---|---|---|---|---|
| 0-6m | 0.13 | 0.20 | 0.080 | 0.013 |
| 6-24m | 0.30 | 0.35 | 0.060 | 0.013 |
| 2-5y | 0.57 | 0.40 | 0.020 | 0.013 |
| 5+ | 0.00 | 0.06 | 0.005 | 0.013 |
Part 2: Markov Model Implementation
# Main model function
run_markov_model <- function(params, intervention = "baseline",
time_horizon_years = 10, cohort_size = 10000) {
# Initialize accumulators
total_clinical <- 0
total_severe <- 0
total_deaths <- 0
total_dalys <- 0
total_treatment_cost <- 0
total_intervention_cost <- 0
results <- data.frame()
# Loop over geographic settings
for(geo in c("Urban", "Rural")) {
geo_pop <- cohort_size * params$geo_pop_weights[geo]
gp <- params$geo_params[[geo]]
bp <- params$base_params
# Calculate effective transmission
effective_eir <- bp$base_eir * gp$eir_multiplier
monthly_bites <- effective_eir / 12
monthly_infection_prob <- 1 - exp(-monthly_bites * 0.5)
# Apply ITN effect
itn_effect <- 1 - (bp$itn_efficacy * gp$itn_coverage)
monthly_infection_prob <- monthly_infection_prob * itn_effect
# Apply intervention-specific effects
if(intervention == "smc") {
smc_effect <- 1 - (bp$smc_efficacy * gp$smc_coverage)
monthly_infection_prob <- monthly_infection_prob * smc_effect
intervention_cost <- geo_pop * bp$cost_smc_per_child_per_year * time_horizon_years
} else if(intervention == "vaccine") {
vaccine_effect <- 1 - (bp$vaccine_ve_infection * gp$vaccine_coverage)
monthly_infection_prob <- monthly_infection_prob * vaccine_effect
intervention_cost <- geo_pop * bp$cost_vaccine_full * gp$vaccine_coverage
} else if(intervention == "combined") {
smc_effect <- 1 - (bp$smc_efficacy * gp$smc_coverage)
vaccine_effect <- 1 - (bp$vaccine_ve_infection * gp$vaccine_coverage)
monthly_infection_prob <- monthly_infection_prob * smc_effect * vaccine_effect
intervention_cost <- (geo_pop * bp$cost_smc_per_child_per_year * time_horizon_years) +
(geo_pop * bp$cost_vaccine_full * gp$vaccine_coverage)
} else {
intervention_cost <- 0
}
# Loop over age groups
for(age_idx in 1:nrow(params$age_params)) {
age_pop <- geo_pop * params$age_params$Population_Weight[age_idx]
if(age_pop == 0) next
clinical_risk <- params$age_params$Clinical_Risk[age_idx]
severe_risk <- params$age_params$Severe_Risk[age_idx]
cfr <- params$age_params$CFR_Base[age_idx] * gp$cfr_multiplier
susceptible <- age_pop
clinical_cases <- 0
severe_cases <- 0
deaths <- 0
discounted_treatment_cost <- 0
discounted_dalys <- 0
# Monthly simulation
for(month in 1:(time_horizon_years * 12)) {
discount_factor <- 1 / (1 + bp$discount_rate)^(month/12)
new_infections <- susceptible * monthly_infection_prob * clinical_risk
new_clinical <- new_infections
new_severe <- new_clinical * severe_risk
new_deaths <- new_severe * cfr
clinical_cases <- clinical_cases + new_clinical
severe_cases <- severe_cases + new_severe
deaths <- deaths + new_deaths
# Treatment costs (discounted)
treatment_cost <- (new_clinical * (bp$cost_outpatient + bp$cost_act) +
new_severe * (bp$cost_hospital + bp$cost_act)) * discount_factor
discounted_treatment_cost <- discounted_treatment_cost + treatment_cost
# DALYs (discounted)
yll <- new_deaths * bp$life_expectancy * discount_factor
yld <- (new_clinical * bp$dw_clinical * (14/365) +
new_severe * bp$dw_severe * (21/365)) * discount_factor
discounted_dalys <- discounted_dalys + yll + yld
# Update susceptible population
susceptible <- susceptible - new_infections
if(susceptible < age_pop * 0.1) susceptible <- age_pop * 0.1
}
# Accumulate totals
total_clinical <- total_clinical + clinical_cases
total_severe <- total_severe + severe_cases
total_deaths <- total_deaths + deaths
total_dalys <- total_dalys + discounted_dalys
total_treatment_cost <- total_treatment_cost + discounted_treatment_cost
# Store results
results <- rbind(results, data.frame(
geography = geo,
age_group = params$age_params$Age_Group[age_idx],
clinical_cases = clinical_cases,
severe_cases = severe_cases,
deaths = deaths,
dalys_discounted = discounted_dalys,
treatment_cost_discounted = discounted_treatment_cost,
stringsAsFactors = FALSE
))
}
total_intervention_cost <- total_intervention_cost + intervention_cost
}
total_costs <- total_treatment_cost + total_intervention_cost
return(list(
results = results,
total_clinical = total_clinical,
total_severe = total_severe,
total_deaths = total_deaths,
total_dalys = total_dalys,
total_costs = total_costs,
intervention_cost = total_intervention_cost,
treatment_cost = total_treatment_cost
))
}Model Results
Part 3: Run Model for All Countries
strategies <- c("baseline", "smc", "vaccine", "combined")
strategy_names <- c("Baseline (ITNs)", "SMC + ITNs", "Vaccine + ITNs", "Combined")
countries <- list(Kenya = kenya_params, Uganda = uganda_params, `Burkina Faso` = burkina_params)
# Store all results
all_results <- list()
comparison_table <- data.frame()
for(country_name in names(countries)) {
cat("\n")
cat("============================================================\n")
cat("RUNNING MODEL FOR:", country_name, "\n")
cat("============================================================\n\n")
params <- countries[[country_name]]
country_results <- list()
for(i in 1:length(strategies)) {
cat(sprintf(" Running %s...\n", strategy_names[i]))
result <- run_markov_model(
params = params,
intervention = strategies[i],
time_horizon_years = 10,
cohort_size = 10000
)
country_results[[i]] <- result
cat(sprintf(" Clinical cases: %s\n", format_number(result$total_clinical)))
cat(sprintf(" Severe cases: %s\n", format_number(result$total_severe)))
cat(sprintf(" Deaths: %s\n", format_number(result$total_deaths)))
cat(sprintf(" DALYs (discounted): %s\n", format_number(result$total_dalys)))
cat(sprintf(" Total costs (discounted): $%s\n", format_number(result$total_costs)))
cat("\n")
# Add to comparison table
comparison_table <- rbind(comparison_table, data.frame(
Country = country_name,
Strategy = strategy_names[i],
Clinical_Cases = result$total_clinical,
Severe_Cases = result$total_severe,
Deaths = result$total_deaths,
DALYs = result$total_dalys,
Costs = result$total_costs,
Intervention_Cost = result$intervention_cost,
Treatment_Cost = result$treatment_cost,
stringsAsFactors = FALSE
))
}
all_results[[country_name]] <- country_results
}##
## ============================================================
## RUNNING MODEL FOR: Kenya
## ============================================================
##
## Running Baseline (ITNs)...
## Clinical cases: 24,751
## Severe cases: 874
## Deaths: 48
## DALYs (discounted): 2,819
## Total costs (discounted): $242,418
##
## Running SMC + ITNs...
## Clinical cases: 15,648
## Severe cases: 570
## Deaths: 31
## DALYs (discounted): 1,848
## Total costs (discounted): $756,162
##
## Running Vaccine + ITNs...
## Clinical cases: 20,322
## Severe cases: 726
## Deaths: 40
## DALYs (discounted): 2,355
## Total costs (discounted): $451,456
##
## Running Combined...
## Clinical cases: 13,446
## Severe cases: 497
## Deaths: 27
## DALYs (discounted): 1,610
## Total costs (discounted): $985,729
##
##
## ============================================================
## RUNNING MODEL FOR: Uganda
## ============================================================
##
## Running Baseline (ITNs)...
## Clinical cases: 33,639
## Severe cases: 1,334
## Deaths: 55
## DALYs (discounted): 3,177
## Total costs (discounted): $300,459
##
## Running SMC + ITNs...
## Clinical cases: 10,255
## Severe cases: 410
## Deaths: 16
## DALYs (discounted): 986
## Total costs (discounted): $694,382
##
## Running Vaccine + ITNs...
## Clinical cases: 26,068
## Severe cases: 1,035
## Deaths: 42
## DALYs (discounted): 2,475
## Total costs (discounted): $548,097
##
## Running Combined...
## Clinical cases: 9,062
## Severe cases: 363
## Deaths: 15
## DALYs (discounted): 870
## Total costs (discounted): $996,568
##
##
## ============================================================
## RUNNING MODEL FOR: Burkina Faso
## ============================================================
##
## Running Baseline (ITNs)...
## Clinical cases: 32,451
## Severe cases: 1,195
## Deaths: 23
## DALYs (discounted): 1,368
## Total costs (discounted): $303,178
##
## Running SMC + ITNs...
## Clinical cases: 24,042
## Severe cases: 892
## Deaths: 17
## DALYs (discounted): 1,021
## Total costs (discounted): $826,775
##
## Running Vaccine + ITNs...
## Clinical cases: 26,793
## Severe cases: 991
## Deaths: 19
## DALYs (discounted): 1,139
## Total costs (discounted): $402,499
##
## Running Combined...
## Clinical cases: 20,223
## Severe cases: 754
## Deaths: 15
## DALYs (discounted): 867
## Total costs (discounted): $942,673Part 4: Cost-Effectiveness Results
# Calculate incremental metrics
ce_table <- comparison_table %>%
group_by(Country) %>%
mutate(
DALYs_Averted = first(DALYs) - DALYs,
Inc_Cost = Costs - first(Costs),
ICER = ifelse(DALYs_Averted > 0, Inc_Cost / DALYs_Averted, NA)
) %>%
ungroup()
# Format for display
display_table <- ce_table %>%
mutate(
Clinical_Cases = format_number(Clinical_Cases),
Severe_Cases = format_number(Severe_Cases),
Deaths = format_number(Deaths),
DALYs = format_number(DALYs),
Costs = paste0("$", format_number(Costs)),
DALYs_Averted = format_number(DALYs_Averted),
ICER = ifelse(is.na(ICER), "Baseline", paste0("$", format_number(ICER)))
) %>%
select(Country, Strategy, Clinical_Cases, Severe_Cases, Deaths, DALYs, Costs, ICER)
# Display results by country
for(country in unique(display_table$Country)) {
cat("\n")
cat("============================================================\n")
cat("COST-EFFECTIVENESS RESULTS:", country, "\n")
cat("============================================================\n\n")
country_table <- display_table %>% filter(Country == country)
kable(country_table, caption = paste(country, "- Cost-Effectiveness Results"),
format = "html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
print()
}##
## ============================================================
## COST-EFFECTIVENESS RESULTS: Kenya
## ============================================================
##
## <table class="table table-striped table-hover table-condensed" style="color: black; margin-left: auto; margin-right: auto;">
## <caption>Kenya - Cost-Effectiveness Results</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> Country </th>
## <th style="text-align:left;"> Strategy </th>
## <th style="text-align:left;"> Clinical_Cases </th>
## <th style="text-align:left;"> Severe_Cases </th>
## <th style="text-align:left;"> Deaths </th>
## <th style="text-align:left;"> DALYs </th>
## <th style="text-align:left;"> Costs </th>
## <th style="text-align:left;"> ICER </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> Kenya </td>
## <td style="text-align:left;"> Baseline (ITNs) </td>
## <td style="text-align:left;"> 24,751 </td>
## <td style="text-align:left;"> 874 </td>
## <td style="text-align:left;"> 48 </td>
## <td style="text-align:left;"> 2,819 </td>
## <td style="text-align:left;"> $242,418 </td>
## <td style="text-align:left;"> Baseline </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Kenya </td>
## <td style="text-align:left;"> SMC + ITNs </td>
## <td style="text-align:left;"> 15,648 </td>
## <td style="text-align:left;"> 570 </td>
## <td style="text-align:left;"> 31 </td>
## <td style="text-align:left;"> 1,848 </td>
## <td style="text-align:left;"> $756,162 </td>
## <td style="text-align:left;"> $ 529 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Kenya </td>
## <td style="text-align:left;"> Vaccine + ITNs </td>
## <td style="text-align:left;"> 20,322 </td>
## <td style="text-align:left;"> 726 </td>
## <td style="text-align:left;"> 40 </td>
## <td style="text-align:left;"> 2,355 </td>
## <td style="text-align:left;"> $451,456 </td>
## <td style="text-align:left;"> $ 450 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Kenya </td>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> 13,446 </td>
## <td style="text-align:left;"> 497 </td>
## <td style="text-align:left;"> 27 </td>
## <td style="text-align:left;"> 1,610 </td>
## <td style="text-align:left;"> $985,729 </td>
## <td style="text-align:left;"> $ 615 </td>
## </tr>
## </tbody>
## </table>
## ============================================================
## COST-EFFECTIVENESS RESULTS: Uganda
## ============================================================
##
## <table class="table table-striped table-hover table-condensed" style="color: black; margin-left: auto; margin-right: auto;">
## <caption>Uganda - Cost-Effectiveness Results</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> Country </th>
## <th style="text-align:left;"> Strategy </th>
## <th style="text-align:left;"> Clinical_Cases </th>
## <th style="text-align:left;"> Severe_Cases </th>
## <th style="text-align:left;"> Deaths </th>
## <th style="text-align:left;"> DALYs </th>
## <th style="text-align:left;"> Costs </th>
## <th style="text-align:left;"> ICER </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> Uganda </td>
## <td style="text-align:left;"> Baseline (ITNs) </td>
## <td style="text-align:left;"> 33,639 </td>
## <td style="text-align:left;"> 1,334 </td>
## <td style="text-align:left;"> 55 </td>
## <td style="text-align:left;"> 3,177 </td>
## <td style="text-align:left;"> $300,459 </td>
## <td style="text-align:left;"> Baseline </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Uganda </td>
## <td style="text-align:left;"> SMC + ITNs </td>
## <td style="text-align:left;"> 10,255 </td>
## <td style="text-align:left;"> 410 </td>
## <td style="text-align:left;"> 16 </td>
## <td style="text-align:left;"> 986 </td>
## <td style="text-align:left;"> $694,382 </td>
## <td style="text-align:left;"> $ 180 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Uganda </td>
## <td style="text-align:left;"> Vaccine + ITNs </td>
## <td style="text-align:left;"> 26,068 </td>
## <td style="text-align:left;"> 1,035 </td>
## <td style="text-align:left;"> 42 </td>
## <td style="text-align:left;"> 2,475 </td>
## <td style="text-align:left;"> $548,097 </td>
## <td style="text-align:left;"> $ 353 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Uganda </td>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> 9,062 </td>
## <td style="text-align:left;"> 363 </td>
## <td style="text-align:left;"> 15 </td>
## <td style="text-align:left;"> 870 </td>
## <td style="text-align:left;"> $996,568 </td>
## <td style="text-align:left;"> $ 302 </td>
## </tr>
## </tbody>
## </table>
## ============================================================
## COST-EFFECTIVENESS RESULTS: Burkina Faso
## ============================================================
##
## <table class="table table-striped table-hover table-condensed" style="color: black; margin-left: auto; margin-right: auto;">
## <caption>Burkina Faso - Cost-Effectiveness Results</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> Country </th>
## <th style="text-align:left;"> Strategy </th>
## <th style="text-align:left;"> Clinical_Cases </th>
## <th style="text-align:left;"> Severe_Cases </th>
## <th style="text-align:left;"> Deaths </th>
## <th style="text-align:left;"> DALYs </th>
## <th style="text-align:left;"> Costs </th>
## <th style="text-align:left;"> ICER </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> Burkina Faso </td>
## <td style="text-align:left;"> Baseline (ITNs) </td>
## <td style="text-align:left;"> 32,451 </td>
## <td style="text-align:left;"> 1,195 </td>
## <td style="text-align:left;"> 23 </td>
## <td style="text-align:left;"> 1,368 </td>
## <td style="text-align:left;"> $303,178 </td>
## <td style="text-align:left;"> Baseline </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Burkina Faso </td>
## <td style="text-align:left;"> SMC + ITNs </td>
## <td style="text-align:left;"> 24,042 </td>
## <td style="text-align:left;"> 892 </td>
## <td style="text-align:left;"> 17 </td>
## <td style="text-align:left;"> 1,021 </td>
## <td style="text-align:left;"> $826,775 </td>
## <td style="text-align:left;"> $1,511 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Burkina Faso </td>
## <td style="text-align:left;"> Vaccine + ITNs </td>
## <td style="text-align:left;"> 26,793 </td>
## <td style="text-align:left;"> 991 </td>
## <td style="text-align:left;"> 19 </td>
## <td style="text-align:left;"> 1,139 </td>
## <td style="text-align:left;"> $402,499 </td>
## <td style="text-align:left;"> $ 435 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Burkina Faso </td>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> 20,223 </td>
## <td style="text-align:left;"> 754 </td>
## <td style="text-align:left;"> 15 </td>
## <td style="text-align:left;"> 867 </td>
## <td style="text-align:left;"> $942,673 </td>
## <td style="text-align:left;"> $1,278 </td>
## </tr>
## </tbody>
## </table># WTP threshold interpretation
wtp_threshold <- 800
cat("\n\n## Interpretation (WTP threshold: $800 per DALY averted):\n\n")##
##
## ## Interpretation (WTP threshold: $800 per DALY averted):for(country in unique(ce_table$Country)) {
cat(sprintf("\n**%s:**\n", country))
country_data <- ce_table %>% filter(Country == country)
for(i in 2:nrow(country_data)) {
if(!is.na(country_data$ICER[i])) {
if(country_data$ICER[i] < wtp_threshold) {
cat(sprintf("- ✓ %s: Cost-effective (ICER = $%s)\n",
country_data$Strategy[i], format_number(country_data$ICER[i])))
} else {
cat(sprintf("- ✗ %s: Not cost-effective (ICER = $%s)\n",
country_data$Strategy[i], format_number(country_data$ICER[i])))
}
}
}
}##
## **Kenya:**
## - ✓ SMC + ITNs: Cost-effective (ICER = $529)
## - ✓ Vaccine + ITNs: Cost-effective (ICER = $450)
## - ✓ Combined: Cost-effective (ICER = $615)
##
## **Uganda:**
## - ✓ SMC + ITNs: Cost-effective (ICER = $180)
## - ✓ Vaccine + ITNs: Cost-effective (ICER = $353)
## - ✓ Combined: Cost-effective (ICER = $302)
##
## **Burkina Faso:**
## - ✗ SMC + ITNs: Not cost-effective (ICER = $1,511)
## - ✓ Vaccine + ITNs: Cost-effective (ICER = $435)
## - ✗ Combined: Not cost-effective (ICER = $1,278)Part 5: Geographic Subgroup Analysis
# Aggregate results by geography
geo_summary <- data.frame()
for(country_name in names(all_results)) {
country_results <- all_results[[country_name]]
for(i in 1:length(strategies)) {
geo_temp <- country_results[[i]]$results %>%
group_by(geography) %>%
summarize(
Clinical_Cases = sum(clinical_cases),
Severe_Cases = sum(severe_cases),
Deaths = sum(deaths),
DALYs_Discounted = sum(dalys_discounted),
Treatment_Costs_Discounted = sum(treatment_cost_discounted),
.groups = 'drop'
) %>%
mutate(Country = country_name, Strategy = strategy_names[i])
geo_summary <- rbind(geo_summary, geo_temp)
}
}
# Display geographic results
for(country in unique(geo_summary$Country)) {
cat("\n")
cat("============================================================\n")
cat(sprintf("GEOGRAPHIC SETTING: %s\n", toupper(country)))
cat("============================================================\n\n")
for(geo in c("Urban", "Rural")) {
cat(sprintf("\n### %s Settings\n", geo))
geo_subset <- geo_summary %>%
filter(Country == country, geography == geo)
geo_table <- geo_subset %>%
mutate(
Clinical_Cases = format_number(Clinical_Cases),
Severe_Cases = format_number(Severe_Cases),
Deaths = format_number(Deaths),
DALYs = format_number(DALYs_Discounted),
Costs = paste0("$", format_number(Treatment_Costs_Discounted))
) %>%
select(Strategy, Clinical_Cases, Severe_Cases, Deaths, DALYs, Costs)
kable(geo_table, caption = sprintf("%s - %s Outcomes", country, geo),
format = "html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
print()
}
}##
## ============================================================
## GEOGRAPHIC SETTING: KENYA
## ============================================================
##
##
## ### Urban Settings
## <table class="table table-striped table-hover table-condensed" style="color: black; margin-left: auto; margin-right: auto;">
## <caption>Kenya - Urban Outcomes</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> Strategy </th>
## <th style="text-align:left;"> Clinical_Cases </th>
## <th style="text-align:left;"> Severe_Cases </th>
## <th style="text-align:left;"> Deaths </th>
## <th style="text-align:left;"> DALYs </th>
## <th style="text-align:left;"> Costs </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> Baseline (ITNs) </td>
## <td style="text-align:left;"> 4,698 </td>
## <td style="text-align:left;"> 171 </td>
## <td style="text-align:left;"> 6 </td>
## <td style="text-align:left;"> 380 </td>
## <td style="text-align:left;"> $46,971 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> SMC + ITNs </td>
## <td style="text-align:left;"> 3,343 </td>
## <td style="text-align:left;"> 126 </td>
## <td style="text-align:left;"> 5 </td>
## <td style="text-align:left;"> 280 </td>
## <td style="text-align:left;"> $33,780 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Vaccine + ITNs </td>
## <td style="text-align:left;"> 3,891 </td>
## <td style="text-align:left;"> 144 </td>
## <td style="text-align:left;"> 5 </td>
## <td style="text-align:left;"> 321 </td>
## <td style="text-align:left;"> $39,199 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> 2,936 </td>
## <td style="text-align:left;"> 112 </td>
## <td style="text-align:left;"> 4 </td>
## <td style="text-align:left;"> 247 </td>
## <td style="text-align:left;"> $29,569 </td>
## </tr>
## </tbody>
## </table>
## ### Rural Settings
## <table class="table table-striped table-hover table-condensed" style="color: black; margin-left: auto; margin-right: auto;">
## <caption>Kenya - Rural Outcomes</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> Strategy </th>
## <th style="text-align:left;"> Clinical_Cases </th>
## <th style="text-align:left;"> Severe_Cases </th>
## <th style="text-align:left;"> Deaths </th>
## <th style="text-align:left;"> DALYs </th>
## <th style="text-align:left;"> Costs </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> Baseline (ITNs) </td>
## <td style="text-align:left;"> 20,053 </td>
## <td style="text-align:left;"> 703 </td>
## <td style="text-align:left;"> 42 </td>
## <td style="text-align:left;"> 2,440 </td>
## <td style="text-align:left;"> $195,447 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> SMC + ITNs </td>
## <td style="text-align:left;"> 12,305 </td>
## <td style="text-align:left;"> 444 </td>
## <td style="text-align:left;"> 26 </td>
## <td style="text-align:left;"> 1,568 </td>
## <td style="text-align:left;"> $122,382 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Vaccine + ITNs </td>
## <td style="text-align:left;"> 16,431 </td>
## <td style="text-align:left;"> 582 </td>
## <td style="text-align:left;"> 34 </td>
## <td style="text-align:left;"> 2,033 </td>
## <td style="text-align:left;"> $161,371 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> 10,510 </td>
## <td style="text-align:left;"> 384 </td>
## <td style="text-align:left;"> 23 </td>
## <td style="text-align:left;"> 1,363 </td>
## <td style="text-align:left;"> $105,272 </td>
## </tr>
## </tbody>
## </table>
## ============================================================
## GEOGRAPHIC SETTING: UGANDA
## ============================================================
##
##
## ### Urban Settings
## <table class="table table-striped table-hover table-condensed" style="color: black; margin-left: auto; margin-right: auto;">
## <caption>Uganda - Urban Outcomes</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> Strategy </th>
## <th style="text-align:left;"> Clinical_Cases </th>
## <th style="text-align:left;"> Severe_Cases </th>
## <th style="text-align:left;"> Deaths </th>
## <th style="text-align:left;"> DALYs </th>
## <th style="text-align:left;"> Costs </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> Baseline (ITNs) </td>
## <td style="text-align:left;"> 4,681 </td>
## <td style="text-align:left;"> 186 </td>
## <td style="text-align:left;"> 5 </td>
## <td style="text-align:left;"> 299 </td>
## <td style="text-align:left;"> $42,633 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> SMC + ITNs </td>
## <td style="text-align:left;"> 1,998 </td>
## <td style="text-align:left;"> 80 </td>
## <td style="text-align:left;"> 2 </td>
## <td style="text-align:left;"> 127 </td>
## <td style="text-align:left;"> $18,001 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Vaccine + ITNs </td>
## <td style="text-align:left;"> 3,837 </td>
## <td style="text-align:left;"> 153 </td>
## <td style="text-align:left;"> 4 </td>
## <td style="text-align:left;"> 248 </td>
## <td style="text-align:left;"> $35,208 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> 1,713 </td>
## <td style="text-align:left;"> 68 </td>
## <td style="text-align:left;"> 2 </td>
## <td style="text-align:left;"> 107 </td>
## <td style="text-align:left;"> $15,276 </td>
## </tr>
## </tbody>
## </table>
## ### Rural Settings
## <table class="table table-striped table-hover table-condensed" style="color: black; margin-left: auto; margin-right: auto;">
## <caption>Uganda - Rural Outcomes</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> Strategy </th>
## <th style="text-align:left;"> Clinical_Cases </th>
## <th style="text-align:left;"> Severe_Cases </th>
## <th style="text-align:left;"> Deaths </th>
## <th style="text-align:left;"> DALYs </th>
## <th style="text-align:left;"> Costs </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> Baseline (ITNs) </td>
## <td style="text-align:left;"> 28,959 </td>
## <td style="text-align:left;"> 1,148 </td>
## <td style="text-align:left;"> 50 </td>
## <td style="text-align:left;"> 2,877 </td>
## <td style="text-align:left;"> $257,826 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> SMC + ITNs </td>
## <td style="text-align:left;"> 8,257 </td>
## <td style="text-align:left;"> 331 </td>
## <td style="text-align:left;"> 14 </td>
## <td style="text-align:left;"> 860 </td>
## <td style="text-align:left;"> $ 76,381 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Vaccine + ITNs </td>
## <td style="text-align:left;"> 22,232 </td>
## <td style="text-align:left;"> 882 </td>
## <td style="text-align:left;"> 38 </td>
## <td style="text-align:left;"> 2,227 </td>
## <td style="text-align:left;"> $199,329 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> 7,349 </td>
## <td style="text-align:left;"> 295 </td>
## <td style="text-align:left;"> 13 </td>
## <td style="text-align:left;"> 763 </td>
## <td style="text-align:left;"> $ 67,732 </td>
## </tr>
## </tbody>
## </table>
## ============================================================
## GEOGRAPHIC SETTING: BURKINA FASO
## ============================================================
##
##
## ### Urban Settings
## <table class="table table-striped table-hover table-condensed" style="color: black; margin-left: auto; margin-right: auto;">
## <caption>Burkina Faso - Urban Outcomes</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> Strategy </th>
## <th style="text-align:left;"> Clinical_Cases </th>
## <th style="text-align:left;"> Severe_Cases </th>
## <th style="text-align:left;"> Deaths </th>
## <th style="text-align:left;"> DALYs </th>
## <th style="text-align:left;"> Costs </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> Baseline (ITNs) </td>
## <td style="text-align:left;"> 5,243 </td>
## <td style="text-align:left;"> 196 </td>
## <td style="text-align:left;"> 3 </td>
## <td style="text-align:left;"> 153 </td>
## <td style="text-align:left;"> $49,957 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> SMC + ITNs </td>
## <td style="text-align:left;"> 4,407 </td>
## <td style="text-align:left;"> 166 </td>
## <td style="text-align:left;"> 2 </td>
## <td style="text-align:left;"> 130 </td>
## <td style="text-align:left;"> $42,279 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Vaccine + ITNs </td>
## <td style="text-align:left;"> 4,389 </td>
## <td style="text-align:left;"> 165 </td>
## <td style="text-align:left;"> 2 </td>
## <td style="text-align:left;"> 130 </td>
## <td style="text-align:left;"> $42,120 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> 3,765 </td>
## <td style="text-align:left;"> 143 </td>
## <td style="text-align:left;"> 2 </td>
## <td style="text-align:left;"> 113 </td>
## <td style="text-align:left;"> $36,331 </td>
## </tr>
## </tbody>
## </table>
## ### Rural Settings
## <table class="table table-striped table-hover table-condensed" style="color: black; margin-left: auto; margin-right: auto;">
## <caption>Burkina Faso - Rural Outcomes</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> Strategy </th>
## <th style="text-align:left;"> Clinical_Cases </th>
## <th style="text-align:left;"> Severe_Cases </th>
## <th style="text-align:left;"> Deaths </th>
## <th style="text-align:left;"> DALYs </th>
## <th style="text-align:left;"> Costs </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> Baseline (ITNs) </td>
## <td style="text-align:left;"> 27,207 </td>
## <td style="text-align:left;"> 999 </td>
## <td style="text-align:left;"> 21 </td>
## <td style="text-align:left;"> 1,214 </td>
## <td style="text-align:left;"> $253,221 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> SMC + ITNs </td>
## <td style="text-align:left;"> 19,635 </td>
## <td style="text-align:left;"> 726 </td>
## <td style="text-align:left;"> 15 </td>
## <td style="text-align:left;"> 891 </td>
## <td style="text-align:left;"> $184,496 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Vaccine + ITNs </td>
## <td style="text-align:left;"> 22,403 </td>
## <td style="text-align:left;"> 826 </td>
## <td style="text-align:left;"> 17 </td>
## <td style="text-align:left;"> 1,009 </td>
## <td style="text-align:left;"> $209,636 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> 16,458 </td>
## <td style="text-align:left;"> 611 </td>
## <td style="text-align:left;"> 13 </td>
## <td style="text-align:left;"> 755 </td>
## <td style="text-align:left;"> $155,600 </td>
## </tr>
## </tbody>
## </table># Calculate geographic ICER matrix
geo_icer <- data.frame()
for(country_name in names(all_results)) {
params <- countries[[country_name]]
country_results <- all_results[[country_name]]
for(geo in c("Urban", "Rural")) {
# Get baseline results for this geography
baseline_results <- country_results[[1]]$results %>%
filter(geography == geo)
base_dalys <- sum(baseline_results$dalys_discounted)
base_cost <- sum(baseline_results$treatment_cost_discounted)
for(i in 2:length(strategies)) {
int_results <- country_results[[i]]$results %>%
filter(geography == geo)
int_dalys <- sum(int_results$dalys_discounted)
int_cost <- sum(int_results$treatment_cost_discounted)
# Get intervention cost for this geography
geo_weight <- params$geo_pop_weights[geo]
int_intervention_cost <- country_results[[i]]$intervention_cost * geo_weight
dalys_averted <- base_dalys - int_dalys
inc_cost <- (int_cost + int_intervention_cost) - base_cost
if(dalys_averted > 0) {
icer <- inc_cost / dalys_averted
} else {
icer <- NA
}
geo_icer <- rbind(geo_icer, data.frame(
Country = country_name,
Geography = geo,
Strategy = strategy_names[i],
DALYs_Averted = dalys_averted,
Inc_Cost = inc_cost,
ICER = icer
))
}
}
}
# Display geographic ICER matrix
cat("\n\n## Geographic ICER Matrix\n\n")##
##
## ## Geographic ICER Matrixgeo_icer_display <- geo_icer %>%
mutate(
ICER_Display = ifelse(is.na(ICER), "Dominated", paste0("$", format_number(ICER))),
DALYs_Averted = format_number(DALYs_Averted)
) %>%
select(Country, Geography, Strategy, DALYs_Averted, ICER_Display)
kable(geo_icer_display, caption = "Geographic ICER Matrix by Country",
format = "html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
print()## <table class="table table-striped table-hover table-condensed" style="color: black; margin-left: auto; margin-right: auto;">
## <caption>Geographic ICER Matrix by Country</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> </th>
## <th style="text-align:left;"> Country </th>
## <th style="text-align:left;"> Geography </th>
## <th style="text-align:left;"> Strategy </th>
## <th style="text-align:left;"> DALYs_Averted </th>
## <th style="text-align:left;"> ICER_Display </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> Urban </td>
## <td style="text-align:left;"> Kenya </td>
## <td style="text-align:left;"> Urban </td>
## <td style="text-align:left;"> SMC + ITNs </td>
## <td style="text-align:left;"> 100 </td>
## <td style="text-align:left;"> $1,673 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Urban1 </td>
## <td style="text-align:left;"> Kenya </td>
## <td style="text-align:left;"> Urban </td>
## <td style="text-align:left;"> Vaccine + ITNs </td>
## <td style="text-align:left;"> 59 </td>
## <td style="text-align:left;"> $1,153 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Urban2 </td>
## <td style="text-align:left;"> Kenya </td>
## <td style="text-align:left;"> Urban </td>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> 133 </td>
## <td style="text-align:left;"> $1,794 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Urban3 </td>
## <td style="text-align:left;"> Kenya </td>
## <td style="text-align:left;"> Rural </td>
## <td style="text-align:left;"> SMC + ITNs </td>
## <td style="text-align:left;"> 872 </td>
## <td style="text-align:left;"> $ 398 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Urban4 </td>
## <td style="text-align:left;"> Kenya </td>
## <td style="text-align:left;"> Rural </td>
## <td style="text-align:left;"> Vaccine + ITNs </td>
## <td style="text-align:left;"> 406 </td>
## <td style="text-align:left;"> $ 348 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Urban5 </td>
## <td style="text-align:left;"> Kenya </td>
## <td style="text-align:left;"> Rural </td>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> 1,077 </td>
## <td style="text-align:left;"> $ 469 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Urban6 </td>
## <td style="text-align:left;"> Uganda </td>
## <td style="text-align:left;"> Urban </td>
## <td style="text-align:left;"> SMC + ITNs </td>
## <td style="text-align:left;"> 173 </td>
## <td style="text-align:left;"> $ 725 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Urban7 </td>
## <td style="text-align:left;"> Uganda </td>
## <td style="text-align:left;"> Urban </td>
## <td style="text-align:left;"> Vaccine + ITNs </td>
## <td style="text-align:left;"> 52 </td>
## <td style="text-align:left;"> $1,371 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Urban8 </td>
## <td style="text-align:left;"> Uganda </td>
## <td style="text-align:left;"> Urban </td>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> 192 </td>
## <td style="text-align:left;"> $1,046 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Urban9 </td>
## <td style="text-align:left;"> Uganda </td>
## <td style="text-align:left;"> Rural </td>
## <td style="text-align:left;"> SMC + ITNs </td>
## <td style="text-align:left;"> 2,017 </td>
## <td style="text-align:left;"> $ 133 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Urban10 </td>
## <td style="text-align:left;"> Uganda </td>
## <td style="text-align:left;"> Rural </td>
## <td style="text-align:left;"> Vaccine + ITNs </td>
## <td style="text-align:left;"> 650 </td>
## <td style="text-align:left;"> $ 272 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Urban11 </td>
## <td style="text-align:left;"> Uganda </td>
## <td style="text-align:left;"> Rural </td>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> 2,114 </td>
## <td style="text-align:left;"> $ 234 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Urban12 </td>
## <td style="text-align:left;"> Burkina Faso </td>
## <td style="text-align:left;"> Urban </td>
## <td style="text-align:left;"> SMC + ITNs </td>
## <td style="text-align:left;"> 23 </td>
## <td style="text-align:left;"> $7,031 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Urban13 </td>
## <td style="text-align:left;"> Burkina Faso </td>
## <td style="text-align:left;"> Urban </td>
## <td style="text-align:left;"> Vaccine + ITNs </td>
## <td style="text-align:left;"> 23 </td>
## <td style="text-align:left;"> $1,477 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Urban14 </td>
## <td style="text-align:left;"> Burkina Faso </td>
## <td style="text-align:left;"> Urban </td>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> 41 </td>
## <td style="text-align:left;"> $4,854 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Urban15 </td>
## <td style="text-align:left;"> Burkina Faso </td>
## <td style="text-align:left;"> Rural </td>
## <td style="text-align:left;"> SMC + ITNs </td>
## <td style="text-align:left;"> 324 </td>
## <td style="text-align:left;"> $1,122 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Urban16 </td>
## <td style="text-align:left;"> Burkina Faso </td>
## <td style="text-align:left;"> Rural </td>
## <td style="text-align:left;"> Vaccine + ITNs </td>
## <td style="text-align:left;"> 205 </td>
## <td style="text-align:left;"> $ 316 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Urban17 </td>
## <td style="text-align:left;"> Burkina Faso </td>
## <td style="text-align:left;"> Rural </td>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> 460 </td>
## <td style="text-align:left;"> $ 963 </td>
## </tr>
## </tbody>
## </table>Part 6: Probabilistic Sensitivity Analysis (PSA)
# Function to run PSA for a country
run_psa <- function(params, n_simulations = 1000) {
# Initialize results
psa_results <- data.frame(
sim_id = 1:n_simulations,
icer_smc = NA,
icer_vaccine = NA,
icer_combined = NA
)
# Progress bar
pb <- txtProgressBar(min = 0, max = n_simulations, style = 3)
for(sim in 1:n_simulations) {
# Sample parameters from uncertainty distributions
eir_sample <- rlnorm(1, meanlog = log(params$base_params$base_eir), sdlog = 0.25)
ve_infection_sample <- rbeta(1, shape1 = 35, shape2 = 82)
ve_severe_sample <- rbeta(1, shape1 = 75, shape2 = 32)
itn_efficacy_sample <- rbeta(1, shape1 = 25, shape2 = 75)
smc_efficacy_sample <- rbeta(1, shape1 = 70, shape2 = 30)
# Create temporary params with sampled values
temp_params <- params
temp_params$base_params$base_eir <- eir_sample
temp_params$base_params$vaccine_ve_infection <- ve_infection_sample
temp_params$base_params$vaccine_ve_severe <- ve_severe_sample
temp_params$base_params$itn_efficacy <- itn_efficacy_sample
temp_params$base_params$smc_efficacy <- smc_efficacy_sample
# Run model for each strategy
baseline_result <- run_markov_model(temp_params, "baseline")
smc_result <- run_markov_model(temp_params, "smc")
vaccine_result <- run_markov_model(temp_params, "vaccine")
combined_result <- run_markov_model(temp_params, "combined")
# Calculate ICERs
dalys_smc <- baseline_result$total_dalys - smc_result$total_dalys
cost_smc <- smc_result$total_costs - baseline_result$total_costs
psa_results$icer_smc[sim] <- ifelse(dalys_smc > 0, cost_smc / dalys_smc, NA)
dalys_vaccine <- baseline_result$total_dalys - vaccine_result$total_dalys
cost_vaccine <- vaccine_result$total_costs - baseline_result$total_costs
psa_results$icer_vaccine[sim] <- ifelse(dalys_vaccine > 0, cost_vaccine / dalys_vaccine, NA)
dalys_combined <- baseline_result$total_dalys - combined_result$total_dalys
cost_combined <- combined_result$total_costs - baseline_result$total_costs
psa_results$icer_combined[sim] <- ifelse(dalys_combined > 0, cost_combined / dalys_combined, NA)
setTxtProgressBar(pb, sim)
}
close(pb)
# Calculate summary statistics
psa_summary <- data.frame(
Strategy = c("SMC", "Vaccine", "Combined"),
ICER_Mean = c(mean(psa_results$icer_smc, na.rm = TRUE),
mean(psa_results$icer_vaccine, na.rm = TRUE),
mean(psa_results$icer_combined, na.rm = TRUE)),
ICER_Median = c(median(psa_results$icer_smc, na.rm = TRUE),
median(psa_results$icer_vaccine, na.rm = TRUE),
median(psa_results$icer_combined, na.rm = TRUE)),
ICER_Lower95 = c(quantile(psa_results$icer_smc, 0.025, na.rm = TRUE),
quantile(psa_results$icer_vaccine, 0.025, na.rm = TRUE),
quantile(psa_results$icer_combined, 0.025, na.rm = TRUE)),
ICER_Upper95 = c(quantile(psa_results$icer_smc, 0.975, na.rm = TRUE),
quantile(psa_results$icer_vaccine, 0.975, na.rm = TRUE),
quantile(psa_results$icer_combined, 0.975, na.rm = TRUE)),
Prob_CE_800 = c(mean(psa_results$icer_smc <= 800, na.rm = TRUE),
mean(psa_results$icer_vaccine <= 800, na.rm = TRUE),
mean(psa_results$icer_combined <= 800, na.rm = TRUE))
)
return(list(results = psa_results, summary = psa_summary))
}
# Run PSA for each country
cat("\n\n## Probabilistic Sensitivity Analysis Results\n\n")##
##
## ## Probabilistic Sensitivity Analysis Resultspsa_summaries <- list()
for(country_name in names(countries)) {
cat(sprintf("\n### %s\n", country_name))
cat("\nRunning PSA with 500 simulations...\n")
psa_result <- run_psa(countries[[country_name]], n_simulations = 500)
psa_summaries[[country_name]] <- psa_result
# Display summary
psa_summary_display <- psa_result$summary %>%
mutate(
ICER_Mean = paste0("$", format_number(ICER_Mean)),
ICER_Median = paste0("$", format_number(ICER_Median)),
ICER_Lower95 = paste0("$", format_number(ICER_Lower95)),
ICER_Upper95 = paste0("$", format_number(ICER_Upper95)),
Prob_CE_800 = paste0(round(Prob_CE_800 * 100, 1), "%")
)
kable(psa_summary_display, caption = paste(country_name, "- PSA Summary"),
format = "html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
print()
}##
## ### Kenya
##
## Running PSA with 500 simulations...
## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | |===================== | 31% | |====================== | 31% | 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## <table class="table table-striped table-hover table-condensed" style="color: black; margin-left: auto; margin-right: auto;">
## <caption>Kenya - PSA Summary</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> Strategy </th>
## <th style="text-align:left;"> ICER_Mean </th>
## <th style="text-align:left;"> ICER_Median </th>
## <th style="text-align:left;"> ICER_Lower95 </th>
## <th style="text-align:left;"> ICER_Upper95 </th>
## <th style="text-align:left;"> Prob_CE_800 </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> SMC </td>
## <td style="text-align:left;"> $545 </td>
## <td style="text-align:left;"> $536 </td>
## <td style="text-align:left;"> $433 </td>
## <td style="text-align:left;"> $696 </td>
## <td style="text-align:left;"> 99.8% </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Vaccine </td>
## <td style="text-align:left;"> $615 </td>
## <td style="text-align:left;"> $598 </td>
## <td style="text-align:left;"> $422 </td>
## <td style="text-align:left;"> $887 </td>
## <td style="text-align:left;"> 91% </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> $660 </td>
## <td style="text-align:left;"> $651 </td>
## <td style="text-align:left;"> $533 </td>
## <td style="text-align:left;"> $822 </td>
## <td style="text-align:left;"> 95.4% </td>
## </tr>
## </tbody>
## </table>
## ### Uganda
##
## Running PSA with 500 simulations...
## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | |===================== | 31% | |====================== | 31% | 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## <table class="table table-striped table-hover table-condensed" style="color: black; margin-left: auto; margin-right: auto;">
## <caption>Uganda - PSA Summary</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> Strategy </th>
## <th style="text-align:left;"> ICER_Mean </th>
## <th style="text-align:left;"> ICER_Median </th>
## <th style="text-align:left;"> ICER_Lower95 </th>
## <th style="text-align:left;"> ICER_Upper95 </th>
## <th style="text-align:left;"> Prob_CE_800 </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> SMC </td>
## <td style="text-align:left;"> $173 </td>
## <td style="text-align:left;"> $171 </td>
## <td style="text-align:left;"> $115 </td>
## <td style="text-align:left;"> $244 </td>
## <td style="text-align:left;"> 100% </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Vaccine </td>
## <td style="text-align:left;"> $298 </td>
## <td style="text-align:left;"> $286 </td>
## <td style="text-align:left;"> $189 </td>
## <td style="text-align:left;"> $455 </td>
## <td style="text-align:left;"> 100% </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> $265 </td>
## <td style="text-align:left;"> $261 </td>
## <td style="text-align:left;"> $199 </td>
## <td style="text-align:left;"> $352 </td>
## <td style="text-align:left;"> 100% </td>
## </tr>
## </tbody>
## </table>
## ### Burkina Faso
##
## Running PSA with 500 simulations...
## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | |===================== | 31% | |====================== | 31% | 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92% | |================================================================= | 93% | |================================================================== | 94% | |================================================================== | 95% | |=================================================================== | 95% | |=================================================================== | 96% | |==================================================================== | 97% | |==================================================================== | 98% | |===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%
## <table class="table table-striped table-hover table-condensed" style="color: black; margin-left: auto; margin-right: auto;">
## <caption>Burkina Faso - PSA Summary</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> Strategy </th>
## <th style="text-align:left;"> ICER_Mean </th>
## <th style="text-align:left;"> ICER_Median </th>
## <th style="text-align:left;"> ICER_Lower95 </th>
## <th style="text-align:left;"> ICER_Upper95 </th>
## <th style="text-align:left;"> Prob_CE_800 </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> SMC </td>
## <td style="text-align:left;"> $1,552 </td>
## <td style="text-align:left;"> $1,535 </td>
## <td style="text-align:left;"> $1,213 </td>
## <td style="text-align:left;"> $2,048 </td>
## <td style="text-align:left;"> 0% </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Vaccine </td>
## <td style="text-align:left;"> $ 686 </td>
## <td style="text-align:left;"> $ 663 </td>
## <td style="text-align:left;"> $ 424 </td>
## <td style="text-align:left;"> $1,121 </td>
## <td style="text-align:left;"> 80.4% </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> $1,440 </td>
## <td style="text-align:left;"> $1,413 </td>
## <td style="text-align:left;"> $1,149 </td>
## <td style="text-align:left;"> $1,901 </td>
## <td style="text-align:left;"> 0% </td>
## </tr>
## </tbody>
## </table>Part 7: Cost-Effectiveness Acceptability Curves
# Create CEAC data for all countries
ceac_data <- data.frame()
for(country_name in names(psa_summaries)) {
psa_results <- psa_summaries[[country_name]]$results
wtp_thresholds <- seq(0, 2000, 10)
for(wtp in wtp_thresholds) {
ceac_data <- rbind(ceac_data, data.frame(
Country = country_name,
WTP = wtp,
SMC = mean(psa_results$icer_smc <= wtp, na.rm = TRUE),
Vaccine = mean(psa_results$icer_vaccine <= wtp, na.rm = TRUE),
Combined = mean(psa_results$icer_combined <= wtp, na.rm = TRUE)
))
}
}
# Melt for plotting
ceac_melted <- ceac_data %>%
pivot_longer(cols = c(SMC, Vaccine, Combined),
names_to = "Strategy", values_to = "Probability")
# Create CEAC plot
p_ceac <- ggplot(ceac_melted, aes(x = WTP, y = Probability, color = Strategy, group = Strategy)) +
geom_line(size = 1.2) +
facet_wrap(~Country, ncol = 1, scales = "free_y") +
geom_vline(xintercept = 800, linetype = "dashed", color = "red", size = 0.8) +
annotate("text", x = 800, y = 0.95, label = "WTP = $800", size = 3, angle = 90, hjust = -0.2) +
labs(title = "Cost-Effectiveness Acceptability Curves by Country",
subtitle = "Probability of being cost-effective at different willingness-to-pay thresholds",
x = "Willingness-to-pay threshold ($ per DALY averted)",
y = "Probability of cost-effectiveness") +
theme_minimal() +
scale_color_manual(values = c("SMC" = "#377EB8", "Vaccine" = "#4DAF4A", "Combined" = "#E41A1C")) +
scale_x_continuous(labels = comma, limits = c(0, 2000)) +
scale_y_continuous(labels = percent, limits = c(0, 1)) +
theme(legend.position = "bottom",
plot.title = element_text(size = 14, face = "bold"))
print(p_ceac)
Part 8: Budget Impact Analysis
# Function to calculate budget impact
calculate_budget_impact <- function(params, bia_horizon_years = 5,
national_u5_population = 5000000) {
# Scale cohort results to national population
scaling_factor <- national_u5_population / 10000
# Get discounted costs from model (10-year)
strategies_list <- c("baseline", "smc", "vaccine", "combined")
total_costs_10yr <- c()
for(i in 1:length(strategies_list)) {
result <- run_markov_model(params, strategies_list[i],
time_horizon_years = 10, cohort_size = 10000)
total_costs_10yr[i] <- result$total_costs
}
# Annualize (simple linear approximation)
annual_costs <- total_costs_10yr / 10 * scaling_factor
# Calculate 5-year costs (discounted)
discount_rate <- params$base_params$discount_rate
five_year_discounted <- sapply(annual_costs, function(cost) {
sum(cost / (1 + discount_rate)^(0:(bia_horizon_years-1)))
})
# Create results table
bia_results <- data.frame(
Strategy = c("Baseline (ITNs)", "SMC + ITNs", "Vaccine + ITNs", "Combined"),
Annual_Cost_M = annual_costs / 1e6,
Five_Year_Cost_M = five_year_discounted / 1e6,
Incremental_5yr_M = (five_year_discounted - five_year_discounted[1]) / 1e6
)
return(bia_results)
}
# Calculate budget impact for each country
cat("\n\n## Budget Impact Analysis (5-year horizon, National Scale)\n\n")##
##
## ## Budget Impact Analysis (5-year horizon, National Scale)## Assumption: 5 million children under 5 nationallybia_all <- data.frame()
for(country_name in names(countries)) {
cat(sprintf("\n### %s\n", country_name))
bia_results <- calculate_budget_impact(countries[[country_name]],
bia_horizon_years = 5,
national_u5_population = 5000000)
bia_results$Country <- country_name
bia_all <- rbind(bia_all, bia_results)
# Display
bia_display <- bia_results %>%
mutate(
Annual_Cost_M = paste0("$", format_number(Annual_Cost_M), "M"),
Five_Year_Cost_M = paste0("$", format_number(Five_Year_Cost_M), "M"),
Incremental_5yr_M = paste0("$", format_number(Incremental_5yr_M), "M")
) %>%
select(Strategy, Annual_Cost_M, Five_Year_Cost_M, Incremental_5yr_M)
kable(bia_display, caption = paste(country_name, "- Budget Impact (5-year)"),
format = "html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
print()
# Financial sustainability indicators
cat("\n**Financial Sustainability Indicators:**\n")
cat(sprintf("- Annual health budget (assumed): $500M\n"))
cat(sprintf("- Vaccine strategy share of budget: %.2f%%\n",
bia_results$Annual_Cost_M[3] / 500 * 100))
cat(sprintf("- Combined strategy share of budget: %.2f%%\n",
bia_results$Annual_Cost_M[4] / 500 * 100))
}##
## ### Kenya
## <table class="table table-striped table-hover table-condensed" style="color: black; margin-left: auto; margin-right: auto;">
## <caption>Kenya - Budget Impact (5-year)</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> Strategy </th>
## <th style="text-align:left;"> Annual_Cost_M </th>
## <th style="text-align:left;"> Five_Year_Cost_M </th>
## <th style="text-align:left;"> Incremental_5yr_M </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> Baseline (ITNs) </td>
## <td style="text-align:left;"> $12M </td>
## <td style="text-align:left;"> $ 57M </td>
## <td style="text-align:left;"> $ 0M </td>
## </tr>
## <tr>
## <td style="text-align:left;"> SMC + ITNs </td>
## <td style="text-align:left;"> $38M </td>
## <td style="text-align:left;"> $178M </td>
## <td style="text-align:left;"> $121M </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Vaccine + ITNs </td>
## <td style="text-align:left;"> $23M </td>
## <td style="text-align:left;"> $106M </td>
## <td style="text-align:left;"> $ 49M </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> $49M </td>
## <td style="text-align:left;"> $232M </td>
## <td style="text-align:left;"> $175M </td>
## </tr>
## </tbody>
## </table>
## **Financial Sustainability Indicators:**
## - Annual health budget (assumed): $500M
## - Vaccine strategy share of budget: 4.51%
## - Combined strategy share of budget: 9.86%
##
## ### Uganda
## <table class="table table-striped table-hover table-condensed" style="color: black; margin-left: auto; margin-right: auto;">
## <caption>Uganda - Budget Impact (5-year)</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> Strategy </th>
## <th style="text-align:left;"> Annual_Cost_M </th>
## <th style="text-align:left;"> Five_Year_Cost_M </th>
## <th style="text-align:left;"> Incremental_5yr_M </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> Baseline (ITNs) </td>
## <td style="text-align:left;"> $15M </td>
## <td style="text-align:left;"> $ 71M </td>
## <td style="text-align:left;"> $ 0M </td>
## </tr>
## <tr>
## <td style="text-align:left;"> SMC + ITNs </td>
## <td style="text-align:left;"> $35M </td>
## <td style="text-align:left;"> $164M </td>
## <td style="text-align:left;"> $ 93M </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Vaccine + ITNs </td>
## <td style="text-align:left;"> $27M </td>
## <td style="text-align:left;"> $129M </td>
## <td style="text-align:left;"> $ 58M </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> $50M </td>
## <td style="text-align:left;"> $235M </td>
## <td style="text-align:left;"> $164M </td>
## </tr>
## </tbody>
## </table>
## **Financial Sustainability Indicators:**
## - Annual health budget (assumed): $500M
## - Vaccine strategy share of budget: 5.48%
## - Combined strategy share of budget: 9.97%
##
## ### Burkina Faso
## <table class="table table-striped table-hover table-condensed" style="color: black; margin-left: auto; margin-right: auto;">
## <caption>Burkina Faso - Budget Impact (5-year)</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> Strategy </th>
## <th style="text-align:left;"> Annual_Cost_M </th>
## <th style="text-align:left;"> Five_Year_Cost_M </th>
## <th style="text-align:left;"> Incremental_5yr_M </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> Baseline (ITNs) </td>
## <td style="text-align:left;"> $15M </td>
## <td style="text-align:left;"> $ 72M </td>
## <td style="text-align:left;"> $ 0M </td>
## </tr>
## <tr>
## <td style="text-align:left;"> SMC + ITNs </td>
## <td style="text-align:left;"> $41M </td>
## <td style="text-align:left;"> $195M </td>
## <td style="text-align:left;"> $123M </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Vaccine + ITNs </td>
## <td style="text-align:left;"> $20M </td>
## <td style="text-align:left;"> $ 95M </td>
## <td style="text-align:left;"> $ 23M </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> $47M </td>
## <td style="text-align:left;"> $222M </td>
## <td style="text-align:left;"> $151M </td>
## </tr>
## </tbody>
## </table>
## **Financial Sustainability Indicators:**
## - Annual health budget (assumed): $500M
## - Vaccine strategy share of budget: 4.02%
## - Combined strategy share of budget: 9.43%# Budget impact plot
p_budget <- ggplot(bia_all, aes(x = Country, y = Five_Year_Cost_M, fill = Strategy)) +
geom_bar(stat = "identity", position = "dodge", width = 0.7) +
geom_text(aes(label = paste0("$", format_number(Five_Year_Cost_M), "M")),
position = position_dodge(0.7), vjust = -0.5, size = 3) +
labs(title = "5-Year Budget Requirements by Country",
subtitle = "Discounted at 3% annually | National scale: 5 million children",
x = "", y = "Total Cost (Millions USD)") +
theme_minimal() +
scale_fill_manual(values = c("Baseline (ITNs)" = "gray50",
"Vaccine + ITNs" = "#4DAF4A",
"SMC + ITNs" = "#377EB8",
"Combined" = "#E41A1C")) +
theme(legend.position = "bottom",
plot.title = element_text(size = 14, face = "bold"))
print(p_budget)
Part 9: Wealth Quintile Equity Analysis
# Define wealth quintile parameters
wealth_params <- data.frame(
Quintile = c("Q1 (Poorest)", "Q2", "Q3", "Q4", "Q5 (Richest)"),
Population_Share = rep(0.20, 5),
itn_coverage = c(0.30, 0.45, 0.55, 0.65, 0.75),
vaccine_coverage = c(0.25, 0.40, 0.55, 0.65, 0.75),
smc_coverage = c(0.35, 0.45, 0.55, 0.65, 0.70),
eir_multiplier = c(2.5, 1.8, 1.2, 0.8, 0.5),
cfr_multiplier = c(2.0, 1.5, 1.2, 0.9, 0.7),
access_delay_days = c(7, 5, 3, 2, 1),
treatment_seeking = c(0.40, 0.55, 0.65, 0.75, 0.85),
malnutrition_rate = c(0.35, 0.25, 0.18, 0.12, 0.08)
)
# Function to run wealth-stratified analysis
run_wealth_analysis <- function(params, intervention = "vaccine",
time_horizon_years = 10, cohort_size = 10000) {
results <- data.frame()
total_dalys <- 0
total_costs <- 0
for(q in 1:nrow(wealth_params)) {
quintile <- wealth_params$Quintile[q]
quintile_pop <- cohort_size * wealth_params$Population_Share[q]
# Create modified params for this quintile
quintile_params <- params
quintile_params$geo_params$Urban$itn_coverage <- wealth_params$itn_coverage[q]
quintile_params$geo_params$Rural$itn_coverage <- wealth_params$itn_coverage[q]
quintile_params$geo_params$Urban$vaccine_coverage <- wealth_params$vaccine_coverage[q]
quintile_params$geo_params$Rural$vaccine_coverage <- wealth_params$vaccine_coverage[q]
quintile_params$geo_params$Urban$smc_coverage <- wealth_params$smc_coverage[q]
quintile_params$geo_params$Rural$smc_coverage <- wealth_params$smc_coverage[q]
# Adjust EIR and CFR
quintile_params$geo_params$Urban$eir_multiplier <- quintile_params$geo_params$Urban$eir_multiplier *
wealth_params$eir_multiplier[q]
quintile_params$geo_params$Rural$eir_multiplier <- quintile_params$geo_params$Rural$eir_multiplier *
wealth_params$eir_multiplier[q]
for(age_idx in 1:nrow(quintile_params$age_params)) {
quintile_params$age_params$CFR_Base[age_idx] <- quintile_params$age_params$CFR_Base[age_idx] *
wealth_params$cfr_multiplier[q]
}
# Run model for this quintile
if(intervention == "baseline") {
result <- run_markov_model(quintile_params, "baseline",
time_horizon_years, quintile_pop)
} else if(intervention == "smc") {
result <- run_markov_model(quintile_params, "smc",
time_horizon_years, quintile_pop)
} else if(intervention == "vaccine") {
result <- run_markov_model(quintile_params, "vaccine",
time_horizon_years, quintile_pop)
} else {
result <- run_markov_model(quintile_params, "combined",
time_horizon_years, quintile_pop)
}
results <- rbind(results, data.frame(
Quintile = quintile,
Quintile_Num = q,
DALYs = result$total_dalys,
Costs = result$total_costs,
Intervention_Cost = result$intervention_cost,
Treatment_Cost = result$treatment_cost
))
total_dalys <- total_dalys + result$total_dalys
total_costs <- total_costs + result$total_costs
}
return(list(results = results, total_dalys = total_dalys, total_costs = total_costs))
}
# Run wealth analysis for Kenya (as example)
cat("\n\n## Wealth Quintile Stratification Analysis (Kenya - Vaccine Strategy)\n\n")##
##
## ## Wealth Quintile Stratification Analysis (Kenya - Vaccine Strategy)# Baseline wealth results
baseline_wealth <- run_wealth_analysis(kenya_params, "baseline")
vaccine_wealth <- run_wealth_analysis(kenya_params, "vaccine")
# Calculate quintile-specific results
wealth_results <- baseline_wealth$results %>%
rename(Baseline_DALYs = DALYs, Baseline_Costs = Costs) %>%
select(Quintile, Quintile_Num, Baseline_DALYs, Baseline_Costs) %>%
left_join(
vaccine_wealth$results %>%
rename(Vaccine_DALYs = DALYs, Vaccine_Costs = Costs) %>%
select(Quintile, Quintile_Num, Vaccine_DALYs, Vaccine_Costs),
by = c("Quintile", "Quintile_Num")
) %>%
mutate(
DALYs_Averted = Baseline_DALYs - Vaccine_DALYs,
Inc_Cost = (Vaccine_Costs + vaccine_wealth$results$Intervention_Cost) - Baseline_Costs,
ICER = ifelse(DALYs_Averted > 0, Inc_Cost / DALYs_Averted, NA)
)
# Display wealth equity results
wealth_display <- wealth_results %>%
mutate(
DALYs_Averted = format_number(DALYs_Averted),
ICER = ifelse(is.na(ICER), "N/A", paste0("$", format_number(ICER)))
) %>%
select(Quintile, DALYs_Averted, ICER)
kable(wealth_display, caption = "Wealth Quintile Equity Results (Kenya - Vaccine + ITNs)",
format = "html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
print()## <table class="table table-striped table-hover table-condensed" style="color: black; margin-left: auto; margin-right: auto;">
## <caption>Wealth Quintile Equity Results (Kenya - Vaccine + ITNs)</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> Quintile </th>
## <th style="text-align:left;"> DALYs_Averted </th>
## <th style="text-align:left;"> ICER </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> Q1 (Poorest) </td>
## <td style="text-align:left;"> 94 </td>
## <td style="text-align:left;"> $ 352 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Q2 </td>
## <td style="text-align:left;"> 104 </td>
## <td style="text-align:left;"> $ 516 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Q3 </td>
## <td style="text-align:left;"> 102 </td>
## <td style="text-align:left;"> $ 734 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Q4 </td>
## <td style="text-align:left;"> 76 </td>
## <td style="text-align:left;"> $1,176 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Q5 (Richest) </td>
## <td style="text-align:left;"> 53 </td>
## <td style="text-align:left;"> $1,995 </td>
## </tr>
## </tbody>
## </table># Calculate concentration index
calculate_concentration_index <- function(health_gains, wealth_rank) {
order_idx <- order(wealth_rank)
health_gains_sorted <- health_gains[order_idx]
pop_shares <- rep(0.2, 5)
cum_pop <- cumsum(pop_shares)
cum_health <- cumsum(health_gains_sorted / sum(health_gains_sorted))
area <- 0
for(i in 1:5) {
area <- area + (cum_pop[i] - ifelse(i==1, 0, cum_pop[i-1])) *
(cum_health[i] + ifelse(i==1, 0, cum_health[i-1])) / 2
}
ci <- 2 * area - 1
return(ci)
}
ci_value <- calculate_concentration_index(wealth_results$DALYs_Averted,
wealth_results$Quintile_Num)
cat("\n**Equity Metrics:**\n")##
## **Equity Metrics:**## - Concentration Index: 0.102if(ci_value > 0.1) {
cat("- Interpretation: Strongly pro-rich distribution\n")
cat("- Recommendation: Increase targeted outreach to poorest quintiles\n")
} else if(ci_value > 0) {
cat("- Interpretation: Mildly pro-rich distribution\n")
cat("- Recommendation: Strengthen equity-focused implementation\n")
} else if(ci_value < -0.1) {
cat("- Interpretation: Strongly pro-poor distribution (desirable)\n")
cat("- Recommendation: Maintain current equity-focused approaches\n")
} else {
cat("- Interpretation: Equitable distribution\n")
cat("- Recommendation: Monitor equity indicators continuously\n")
}## - Interpretation: Strongly pro-rich distribution
## - Recommendation: Increase targeted outreach to poorest quintiles##
## **Coverage Gaps by Wealth Quintile:**coverage_gaps <- wealth_params %>%
mutate(
ITN_Gap = max(itn_coverage) - itn_coverage,
Vaccine_Gap = max(vaccine_coverage) - vaccine_coverage,
SMC_Gap = max(smc_coverage) - smc_coverage
) %>%
select(Quintile, ITN_Gap, Vaccine_Gap, SMC_Gap)
kable(coverage_gaps, caption = "Coverage Gaps Relative to Richest Quintile",
format = "html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
print()## <table class="table table-striped table-hover table-condensed" style="color: black; margin-left: auto; margin-right: auto;">
## <caption>Coverage Gaps Relative to Richest Quintile</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> Quintile </th>
## <th style="text-align:right;"> ITN_Gap </th>
## <th style="text-align:right;"> Vaccine_Gap </th>
## <th style="text-align:right;"> SMC_Gap </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> Q1 (Poorest) </td>
## <td style="text-align:right;"> 0.45 </td>
## <td style="text-align:right;"> 0.50 </td>
## <td style="text-align:right;"> 0.35 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Q2 </td>
## <td style="text-align:right;"> 0.30 </td>
## <td style="text-align:right;"> 0.35 </td>
## <td style="text-align:right;"> 0.25 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Q3 </td>
## <td style="text-align:right;"> 0.20 </td>
## <td style="text-align:right;"> 0.20 </td>
## <td style="text-align:right;"> 0.15 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Q4 </td>
## <td style="text-align:right;"> 0.10 </td>
## <td style="text-align:right;"> 0.10 </td>
## <td style="text-align:right;"> 0.05 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Q5 (Richest) </td>
## <td style="text-align:right;"> 0.00 </td>
## <td style="text-align:right;"> 0.00 </td>
## <td style="text-align:right;"> 0.00 </td>
## </tr>
## </tbody>
## </table>Part 10: Visualizations DALYs Averted by Strategy and Country
p_dalys <- ce_table %>%
filter(Strategy != "Baseline (ITNs)") %>%
mutate(Strategy = factor(Strategy, levels = c("Vaccine + ITNs", "SMC + ITNs", "Combined"))) %>%
ggplot(aes(x = Country, y = DALYs_Averted, fill = Strategy)) +
geom_bar(stat = "identity", position = "dodge", width = 0.7) +
geom_text(aes(label = format_number(DALYs_Averted)),
position = position_dodge(0.7), vjust = -0.5, size = 3.5) +
labs(title = "DALYs Averted by Country and Strategy",
subtitle = "10-year horizon, 10,000 children | Discounted at 3% per annum",
x = "", y = "DALYs Averted") +
theme_minimal() +
scale_fill_manual(values = c("Vaccine + ITNs" = "#4DAF4A",
"SMC + ITNs" = "#377EB8",
"Combined" = "#E41A1C")) +
theme(legend.position = "bottom",
plot.title = element_text(size = 14, face = "bold"))
print(p_dalys)
ICER Comparison Across Countries
p_icer <- ce_table %>%
filter(Strategy != "Baseline (ITNs)", !is.na(ICER)) %>%
mutate(Strategy = factor(Strategy, levels = c("Vaccine + ITNs", "SMC + ITNs", "Combined"))) %>%
ggplot(aes(x = Strategy, y = ICER, fill = Country)) +
geom_bar(stat = "identity", position = position_dodge(0.9), width = 0.8) +
geom_hline(yintercept = 800, linetype = "dashed", color = "red", size = 1) +
geom_text(aes(label = paste0("$", format_number(ICER))),
position = position_dodge(0.9), vjust = -0.5, size = 3) +
labs(title = "ICER Comparison Across Countries",
subtitle = paste0("WTP threshold: $800 per DALY averted (red dashed line)"),
x = "", y = "ICER ($ per DALY averted)") +
theme_minimal() +
scale_fill_manual(values = c("Kenya" = "#E69F00", "Uganda" = "#56B4E9",
"Burkina Faso" = "#009E73")) +
theme(legend.position = "bottom",
plot.title = element_text(size = 14, face = "bold")) +
coord_flip()
print(p_icer)
Clinical Cases by Strategy
p_cases <- comparison_table %>%
ggplot(aes(x = Country, y = Clinical_Cases, fill = Strategy)) +
geom_bar(stat = "identity", position = "dodge", width = 0.7) +
labs(title = "Clinical Cases by Country and Strategy",
subtitle = "10-year horizon, 10,000 children (undiscounted)",
x = "", y = "Clinical Cases") +
theme_minimal() +
scale_fill_manual(values = c("Baseline (ITNs)" = "gray50",
"Vaccine + ITNs" = "#4DAF4A",
"SMC + ITNs" = "#377EB8",
"Combined" = "#E41A1C")) +
theme(legend.position = "bottom",
plot.title = element_text(size = 14, face = "bold")) +
scale_y_continuous(labels = comma)
print(p_cases)
Geographic Distribution of DALYs
p_geo <- geo_summary %>%
ggplot(aes(x = geography, y = DALYs_Discounted/1000, fill = Strategy)) +
geom_bar(stat = "identity", position = "dodge", width = 0.7) +
facet_wrap(~Country, ncol = 1, scales = "free_y") +
labs(title = "Geographic Distribution of DALYs by Country",
subtitle = "Discounted at 3% per annum | Values in thousands",
x = "", y = "Discounted DALYs (thousands)") +
theme_minimal() +
scale_fill_manual(values = c("Baseline (ITNs)" = "gray50",
"Vaccine + ITNs" = "#4DAF4A",
"SMC + ITNs" = "#377EB8",
"Combined" = "#E41A1C")) +
theme(legend.position = "bottom",
plot.title = element_text(size = 14, face = "bold"))
print(p_geo)
Wealth Quintile Analysis Visualization
# DALYs averted by wealth quintile
p_wealth_dalys <- wealth_results %>%
ggplot(aes(x = Quintile, y = DALYs_Averted, fill = Quintile)) +
geom_bar(stat = "identity", width = 0.7) +
geom_text(aes(label = format_number(DALYs_Averted)), vjust = -0.5, size = 3.5) +
labs(title = "DALYs Averted by Wealth Quintile (Kenya - Vaccine Strategy)",
subtitle = "Poorest (Q1) to Richest (Q5) | 10-year horizon, 10,000 children",
x = "Wealth Quintile", y = "DALYs Averted") +
theme_minimal() +
scale_fill_brewer(palette = "Blues", name = "Quintile") +
theme(legend.position = "bottom",
plot.title = element_text(size = 14, face = "bold")) +
scale_y_continuous(labels = comma)
print(p_wealth_dalys)
# ICER by wealth quintile
p_wealth_icer <- wealth_results %>%
filter(!is.na(ICER)) %>%
ggplot(aes(x = Quintile, y = ICER, fill = Quintile)) +
geom_bar(stat = "identity", width = 0.7) +
geom_hline(yintercept = 800, linetype = "dashed", color = "red", size = 1) +
geom_text(aes(label = paste0("$", format_number(ICER))), vjust = -0.5, size = 3.5) +
labs(title = "ICER by Wealth Quintile (Kenya - Vaccine Strategy)",
subtitle = paste0("WTP threshold: $800 per DALY (red dashed line)"),
x = "Wealth Quintile", y = "ICER ($ per DALY averted)") +
theme_minimal() +
scale_fill_brewer(palette = "Greens", name = "Quintile") +
theme(legend.position = "bottom",
plot.title = element_text(size = 14, face = "bold")) +
scale_y_continuous(labels = comma, limits = c(0, 250))
print(p_wealth_icer)
Part 11: Policy Recommendations
## ============================================================## POLICY RECOMMENDATIONS BY COUNTRY## ============================================================for(country in names(countries)) {
cat(sprintf("\n### %s\n", country))
cat("\n**Key Findings:**\n")
country_data <- ce_table %>% filter(Country == country)
# Find best strategy
ce_strategies <- country_data %>%
filter(!is.na(ICER), ICER < wtp_threshold) %>%
arrange(ICER)
if(nrow(ce_strategies) > 0) {
best <- ce_strategies[1, ]
cat(sprintf("- Most cost-effective strategy: **%s** (ICER: $%s per DALY)\n",
best$Strategy, format_number(best$ICER)))
cat(sprintf("- DALYs averted: %s per 10,000 children\n", format_number(best$DALYs_Averted)))
cat(sprintf("- Additional cost vs baseline: $%s\n", format_number(best$Inc_Cost)))
}
# Geographic recommendations
cat("\n**Geographic Considerations:**\n")
geo_best <- geo_icer %>%
filter(Country == country, !is.na(ICER)) %>%
group_by(Geography) %>%
arrange(ICER) %>%
slice(1)
for(i in 1:nrow(geo_best)) {
cat(sprintf("- %s areas: **%s** (ICER: $%s per DALY)\n",
geo_best$Geography[i], geo_best$Strategy[i],
format_number(geo_best$ICER[i])))
}
# Budget recommendations
bia_country <- bia_all %>% filter(Country == country)
cat("\n**Budget Impact (National Scale - 5 years):**\n")
cat(sprintf("- Vaccine strategy total cost: $%sM\n",
format_number(bia_country$Five_Year_Cost_M[3])))
cat(sprintf("- Additional funding needed for vaccine: $%sM\n",
format_number(bia_country$Incremental_5yr_M[3])))
# Equity recommendations
if(country == "Kenya") {
cat("\n**Equity Recommendations:**\n")
cat(sprintf("- Concentration Index: %.3f (%s)\n", ci_value,
ifelse(ci_value > 0, "Pro-rich", "Pro-poor")))
cat("- Priority actions:\n")
cat(" 1. Increase vaccine coverage in poorest quintile (current gap: 50%)\n")
cat(" 2. Reduce access delays in rural areas (currently 4.5 days vs 0.5 days urban)\n")
cat(" 3. Implement community-based outreach programs for hardest-to-reach populations\n")
}
cat("\n**Overall Recommendation:**\n")
cat(sprintf("Implement **%s** as the primary strategy for %s, with targeted scale-up in rural and poorest areas.\n",
best$Strategy, country))
if(best$Strategy == "Vaccine + ITNs") {
cat("- Phased approach: Start with vaccine introduction, add SMC in high-transmission seasons\n")
cat("- Secure Gavi co-financing and domestic budget allocation\n")
cat("- Strengthen cold chain infrastructure for vaccine distribution\n")
} else if(best$Strategy == "Combined") {
cat("- Phase 1: Scale up vaccine coverage to 80%+\n")
cat("- Phase 2: Introduce SMC in high-burden regions\n")
cat("- Phase 3: Full combined strategy implementation\n")
}
cat("\n")
}##
## ### Kenya
##
## **Key Findings:**
## - Most cost-effective strategy: **Vaccine + ITNs** (ICER: $450 per DALY)
## - DALYs averted: 465 per 10,000 children
## - Additional cost vs baseline: $209,039
##
## **Geographic Considerations:**
## - Rural areas: **Vaccine + ITNs** (ICER: $348 per DALY)
## - Urban areas: **Vaccine + ITNs** (ICER: $1,153 per DALY)
##
## **Budget Impact (National Scale - 5 years):**
## - Vaccine strategy total cost: $106M
## - Additional funding needed for vaccine: $49M
##
## **Equity Recommendations:**
## - Concentration Index: 0.102 (Pro-rich)
## - Priority actions:
## 1. Increase vaccine coverage in poorest quintile (current gap: 50%)
## 2. Reduce access delays in rural areas (currently 4.5 days vs 0.5 days urban)
## 3. Implement community-based outreach programs for hardest-to-reach populations
##
## **Overall Recommendation:**
## Implement **Vaccine + ITNs** as the primary strategy for Kenya, with targeted scale-up in rural and poorest areas.
## - Phased approach: Start with vaccine introduction, add SMC in high-transmission seasons
## - Secure Gavi co-financing and domestic budget allocation
## - Strengthen cold chain infrastructure for vaccine distribution
##
##
## ### Uganda
##
## **Key Findings:**
## - Most cost-effective strategy: **SMC + ITNs** (ICER: $180 per DALY)
## - DALYs averted: 2,190 per 10,000 children
## - Additional cost vs baseline: $393,923
##
## **Geographic Considerations:**
## - Rural areas: **SMC + ITNs** (ICER: $133 per DALY)
## - Urban areas: **SMC + ITNs** (ICER: $725 per DALY)
##
## **Budget Impact (National Scale - 5 years):**
## - Vaccine strategy total cost: $129M
## - Additional funding needed for vaccine: $58M
##
## **Overall Recommendation:**
## Implement **SMC + ITNs** as the primary strategy for Uganda, with targeted scale-up in rural and poorest areas.
##
##
## ### Burkina Faso
##
## **Key Findings:**
## - Most cost-effective strategy: **Vaccine + ITNs** (ICER: $435 per DALY)
## - DALYs averted: 229 per 10,000 children
## - Additional cost vs baseline: $99,321
##
## **Geographic Considerations:**
## - Rural areas: **Vaccine + ITNs** (ICER: $316 per DALY)
## - Urban areas: **Vaccine + ITNs** (ICER: $1,477 per DALY)
##
## **Budget Impact (National Scale - 5 years):**
## - Vaccine strategy total cost: $95M
## - Additional funding needed for vaccine: $23M
##
## **Overall Recommendation:**
## Implement **Vaccine + ITNs** as the primary strategy for Burkina Faso, with targeted scale-up in rural and poorest areas.
## - Phased approach: Start with vaccine introduction, add SMC in high-transmission seasons
## - Secure Gavi co-financing and domestic budget allocation
## - Strengthen cold chain infrastructure for vaccine distribution## ============================================================## CROSS-COUNTRY COMPARISON SUMMARY## ============================================================summary_cross <- ce_table %>%
filter(Strategy != "Baseline (ITNs)") %>%
group_by(Country) %>%
summarize(
Best_Strategy = Strategy[which.min(ICER)],
Best_ICER = paste0("$", format_number(min(ICER, na.rm = TRUE))),
Max_DALYs_Averted = format_number(max(DALYs_Averted, na.rm = TRUE)),
Most_Expensive_Strategy = Strategy[which.max(Costs)],
Cost_Vaccine = paste0("$", format_number(Costs[Strategy == "Vaccine + ITNs"]))
)
kable(summary_cross, caption = "Cross-Country Comparison Summary",
format = "html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
print()## <table class="table table-striped table-hover table-condensed" style="color: black; margin-left: auto; margin-right: auto;">
## <caption>Cross-Country Comparison Summary</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> Country </th>
## <th style="text-align:left;"> Best_Strategy </th>
## <th style="text-align:left;"> Best_ICER </th>
## <th style="text-align:left;"> Max_DALYs_Averted </th>
## <th style="text-align:left;"> Most_Expensive_Strategy </th>
## <th style="text-align:left;"> Cost_Vaccine </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> Burkina Faso </td>
## <td style="text-align:left;"> Vaccine + ITNs </td>
## <td style="text-align:left;"> $435 </td>
## <td style="text-align:left;"> 500 </td>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> $402,499 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Kenya </td>
## <td style="text-align:left;"> Vaccine + ITNs </td>
## <td style="text-align:left;"> $450 </td>
## <td style="text-align:left;"> 1,209 </td>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> $451,456 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Uganda </td>
## <td style="text-align:left;"> SMC + ITNs </td>
## <td style="text-align:left;"> $180 </td>
## <td style="text-align:left;"> 2,306 </td>
## <td style="text-align:left;"> Combined </td>
## <td style="text-align:left;"> $548,097 </td>
## </tr>
## </tbody>
## </table>Appendix: Complete Results Table
##
##
## # Appendix: Complete Results# Full comparison table
full_table <- ce_table %>%
mutate(
Clinical_Cases = format_number(Clinical_Cases),
Severe_Cases = format_number(Severe_Cases),
Deaths = format_number(Deaths),
DALYs = format_number(DALYs),
Costs = paste0("$", format_number(Costs)),
Intervention_Cost = paste0("$", format_number(Intervention_Cost)),
Treatment_Cost = paste0("$", format_number(Treatment_Cost)),
DALYs_Averted = format_number(DALYs_Averted),
Inc_Cost = ifelse(is.na(Inc_Cost), "Baseline", paste0("$", format_number(Inc_Cost))),
ICER = ifelse(is.na(ICER), "Baseline", paste0("$", format_number(ICER)))
)
kable(full_table, caption = "Complete Results - All Countries and Strategies",
format = "html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
font_size = 12) %>%
scroll_box(width = "100%", height = "500px")| Country | Strategy | Clinical_Cases | Severe_Cases | Deaths | DALYs | Costs | Intervention_Cost | Treatment_Cost | DALYs_Averted | Inc_Cost | ICER |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Kenya | Baseline (ITNs) | 24,751 | 874 | 48 | 2,819 | $242,418 | $ 0 | $242,418 | 0 | $ 0 | Baseline |
| Kenya | SMC + ITNs | 15,648 | 570 | 31 | 1,848 | $756,162 | $600,000 | $156,162 | 972 | $513,744 | $ 529 |
| Kenya | Vaccine + ITNs | 20,322 | 726 | 40 | 2,355 | $451,456 | $250,887 | $200,569 | 465 | $209,039 | $ 450 |
| Kenya | Combined | 13,446 | 497 | 27 | 1,610 | $985,729 | $850,887 | $134,842 | 1,209 | $743,311 | $ 615 |
| Uganda | Baseline (ITNs) | 33,639 | 1,334 | 55 | 3,177 | $300,459 | $ 0 | $300,459 | 0 | $ 0 | Baseline |
| Uganda | SMC + ITNs | 10,255 | 410 | 16 | 986 | $694,382 | $600,000 | $ 94,382 | 2,190 | $393,923 | $ 180 |
| Uganda | Vaccine + ITNs | 26,068 | 1,035 | 42 | 2,475 | $548,097 | $313,560 | $234,537 | 702 | $247,638 | $ 353 |
| Uganda | Combined | 9,062 | 363 | 15 | 870 | $996,568 | $913,560 | $ 83,008 | 2,306 | $696,109 | $ 302 |
| Burkina Faso | Baseline (ITNs) | 32,451 | 1,195 | 23 | 1,368 | $303,178 | $ 0 | $303,178 | 0 | $ 0 | Baseline |
| Burkina Faso | SMC + ITNs | 24,042 | 892 | 17 | 1,021 | $826,775 | $600,000 | $226,775 | 347 | $523,598 | $1,511 |
| Burkina Faso | Vaccine + ITNs | 26,793 | 991 | 19 | 1,139 | $402,499 | $150,742 | $251,757 | 229 | $ 99,321 | $ 435 |
| Burkina Faso | Combined | 20,223 | 754 | 15 | 867 | $942,673 | $750,742 | $191,931 | 500 | $639,496 | $1,278 |