ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake in Nigeria
Vineet Nair, Kritika Prakash, Michael Wilbur, Aparna Taneja, Corinne, Namblard, Oyindamola Adeyemo, Abhishek Dubey, Abiodun Adereni, Milind Tambe,, Ayan Mukhopadhyay

TL;DR
This paper introduces ADVISER, an AI-driven framework using heuristic optimization to improve vaccination rates in Nigeria, demonstrating superior results over baseline methods and paving the way for data-driven health interventions.
Contribution
We develop a novel heuristic optimization framework, ADVISER, for allocating health interventions to maximize vaccination uptake in Nigeria, including theoretical bounds and real-world experimental validation.
Findings
Outperforms baseline methods in increasing vaccination rates
Provides theoretical bounds for the heuristic approach
Enables deployment of AI-driven vaccination programs in Nigeria
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 under-developed 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. We collaborate with HelpMum, a large non-profit organization in Nigeria to design and optimize the allocation of heterogeneous health interventions under uncertainty to increase vaccination uptake, the first such collaboration in Nigeria. Our framework, ADVISER: AI-Driven Vaccination Intervention Optimiser, is based on an integer linear program that seeks to maximize the cumulative probability of successful vaccination. Our optimization…
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Taxonomy
TopicsVaccine Coverage and Hesitancy
