Deploying ADVISER: Impact and Lessons from Using Artificial Intelligence for Child Vaccination Uptake in Nigeria

TL;DR

This paper reports on the deployment of ADVISER, an AI-driven tool to optimize vaccination efforts in Nigeria, demonstrating significant impact on increasing vaccination rates and reducing child mortality.

Contribution

The paper introduces the first successful deployment of an AI-enabled intervention optimizer for vaccination in Nigeria, showcasing real-world impact and operational lessons.

Findings

Increased vaccination coverage in Oyo, Nigeria.

Reduction in child mortality rates due to optimized vaccination.

Operational insights from deploying AI in a low-resource setting.

Abstract

More than 5 million children under five years die from largely preventable or treatable medical conditions every year, with an overwhelmingly large proportion of deaths occurring in underdeveloped countries with low vaccination uptake. One of the United Nations' sustainable development goals (SDG 3) aims to end preventable deaths of newborns and children under five years of age. We focus on Nigeria, where the rate of infant mortality is appalling. In particular, low vaccination uptake in Nigeria is a major driver of more than 2,000 daily deaths of children under the age of five years. In this paper, we describe our collaboration with government partners in Nigeria to deploy ADVISER: AI-Driven Vaccination Intervention Optimiser. The framework, based on an integer linear program that seeks to maximize the cumulative probability of successful vaccination, is the first successful deployment of an AI-enabled toolchain for optimizing the allocation of health interventions in Nigeria. In this paper, we provide a background of the ADVISER framework and present results, lessons, and success stories of deploying ADVISER to more than 13,000 families in the state of Oyo, Nigeria.