Incidence of HIV, ages 15-24 (per 1,000 uninfected population ages 15-24)

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 0.04 0% 61
Angola Angola 0.68 -8.11% 25
Albania Albania 0.02 -33.3% 63
Argentina Argentina 0.17 -5.56% 49
Armenia Armenia 0.17 0% 49
Azerbaijan Azerbaijan 0.06 0% 59
Burundi Burundi 0.13 -7.14% 52
Belgium Belgium 0.04 0% 61
Benin Benin 0.14 -17.6% 51
Burkina Faso Burkina Faso 0.08 -11.1% 57
Bangladesh Bangladesh 0.01 0% 64
Bulgaria Bulgaria 0.1 0% 55
Bahamas Bahamas 0.28 -9.68% 42
Belarus Belarus 0.15 0% 50
Belize Belize 0.73 -5.19% 22
Bolivia Bolivia 0.11 -8.33% 54
Barbados Barbados 0.58 -1.69% 30
Bhutan Bhutan 0.05 0% 60
Botswana Botswana 3.15 -11.8% 9
Central African Republic Central African Republic 2.24 -10% 10
Chile Chile 0.41 0% 36
Côte d’Ivoire Côte d’Ivoire 0.31 -13.9% 40
Cameroon Cameroon 0.47 -25.4% 33
Congo - Kinshasa Congo - Kinshasa 0.13 -18.8% 52
Congo - Brazzaville Congo - Brazzaville 4.83 +5.23% 4
Colombia Colombia 0.1 -9.09% 55
Comoros Comoros 0.01 0% 64
Cape Verde Cape Verde 0.27 -6.9% 43
Costa Rica Costa Rica 0.32 -3.03% 39
Cuba Cuba 0.44 0% 35
Czechia Czechia 0.04 0% 61
Dominican Republic Dominican Republic 0.63 -8.7% 28
Algeria Algeria 0.05 0% 60
Ecuador Ecuador 0.23 0% 45
Egypt Egypt 0.05 +25% 60
Eritrea Eritrea 0.08 -11.1% 57
Estonia Estonia 0.08 0% 57
Ethiopia Ethiopia 0.11 -15.4% 54
Fiji Fiji 0.24 +9.09% 44
France France 0.12 +9.09% 53
Gabon Gabon 1.45 -8.81% 13
Georgia Georgia 0.07 0% 58
Ghana Ghana 0.77 -13.5% 19
Guinea Guinea 0.66 -14.3% 26
Gambia Gambia 0.45 -2.17% 34
Guinea-Bissau Guinea-Bissau 0.71 -12.3% 23
Equatorial Guinea Equatorial Guinea 3.95 -1.5% 7
Greece Greece 0.06 +20% 59
Grenada Grenada 0.1 -9.09% 55
Guatemala Guatemala 0.09 0% 56
Guyana Guyana 0.5 +2.04% 32
Honduras Honduras 0.03 0% 62
Croatia Croatia 0.05 0% 60
Haiti Haiti 0.78 0% 18
Indonesia Indonesia 0.27 -3.57% 43
Iran Iran 0.05 +25% 60
Iraq Iraq 0.02 +100% 63
Iceland Iceland 0.03 0% 62
Italy Italy 0.03 0% 62
Jamaica Jamaica 0.75 -9.64% 20
Jordan Jordan 0.01 0% 64
Kenya Kenya 0.74 -21.3% 21
Kyrgyzstan Kyrgyzstan 0.07 0% 58
Cambodia Cambodia 0.21 +5% 46
St. Kitts & Nevis St. Kitts & Nevis 0.73 +1.39% 22
Laos Laos 0.3 0% 41
Liberia Liberia 0.41 -16.3% 36
Sri Lanka Sri Lanka 0.01 0% 64
Lesotho Lesotho 4.49 -20.2% 5
Lithuania Lithuania 0.08 -11.1% 57
Luxembourg Luxembourg 0.07 0% 58
Latvia Latvia 0.14 0% 51
Morocco Morocco 0.01 0% 64
Moldova Moldova 0.44 0% 35
Madagascar Madagascar 0.27 -3.57% 43
Maldives Maldives 0.01 0% 64
Mexico Mexico 0.39 0% 37
North Macedonia North Macedonia 0.02 0% 63
Mali Mali 0.44 -2.22% 35
Malta Malta 0.18 0% 48
Myanmar (Burma) Myanmar (Burma) 0.65 +1.56% 27
Montenegro Montenegro 0.08 -20% 57
Mongolia Mongolia 0.01 0% 64
Mozambique Mozambique 5.58 -20.5% 3
Mauritania Mauritania 0.09 0% 56
Malawi Malawi 0.89 -19.8% 15
Malaysia Malaysia 0.1 -9.09% 55
Namibia Namibia 4.39 -11.7% 6
Niger Niger 0.04 0% 61
Nicaragua Nicaragua 0.19 -5% 47
Nepal Nepal 0.01 0% 64
Oman Oman 0.03 0% 62
Panama Panama 0.85 0% 16
Peru Peru 0.11 -8.33% 54
Philippines Philippines 0.54 +12.5% 31
Papua New Guinea Papua New Guinea 0.85 +4.94% 16
Qatar Qatar 0.02 -33.3% 63
Romania Romania 0.02 0% 63
Rwanda Rwanda 0.32 -13.5% 39
Saudi Arabia Saudi Arabia 0.04 0% 61
Sudan Sudan 0.1 +11.1% 55
Senegal Senegal 0.06 0% 59
Sierra Leone Sierra Leone 0.7 -24.7% 24
El Salvador El Salvador 0.21 0% 46
Serbia Serbia 0.05 0% 60
South Sudan South Sudan 1.14 -6.56% 14
São Tomé & Príncipe São Tomé & Príncipe 0.04 -20% 61
Suriname Suriname 0.59 +9.26% 29
Slovakia Slovakia 0.02 0% 63
Eswatini Eswatini 7.83 -20.2% 1
Syria Syria 0.01 0% 64
Chad Chad 0.31 -13.9% 40
Togo Togo 0.36 -10% 38
Thailand Thailand 0.5 -1.96% 32
Tajikistan Tajikistan 0.06 0% 59
Timor-Leste Timor-Leste 0.06 0% 59
Tanzania Tanzania 0.81 -20.6% 17
Uganda Uganda 2.07 -6.33% 11
Uruguay Uruguay 0.41 0% 36
Vietnam Vietnam 0.15 -6.25% 50
Yemen Yemen 0.04 0% 61
South Africa South Africa 6.25 -7.13% 2
Zambia Zambia 3.18 -16.3% 8
Zimbabwe Zimbabwe 1.66 -8.79% 12 

                    
# 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.YG.P3'

# 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.YG.P3'

# 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))