Bayesian Networks in the Context of Immunization Programs in Africa

Bayesian Networks in the Context of Immunization Programs in Africa

Presented at the 8th Annual BayesiaLab Conference on October 26, 2020.


The immunization approach, Expanded Programme on Immunization (EPI), is a powerful public health strategy for improving child survival; the policy for any Ministry of Health is to ensure that as many children as possible receive the full series of vaccines on their national routine immunization schedule.

The overarching goal of that study is to demonstrate (i) the usefulness of the data mining and modeling approaches in the context of an immunization program and, for the communication aspect, (ii) how mapping and analytics capabilities could be used so as to brainstorm and communicate the proposed actionable insights among the decision-makers in mandated coordination bodies at national and regional levels.

The EPI Bayesian Network Model for Ghana displays the available variables (data elements extracted from the District Health Information & Management System) and the arcs, which have been “manually” laid down, thanks to the theory of change, to justify causal assumptions (or the linkages among the variables in the EPI model). Indeed, Public health knowledge is key. Out of these data elements, the number of children vaccinated and the number for the three types of vaccination sessions (fixed, outreach, and at school) are considered specifically.

Thanks to the optimization algorithm, it is possible to lay down that the best solution gears towards increasing the number of fixed and school vaccinations sessions and, lowering the number of outreach vaccinations sessions, but because of the several contextual factors to be considered, any realistic and meaningful concrete decisions should be taken only at district or sub-district levels.

Whatever the diversity and the complexity of the local situation at the sub-district level, we take recourse of the “batch outlier” procedure so as to come up with priority actions.

A Bayesian network can serve as an inference engine, and thus simulate that public health program comprehensively. Through simulation, we can obtain all associations that exist in the EPI program, and, most importantly, we can compute causal effects directly. Overall, strategies to improve vaccination must be percolated top-down up to sub-districts and communities.

Presentation Video

About the Presenter

Raphael Girod is the founder of the MAHA organization. MAHA stands for Mapping & Analytics for Health Activities (opens in a new tab).

Raphael is a project manager by experience, he gained public health skills as (i) health expert in charge of many result-oriented monitoring missions in Asia and Africa, (ii) as health project coordinator based at the Ministry of Health in Guinea, and (iii) as Local Fund Agent Project leader for Global Fund in Burkina-Faso, all these complementary experiences enables him to fully appraise the strategic, financial, epidemiological and impact evaluation stakes in the process of providing reliable information for high-quality programming…The most updated capability relates to modeling and data-mining, thanks to BayesiaLab.

Passionate about data analysis and public health, his current work aims at collating data sources in order to structure complex datasets, to inform indicator measurements in order to support the strengthening of knowledge management related to global health, especially in regards to immunization programs.

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