Children (ages 0-14) newly infected with HIV

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 100 0% 34
Angola Angola 3,800 -9.52% 10
Azerbaijan Azerbaijan 100 0% 34
Burundi Burundi 500 0% 32
Benin Benin 640 -22.9% 30
Burkina Faso Burkina Faso 780 -10.3% 27
Bangladesh Bangladesh 500 0% 32
Bahamas Bahamas 100 0% 34
Belarus Belarus 100 0% 34
Belize Belize 100 0% 34
Bolivia Bolivia 100 0% 34
Botswana Botswana 200 -60% 33
Central African Republic Central African Republic 2,100 +10.5% 15
Chile Chile 100 0% 34
Côte d’Ivoire Côte d’Ivoire 1,600 0% 19
Cameroon Cameroon 3,400 -10.5% 11
Congo - Kinshasa Congo - Kinshasa 7,300 -2.67% 3
Congo - Brazzaville Congo - Brazzaville 2,500 +4.17% 14
Colombia Colombia 200 0% 33
Cape Verde Cape Verde 100 0% 34
Costa Rica Costa Rica 100 0% 34
Cuba Cuba 100 0% 34
Dominican Republic Dominican Republic 500 0% 32
Algeria Algeria 500 0% 32
Ecuador Ecuador 100 -50% 34
Egypt Egypt 200 0% 33
Eritrea Eritrea 100 0% 34
Ethiopia Ethiopia 2,000 -16.7% 16
Fiji Fiji 100 0% 34
France France 100 0% 34
Gabon Gabon 500 0% 32
Ghana Ghana 2,900 +11.5% 12
Guinea Guinea 1,400 0% 21
Gambia Gambia 500 0% 32
Guinea-Bissau Guinea-Bissau 500 0% 32
Equatorial Guinea Equatorial Guinea 710 -5.33% 29
Guatemala Guatemala 200 0% 33
Guyana Guyana 100 0% 34
Honduras Honduras 100 0% 34
Haiti Haiti 1,100 +10% 23
Indonesia Indonesia 2,800 -6.67% 13
India India 4,100 9
Iran Iran 200 0% 33
Jamaica Jamaica 100 0% 34
Kenya Kenya 4,500 +2.27% 6
Kyrgyzstan Kyrgyzstan 100 0% 34
Cambodia Cambodia 100 0% 34
Laos Laos 100 0% 34
Liberia Liberia 500 0% 32
Lesotho Lesotho 500 -7.41% 32
Morocco Morocco 100 0% 34
Moldova Moldova 100 0% 34
Madagascar Madagascar 1,300 +8.33% 22
Mexico Mexico 500 -1.96% 32
Mali Mali 1,800 0% 18
Myanmar (Burma) Myanmar (Burma) 1,100 -21.4% 23
Mozambique Mozambique 13,000 -18.8% 1
Mauritania Mauritania 100 0% 34
Malawi Malawi 2,800 +3.7% 13
Malaysia Malaysia 100 0% 34
Namibia Namibia 500 0% 32
Niger Niger 620 -7.46% 31
Nicaragua Nicaragua 100 0% 34
Nepal Nepal 100 0% 34
Panama Panama 100 0% 34
Peru Peru 500 0% 32
Philippines Philippines 500 0% 32
Papua New Guinea Papua New Guinea 820 +5.13% 26
Rwanda Rwanda 500 0% 32
Sudan Sudan 860 +1.18% 25
Senegal Senegal 500 0% 32
Sierra Leone Sierra Leone 1,000 +17.6% 24
El Salvador El Salvador 100 0% 34
South Sudan South Sudan 1,900 0% 17
São Tomé & Príncipe São Tomé & Príncipe 100 0% 34
Suriname Suriname 100 0% 34
Eswatini Eswatini 500 0% 32
Chad Chad 1,500 0% 20
Togo Togo 730 -13.1% 28
Thailand Thailand 100 0% 34
Tanzania Tanzania 5,200 -21.2% 5
Uganda Uganda 5,900 +1.72% 4
Uruguay Uruguay 100 0% 34
Vietnam Vietnam 500 0% 32
Yemen Yemen 100 0% 34
South Africa South Africa 8,000 +5.26% 2
Zambia Zambia 4,400 -8.33% 7
Zimbabwe Zimbabwe 4,200 -8.7% 8 

                    
# 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.INCD.14'

# 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.INCD.14'

# 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))