Research Article
Important Predictors for Covid-19 Vaccine Hesitation
@INPROCEEDINGS{10.1007/978-3-031-86493-3_26, author={Mireille Fangueng and Mamadou Thiongane and Idrissa Sarr and Bitsha-kitime D. Kabkia}, title={Important Predictors for Covid-19 Vaccine Hesitation}, proceedings={Innovations and Interdisciplinary Solutions for Underserved Areas. 7th International Conference, InterSol 2024, Dakar, Senegal, July 3--4, 2024, Proceedings}, proceedings_a={INTERSOL}, year={2025}, month={4}, keywords={Bernoulli Mixture Model Covid-19 Vaccine Hesitancy}, doi={10.1007/978-3-031-86493-3_26} }- Mireille Fangueng
Mamadou Thiongane
Idrissa Sarr
Bitsha-kitime D. Kabkia
Year: 2025
Important Predictors for Covid-19 Vaccine Hesitation
INTERSOL
Springer
DOI: 10.1007/978-3-031-86493-3_26
Abstract
Hesitation to take the Covid-19 vaccine is one of the main obstacles to the establishment of a general vaccination program that would quickly achieve mass immunity. Identifying the human and societal factors that lead to hesitancy toward the Covid-19 vaccine can be very useful in raising awareness about vaccine acceptance. In this work, we are interested in finding these factors for the African universities population (students and professors). Surveys are conducted in several universities and some information that we believe may influence vaccine hesitancy, vaccine acceptance, and vaccine rejection are collected from individuals in this community. Three classes of people are observed in these data: the vaccinated, the non-vaccinated, and the hesitant. We propose a Bernoulli Mixture Model with conditional class dependency that can estimate the importance candidate predictor variables for a class. We used this model and determined the most important variables to predict Covid-19 vaccine hesitancy in the study population.
