Machine learning is being used by the United Nations Children’s Fund (UNICEF) to speed up vaccination campaigns in Central and West Africa.
This is part of the Reach the Unreached (RtU) pilot program, which was started in Guinea, Mali, Cameroon, and Chad.
In order to estimate vaccination coverage, the algorithm breaks down population data using machine learning technology.
The Frontier Data Network (FDN) is working with RtU.
In order to give participating countries an extra, detailed source of information to identify local geographies at risk of falling behind and to uncover and investigate child rights inequities, starting with immunisation and birth registration, UNICEF officials explain that colleagues in the regional and country offices have used this approach to map over 1.1 million unreached children.
Without technology, experts would not be able to understand how algorithmic inequities and data bias impact combined population estimation and vaccine coverage models, according to Manuel Garcia-Herranz, lead researcher at FDN.
“It is difficult to understand performance across various socioeconomic contexts, even for single models,” Garcia-Herranz stated.
