UNICEF Implements Machine Learning for Immunisation in Africa

The United Nations Children’s Fund (UNICEF) implements machine learning to speed up immunisation programs across Central and West Africa.

This initiative is part of the Reach the Unreached (RTU) pilot, which was launched in Cameroon, Chad, Guinea, and Mali.

The program leverages machine learning technologies to break down population data and estimate vaccination coverage more accurately.

Read Also: UNICEF pledges support for Jigawa’s health system 

RtU is working in collaboration with the Frontier Data Network (FDN). UNICEF officials highlight that this approach has enabled regional and country office teams to map over 1.1 million unreached children.

The goal is to provide participating countries with detailed information to identify regions at risk of falling behind and address child rights inequities, starting with immunisation and birth registration.

“While the spread of granular population estimates and vaccination coverage datasets is beneficial and potentially game-changing, their impact on improving health programming and outcomes will only be realised if integrated into existing information systems and decision-making processes at the country level,” said Rocco Panciera, UNICEF geospatial health specialist.

Manuel Garcia-Herranz, FDN’s principal researcher, emphasised that without technology, experts lack insights into how data bias and algorithmic inequalities affect combined population estimation and vaccination coverage models.

“Even for single models, understanding performance across different socioeconomic contexts is challenging,” Garcia-Herranz said.

ITWeb Africa