Children (0-14) living with HIV

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 570 0% 53
Angola Angola 35,000 -5.41% 11
Azerbaijan Azerbaijan 200 0% 56
Burundi Burundi 7,400 -7.5% 22
Benin Benin 7,900 -7.06% 21
Burkina Faso Burkina Faso 10,000 -9.09% 19
Bangladesh Bangladesh 1,200 +9.09% 44
Bahamas Bahamas 100 0% 57
Belarus Belarus 200 0% 56
Belize Belize 100 0% 57
Bolivia Bolivia 800 -3.61% 49
Botswana Botswana 7,000 -9.09% 26
Central African Republic Central African Republic 12,000 +9.09% 17
Chile Chile 500 0% 55
Côte d’Ivoire Côte d’Ivoire 21,000 -8.7% 14
Cameroon Cameroon 29,000 -6.45% 12
Congo - Kinshasa Congo - Kinshasa 60,000 -1.64% 8
Congo - Brazzaville Congo - Brazzaville 12,000 0% 17
Colombia Colombia 2,000 -4.76% 41
Cape Verde Cape Verde 100 0% 57
Costa Rica Costa Rica 200 0% 56
Cuba Cuba 200 0% 56
Dominican Republic Dominican Republic 2,800 -3.45% 38
Algeria Algeria 1,300 +8.33% 43
Ecuador Ecuador 980 -2% 46
Egypt Egypt 790 +5.33% 50
Eritrea Eritrea 500 0% 55
Ethiopia Ethiopia 37,000 -7.5% 10
Fiji Fiji 100 0% 57
France France 500 0% 55
Gabon Gabon 2,300 -4.17% 39
Ghana Ghana 25,000 -3.85% 13
Guinea Guinea 11,000 -8.33% 18
Gambia Gambia 2,200 -4.35% 40
Guinea-Bissau Guinea-Bissau 2,300 -8% 39
Equatorial Guinea Equatorial Guinea 4,000 0% 33
Guatemala Guatemala 1,700 -5.56% 42
Guyana Guyana 500 0% 55
Honduras Honduras 810 -11% 48
Haiti Haiti 6,500 +3.17% 28
Indonesia Indonesia 18,000 0% 15
India India 68,000 6
Iran Iran 1,000 0% 45
Jamaica Jamaica 500 0% 55
Kenya Kenya 68,000 -8.11% 6
Kyrgyzstan Kyrgyzstan 500 0% 55
Cambodia Cambodia 2,000 -13% 41
Laos Laos 600 -1.64% 52
Liberia Liberia 3,000 -6.25% 37
Libya Libya 200 0% 56
Lesotho Lesotho 7,100 -11.3% 25
Morocco Morocco 840 -2.33% 47
Moldova Moldova 200 -60% 56
Madagascar Madagascar 5,000 +8.7% 29
Mexico Mexico 3,400 0% 36
Mali Mali 12,000 0% 17
Myanmar (Burma) Myanmar (Burma) 8,900 0% 20
Mozambique Mozambique 150,000 -6.25% 2
Mauritania Mauritania 670 -1.47% 51
Malawi Malawi 57,000 -8.06% 9
Malaysia Malaysia 500 0% 55
Namibia Namibia 7,200 -10% 24
Niger Niger 3,800 -2.56% 34
Nicaragua Nicaragua 200 0% 56
Nepal Nepal 1,200 -7.69% 44
Pakistan Pakistan 6,700 +8.06% 27
Panama Panama 500 0% 55
Peru Peru 1,700 0% 42
Philippines Philippines 1,000 +20.5% 45
Papua New Guinea Papua New Guinea 4,400 +2.33% 31
Paraguay Paraguay 500 0% 55
Rwanda Rwanda 7,100 -12.3% 25
Sudan Sudan 4,600 +2.22% 30
Senegal Senegal 3,600 -7.69% 35
Sierra Leone Sierra Leone 6,700 -2.9% 27
El Salvador El Salvador 500 0% 55
South Sudan South Sudan 15,000 0% 16
São Tomé & Príncipe São Tomé & Príncipe 100 0% 57
Suriname Suriname 200 0% 56
Eswatini Eswatini 7,300 -9.88% 23
Chad Chad 12,000 0% 17
Togo Togo 7,200 -7.69% 24
Thailand Thailand 1,700 -19% 42
Tunisia Tunisia 200 0% 56
Tanzania Tanzania 79,000 -7.06% 4
Uganda Uganda 80,000 -8.05% 3
Uruguay Uruguay 500 0% 55
Venezuela Venezuela 4,100 -4.65% 32
Vietnam Vietnam 3,800 -5% 34
Yemen Yemen 540 +1.89% 54
South Africa South Africa 230,000 -8% 1
Zambia Zambia 66,000 -5.71% 7
Zimbabwe Zimbabwe 75,000 -8.54% 5 

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'SH.HIV.0014'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)
                    
                
                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'SH.HIV.0014'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))