ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake 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 formulation is intractable in practice.
We present a heuristic approach that enables us to
solve the problem for real-world use-cases. We also
present theoretical bounds for the heuristic method.
Finally, we show that the proposed approach outperforms baseline methods in terms of vaccination
uptake through experimental evaluation. HelpMum
is currently planning a pilot program based on our
approach to be deployed in the largest city of Nigeria, which would be the first deployment of an AI driven vaccination uptake program in the country
and hopefully, pave the way for other data-driven
programs to improve health outcomes in Nigeria.
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 formulation is intractable in practice.
We present a heuristic approach that enables us to
solve the problem for real-world use-cases. We also
present theoretical bounds for the heuristic method.
Finally, we show that the proposed approach outperforms baseline methods in terms of vaccination
uptake through experimental evaluation. HelpMum
is currently planning a pilot program based on our
approach to be deployed in the largest city of Nigeria, which would be the first deployment of an AI driven vaccination uptake program in the country
and hopefully, pave the way for other data-driven
programs to improve health outcomes in Nigeria.
Research Areas
