Sub-County Level Geospatial Analysis
This report presents the sub-county level distribution of Neglected Tropical Diseases (NTDs) across Kenya’s 47 counties and 290 sub-counties. Kenya bears a significant NTD burden, with diseases such as Schistosomiasis, Lymphatic Filariasis (LF), Soil-Transmitted Helminths (STH), Visceral Leishmaniasis (Kala-azar), Trachoma, and Onchocerciasis disproportionately affecting populations in coastal, western, and arid/semi-arid regions.
Sys.setenv(SHAPE_RESTORE_SHX = "YES")
shp <- st_read("ken_admin2.shp", quiet = TRUE) |>
st_set_crs(4326) # WGS84
ntd <- read.csv("kenya_ntd_data.csv")
# ── Join (row order matches)
kenya_sf <- shp |>
bind_cols(ntd |> select(-idx))# Derive county-level boundaries for overlay
county_borders <- kenya_sf |>
st_make_valid() |>
group_by(county) |>
summarise(geometry = st_union(geometry), .groups = "drop")The NTD Composite Index integrates prevalence across all six diseases into a single burden score. Higher values indicate areas of co-endemicity and greatest intervention priority.
# Classify into 5 burden categories
kenya_sf <- kenya_sf |>
mutate(
burden_cat = cut(
ntd_composite_index,
breaks = c(0, 4, 8, 12, 18, Inf),
labels = c("Very Low (0–4)", "Low (4–8)", "Moderate (8–12)",
"High (12–18)", "Very High (>18)"),
include.lowest = TRUE
)
)
pal_burden <- c(
"Very Low (0–4)" = "#ffffcc",
"Low (4–8)" = "#fed976",
"Moderate (8–12)" = "#feb24c",
"High (12–18)" = "#f03b20",
"Very High (>18)" = "#7a0404"
)
ggplot() +
# Sub-county fill
geom_sf(data = kenya_sf, aes(fill = burden_cat),
color = "white", linewidth = 0.05, alpha = 0.92) +
# County borders
geom_sf(data = county_borders, fill = NA,
color = "#2d3436", linewidth = 0.45) +
scale_fill_manual(
values = pal_burden,
name = "Burden Level",
guide = guide_legend(
title.position = "top",
keywidth = unit(0.5, "cm"),
keyheight = unit(0.55, "cm"),
override.aes = list(color = "grey50", linewidth = 0.3)
)
) +
annotation_scale(
location = "bl", width_hint = 0.25,
pad_x = unit(0.5, "cm"), pad_y = unit(0.5, "cm"),
text_cex = 0.7, line_width = 0.8
) +
annotation_north_arrow(
location = "bl", which_north = "true",
pad_x = unit(0.5, "cm"), pad_y = unit(1.1, "cm"),
style = north_arrow_fancy_orienteering(
fill = c("white", "#2d3436"),
line_col = "#2d3436", text_col = "#2d3436"
),
height = unit(1.2, "cm"), width = unit(1.2, "cm")
) +
labs(
title = "Kenya — NTD Composite Burden Index",
subtitle = "Sub-county level | All six NTDs combined",
caption = "Sources: Simulated data based on Kenya NTD epidemiological patterns.\nCounty boundaries overlaid in dark grey."
) +
coord_sf(xlim = c(33.9, 42.0), ylim = c(-4.7, 5.0), expand = FALSE) +
theme_ntd_map()
Schistosomiasis (Bilharzia) is endemic primarily in coastal counties (Kilifi, Kwale, Mombasa, Tana River) and western Kenya (Siaya, Homa Bay, Kisumu, Busia), where communities live close to freshwater bodies.
ggplot() +
geom_sf(data = kenya_sf, aes(fill = schisto_prev),
color = "white", linewidth = 0.05, alpha = 0.92) +
geom_sf(data = county_borders, fill = NA,
color = "#2d3436", linewidth = 0.45) +
scale_fill_distiller(
palette = "YlOrRd",
direction = 1,
name = "Prevalence (%)",
limits = c(0, 50),
breaks = c(0, 10, 20, 30, 40, 50),
labels = function(x) paste0(x, "%"),
guide = guide_colorbar(
title.position = "top",
barwidth = unit(0.4, "cm"),
barheight = unit(4, "cm"),
ticks.colour = "white"
)
) +
annotation_scale(
location = "bl", width_hint = 0.25,
pad_x = unit(0.5, "cm"), pad_y = unit(0.5, "cm"),
text_cex = 0.7, line_width = 0.8
) +
annotation_north_arrow(
location = "bl", which_north = "true",
pad_x = unit(0.5, "cm"), pad_y = unit(1.1, "cm"),
style = north_arrow_fancy_orienteering(
fill = c("white", "#2d3436"),
line_col = "#2d3436", text_col = "#2d3436"
),
height = unit(1.2, "cm"), width = unit(1.2, "cm")
) +
labs(
title = "Schistosomiasis (Bilharzia) Prevalence",
subtitle = "Sub-county level | Coastal & western Kenya hotspots",
caption = "Highest prevalence in Kilifi, Kwale, Siaya, Homa Bay & Busia sub-counties."
) +
coord_sf(xlim = c(33.9, 42.0), ylim = c(-4.7, 5.0), expand = FALSE) +
theme_ntd_map()
Lymphatic Filariasis (LF) causes elephantiasis and hydrocele, and is primarily transmitted by Culex mosquitoes in coastal Kenya. Mass Drug Administration (MDA) programs have been ongoing since 2002.
ggplot() +
geom_sf(data = kenya_sf, aes(fill = lf_prev),
color = "white", linewidth = 0.05, alpha = 0.92) +
geom_sf(data = county_borders, fill = NA,
color = "#1a1a2e", linewidth = 0.45) +
scale_fill_distiller(
palette = "Blues",
direction = 1,
name = "Prevalence (%)",
limits = c(0, 28),
breaks = c(0, 5, 10, 15, 20, 25),
labels = function(x) paste0(x, "%"),
guide = guide_colorbar(
title.position = "top",
barwidth = unit(0.4, "cm"),
barheight = unit(4, "cm"),
ticks.colour = "white"
)
) +
annotation_scale(
location = "bl", width_hint = 0.25,
pad_x = unit(0.5, "cm"), pad_y = unit(0.5, "cm"),
text_cex = 0.7, line_width = 0.8
) +
annotation_north_arrow(
location = "bl", which_north = "true",
pad_x = unit(0.5, "cm"), pad_y = unit(1.1, "cm"),
style = north_arrow_fancy_orienteering(
fill = c("white", "#1a1a2e"),
line_col = "#1a1a2e", text_col = "#1a1a2e"
),
height = unit(1.2, "cm"), width = unit(1.2, "cm")
) +
labs(
title = "Lymphatic Filariasis Prevalence",
subtitle = "Sub-county level | Coastal belt & selected western areas",
caption = "MDA program active in Mombasa, Kilifi, Kwale, Malindi, Tana River & Lamu since 2002."
) +
coord_sf(xlim = c(33.9, 42.0), ylim = c(-4.7, 5.0), expand = FALSE) +
theme_ntd_map()
STH (hookworm, roundworm, whipworm) are the most widespread NTDs in Kenya. School-based deworming programs cover 32 endemic counties.
ggplot() +
geom_sf(data = kenya_sf, aes(fill = sth_prev),
color = "white", linewidth = 0.05, alpha = 0.92) +
geom_sf(data = county_borders, fill = NA,
color = "#2d3436", linewidth = 0.45) +
scale_fill_distiller(
palette = "Greens",
direction = 1,
name = "Prevalence (%)",
limits = c(0, 55),
breaks = c(0, 10, 20, 30, 40, 50),
labels = function(x) paste0(x, "%"),
guide = guide_colorbar(
title.position = "top",
barwidth = unit(0.4, "cm"),
barheight = unit(4, "cm"),
ticks.colour = "white"
)
) +
annotation_scale(
location = "bl", width_hint = 0.25,
pad_x = unit(0.5, "cm"), pad_y = unit(0.5, "cm"),
text_cex = 0.7, line_width = 0.8
) +
annotation_north_arrow(
location = "bl", which_north = "true",
pad_x = unit(0.5, "cm"), pad_y = unit(1.1, "cm"),
style = north_arrow_fancy_orienteering(
fill = c("white", "#2d3436"),
line_col = "#2d3436", text_col = "#2d3436"
),
height = unit(1.2, "cm"), width = unit(1.2, "cm")
) +
labs(
title = "🪱 Soil-Transmitted Helminths (STH) Prevalence",
subtitle = "Sub-county level | School-age children most affected",
caption = "Kenya's National School-Based Deworming Programme (NSBDP) active since 2012.\nWestern Kenya shows highest burden."
) +
coord_sf(xlim = c(33.9, 42.0), ylim = c(-4.7, 5.0), expand = FALSE) +
theme_ntd_map()
Kala-azar is caused by Leishmania donovani and transmitted by sandflies. It is highly lethal if untreated, endemic in arid/semi-arid lands (ASAL) of Kenya, particularly Marsabit, Wajir, Mandera, Garissa, Isiolo, Baringo, and West Pokot.
ggplot() +
geom_sf(data = kenya_sf, aes(fill = vl_cases_per100k),
color = "white", linewidth = 0.05, alpha = 0.92) +
geom_sf(data = county_borders, fill = NA,
color = "#2d3436", linewidth = 0.45) +
scale_fill_distiller(
palette = "Purples",
direction = 1,
name = "Cases /\n100k pop",
limits = c(0, 18),
breaks = c(0, 3, 6, 9, 12, 15),
guide = guide_colorbar(
title.position = "top",
barwidth = unit(0.4, "cm"),
barheight = unit(4, "cm"),
ticks.colour = "white"
)
) +
annotation_scale(
location = "bl", width_hint = 0.25,
pad_x = unit(0.5, "cm"), pad_y = unit(0.5, "cm"),
text_cex = 0.7, line_width = 0.8
) +
annotation_north_arrow(
location = "bl", which_north = "true",
pad_x = unit(0.5, "cm"), pad_y = unit(1.1, "cm"),
style = north_arrow_fancy_orienteering(
fill = c("white", "#2d3436"),
line_col = "#2d3436", text_col = "#2d3436"
),
height = unit(1.2, "cm"), width = unit(1.2, "cm")
) +
labs(
title = "Visceral Leishmaniasis (Kala-azar)",
subtitle = "Sub-county level | Arid & semi-arid lands of northern Kenya",
caption = "National Programme to Eliminate VL targets 5 million at-risk people across 12 endemic counties.\nFatal if untreated; caused by Leishmania donovani."
) +
coord_sf(xlim = c(33.9, 42.0), ylim = c(-4.7, 5.0), expand = FALSE) +
theme_ntd_map()
Trachoma (blinding disease) is caused by Chlamydia trachomatis and is endemic in arid and pastoralist communities of northern and Rift Valley Kenya. The WHO SAFE strategy is being implemented.
ggplot() +
geom_sf(data = kenya_sf, aes(fill = trachoma_tf_prev),
color = "white", linewidth = 0.05, alpha = 0.92) +
geom_sf(data = county_borders, fill = NA,
color = "#2d3436", linewidth = 0.45) +
scale_fill_distiller(
palette = "Oranges",
direction = 1,
name = "TF Prevalence\n(%)",
limits = c(0, 35),
breaks = c(0, 5, 10, 15, 20, 25, 30),
labels = function(x) paste0(x, "%"),
guide = guide_colorbar(
title.position = "top",
barwidth = unit(0.4, "cm"),
barheight = unit(4, "cm"),
ticks.colour = "white"
)
) +
# WHO 5% threshold line indicator
annotation_scale(
location = "bl", width_hint = 0.25,
pad_x = unit(0.5, "cm"), pad_y = unit(0.5, "cm"),
text_cex = 0.7, line_width = 0.8
) +
annotation_north_arrow(
location = "bl", which_north = "true",
pad_x = unit(0.5, "cm"), pad_y = unit(1.1, "cm"),
style = north_arrow_fancy_orienteering(
fill = c("white", "#2d3436"),
line_col = "#2d3436", text_col = "#2d3436"
),
height = unit(1.2, "cm"), width = unit(1.2, "cm")
) +
labs(
title = "Trachoma (TF) Prevalence — Children 1–9 yrs",
subtitle = "Sub-county level | WHO elimination target: TF < 5%",
caption = "TF = Trachomatous Inflammation–Follicular. Highest burden in Turkana, Marsabit,\nMandera, Wajir, Garissa and West Pokot sub-counties."
) +
coord_sf(xlim = c(33.9, 42.0), ylim = c(-4.7, 5.0), expand = FALSE) +
theme_ntd_map()
Mass Drug Administration (MDA) is the primary control strategy. This map shows estimated MDA coverage across sub-counties, highlighting areas that may need intervention scale-up.
# Create MDA coverage categories
kenya_sf <- kenya_sf |>
mutate(
mda_cat = cut(
mda_coverage_pct,
breaks = c(0, 50, 65, 75, 85, 100),
labels = c("<50% (Critical)", "50–65% (Low)", "65–75% (Fair)",
"75–85% (Good)", ">85% (Target)"),
include.lowest = TRUE
)
)
pal_mda <- c(
"<50% (Critical)" = "#d73027",
"50–65% (Low)" = "#fc8d59",
"65–75% (Fair)" = "#fee090",
"75–85% (Good)" = "#91bfdb",
">85% (Target)" = "#1a6da6"
)
ggplot() +
geom_sf(data = kenya_sf, aes(fill = mda_cat),
color = "white", linewidth = 0.05, alpha = 0.92) +
geom_sf(data = county_borders, fill = NA,
color = "#2d3436", linewidth = 0.45) +
scale_fill_manual(
values = pal_mda,
name = "MDA Coverage",
guide = guide_legend(
title.position = "top",
keywidth = unit(0.5, "cm"),
keyheight = unit(0.55, "cm"),
override.aes = list(color = "grey50", linewidth = 0.3)
)
) +
annotation_scale(
location = "bl", width_hint = 0.25,
pad_x = unit(0.5, "cm"), pad_y = unit(0.5, "cm"),
text_cex = 0.7, line_width = 0.8
) +
annotation_north_arrow(
location = "bl", which_north = "true",
pad_x = unit(0.5, "cm"), pad_y = unit(1.1, "cm"),
style = north_arrow_fancy_orienteering(
fill = c("white", "#2d3436"),
line_col = "#2d3436", text_col = "#2d3436"
),
height = unit(1.2, "cm"), width = unit(1.2, "cm")
) +
labs(
title = "Mass Drug Administration (MDA) Coverage",
subtitle = "Sub-county level | WHO target ≥ 75% therapeutic coverage",
caption = "Blue = meeting or exceeding WHO target. Red = critical gaps requiring urgent intervention scale-up."
) +
coord_sf(xlim = c(33.9, 42.0), ylim = c(-4.7, 5.0), expand = FALSE) +
theme_ntd_map()
county_summary <- kenya_sf |>
st_drop_geometry() |>
group_by(county) |>
summarise(
`Sub-counties` = n(),
`Schisto (% mean)` = round(mean(schisto_prev), 1),
`LF (% mean)` = round(mean(lf_prev), 1),
`STH (% mean)` = round(mean(sth_prev), 1),
`VL (per 100k)` = round(mean(vl_cases_per100k), 1),
`Trachoma TF (%)` = round(mean(trachoma_tf_prev), 1),
`MDA Coverage (%)` = round(mean(mda_coverage_pct), 1),
`NTD Index (mean)` = round(mean(ntd_composite_index), 2),
.groups = "drop"
) |>
arrange(desc(`NTD Index (mean)`))
county_summary |>
kable(align = c("l", rep("c", ncol(county_summary) - 1))) |>
kable_styling(
bootstrap_options = c("striped", "hover", "condensed", "responsive"),
full_width = TRUE,
font_size = 12
) |>
column_spec(1, bold = TRUE) |>
column_spec(9, bold = TRUE,
color = ifelse(county_summary$`NTD Index (mean)` >= 12,
"white", "black"),
background = ifelse(county_summary$`NTD Index (mean)` >= 18, "#7a0404",
ifelse(county_summary$`NTD Index (mean)` >= 12, "#f03b20",
ifelse(county_summary$`NTD Index (mean)` >= 8, "#feb24c",
ifelse(county_summary$`NTD Index (mean)` >= 4, "#fed976",
"#ffffcc"))))) |>
add_header_above(c(" " = 2,
"NTD Prevalence Indicators" = 5,
"Intervention" = 1,
"Burden" = 1)) |>
scroll_box(height = "450px")| county | Sub-counties | Schisto (% mean) | LF (% mean) | STH (% mean) | VL (per 100k) | Trachoma TF (%) | MDA Coverage (%) | NTD Index (mean) |
|---|---|---|---|---|---|---|---|---|
| Mombasa | 6 | 35.3 | 15.2 | 30.0 | 0.5 | 2.5 | 70.4 | 20.09 |
| Taita Taveta | 4 | 27.3 | 21.0 | 34.5 | 0.7 | 3.0 | 63.3 | 19.75 |
| Tana River | 3 | 27.1 | 16.2 | 25.3 | 6.3 | 13.7 | 48.5 | 18.54 |
| Kwale | 4 | 32.0 | 13.7 | 27.9 | 0.4 | 3.2 | 66.4 | 18.39 |
| Kilifi | 7 | 29.5 | 15.2 | 28.9 | 0.4 | 1.8 | 71.1 | 18.02 |
| Lamu | 2 | 28.6 | 21.6 | 19.4 | 0.2 | 3.5 | 74.8 | 17.21 |
| Siaya | 6 | 23.6 | 5.2 | 39.2 | 0.5 | 3.0 | 69.8 | 17.05 |
| Bungoma | 9 | 26.8 | 5.0 | 34.8 | 0.5 | 2.0 | 57.7 | 16.87 |
| Vihiga | 5 | 19.7 | 5.5 | 40.3 | 0.6 | 3.3 | 68.9 | 16.13 |
| Homa Bay | 8 | 23.6 | 4.5 | 34.4 | 0.6 | 2.8 | 67.6 | 15.28 |
| Kisii | 9 | 24.7 | 2.7 | 33.8 | 0.5 | 2.1 | 74.1 | 15.08 |
| Kisumu | 7 | 18.9 | 5.4 | 39.8 | 0.4 | 2.5 | 75.3 | 15.07 |
| Kakamega | 12 | 20.4 | 5.3 | 34.3 | 0.6 | 2.1 | 75.7 | 14.97 |
| Migori | 8 | 21.0 | 4.0 | 36.4 | 0.4 | 2.6 | 61.8 | 14.77 |
| Busia | 7 | 16.2 | 5.8 | 28.8 | 0.4 | 3.3 | 61.8 | 12.27 |
| Garissa | 6 | 2.5 | 1.4 | 11.2 | 10.0 | 25.5 | 49.2 | 6.91 |
| Turkana | 6 | 2.6 | 1.8 | 10.8 | 9.7 | 22.8 | 53.3 | 6.67 |
| Marsabit | 4 | 3.4 | 2.0 | 11.7 | 11.6 | 15.7 | 54.7 | 6.57 |
| Samburu | 3 | 2.7 | 1.9 | 10.8 | 8.5 | 19.8 | 44.3 | 6.30 |
| Laikipia | 3 | 7.0 | 2.4 | 11.7 | 0.5 | 11.5 | 69.9 | 6.19 |
| Mandera | 6 | 3.6 | 1.7 | 8.7 | 7.0 | 21.7 | 58.6 | 6.18 |
| West Pokot | 4 | 1.0 | 2.3 | 10.0 | 8.6 | 22.6 | 52.9 | 6.00 |
| Wajir | 6 | 2.5 | 1.7 | 10.1 | 9.2 | 17.9 | 47.3 | 5.92 |
| Nandi | 6 | 5.9 | 1.9 | 12.0 | 0.4 | 3.0 | 77.3 | 5.86 |
| Meru | 9 | 8.4 | 1.6 | 10.2 | 3.2 | 2.0 | 60.2 | 5.47 |
| Baringo | 6 | 2.1 | 1.6 | 8.1 | 10.1 | 17.6 | 45.1 | 5.43 |
| Kitui | 8 | 7.6 | 1.5 | 11.3 | 2.3 | 2.7 | 67.7 | 5.43 |
| Trans Nzoia | 5 | 6.9 | 1.1 | 9.5 | 0.4 | 3.0 | 76.0 | 5.25 |
| Narok | 6 | 8.3 | 1.6 | 9.6 | 0.5 | 2.9 | 65.1 | 5.18 |
| Nairobi | 17 | 6.8 | 1.4 | 12.3 | 0.4 | 2.2 | 68.1 | 5.14 |
| Embu | 4 | 6.8 | 1.2 | 10.2 | 3.0 | 3.5 | 62.0 | 5.07 |
| Uasin Gishu | 6 | 7.8 | 1.2 | 10.0 | 0.5 | 2.9 | 65.1 | 4.98 |
| Kiambu | 12 | 7.2 | 1.6 | 10.5 | 0.6 | 2.4 | 73.0 | 4.94 |
| Kericho | 6 | 7.7 | 1.1 | 9.7 | 0.6 | 2.4 | 65.6 | 4.88 |
| Nyandarua | 5 | 8.0 | 1.8 | 8.1 | 0.5 | 3.3 | 64.7 | 4.83 |
| Machakos | 8 | 6.5 | 0.7 | 10.6 | 2.6 | 2.9 | 63.8 | 4.82 |
| Elgeyo Marakwet | 4 | 8.8 | 1.8 | 7.3 | 0.3 | 1.4 | 56.3 | 4.73 |
| Makueni | 6 | 6.2 | 1.4 | 11.1 | 0.4 | 1.9 | 60.2 | 4.70 |
| Isiolo | 3 | 2.5 | 0.9 | 7.3 | 7.6 | 14.5 | 48.7 | 4.68 |
| Tharaka Nithi | 3 | 6.0 | 2.3 | 9.9 | 0.6 | 2.6 | 82.7 | 4.63 |
| Muranga | 7 | 6.9 | 1.4 | 9.2 | 0.5 | 2.7 | 74.3 | 4.61 |
| Nyeri | 8 | 5.8 | 1.2 | 10.7 | 0.6 | 2.7 | 63.7 | 4.58 |
| Nakuru | 11 | 7.0 | 1.7 | 8.7 | 0.5 | 2.3 | 59.9 | 4.53 |
| Bomet | 5 | 6.9 | 0.9 | 9.3 | 0.4 | 2.6 | 73.7 | 4.52 |
| Kirinyaga | 5 | 6.2 | 1.8 | 7.6 | 0.6 | 3.6 | 56.2 | 4.22 |
| Kajiado | 5 | 6.2 | 1.9 | 6.5 | 0.5 | 3.3 | 63.7 | 4.01 |
Priority intervention counties (NTD Index > 12): These sub-counties require immediate intensification of MDA, WASH improvements, and targeted disease-specific interventions.
Co-endemicity: Many western and coastal sub-counties carry simultaneous burdens of Schistosomiasis, LF, and STH — requiring integrated platform delivery to optimize resource use.
MDA coverage gaps: Sub-counties with coverage below 65% are unlikely to reach WHO elimination thresholds regardless of drug availability — community engagement must be strengthened.
| Disease | Primary Hotspots | WHO Target |
|---|---|---|
| Schistosomiasis | Kilifi, Kwale, Siaya, Homa Bay, Busia | Prevalence < 1% (heavy infections) |
| Lymphatic Filariasis | Mombasa, Kilifi, Kwale, Tana River, Lamu | Mf prevalence < 1% |
| STH | Western Kenya (Kakamega, Bungoma, Siaya) | Moderate-heavy < 2% |
| Visceral Leishmaniasis | Marsabit, Turkana, Wajir, Mandera, Garissa | < 1 case/10,000 |
| Trachoma | Turkana, Marsabit, West Pokot, Baringo | TF < 5% in children |
| Onchocerciasis | Nzoia basin (Kakamega, Bungoma, Nandi) | Elimination |