Evaluating the Long-Term Impact and Predictive Analytics of COVID-19 Booster Vaccination Uptake

Kancharla, Deepika (2025) Evaluating the Long-Term Impact and Predictive Analytics of COVID-19 Booster Vaccination Uptake. Masters thesis, Dublin, National College of Ireland.

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Abstract

The difference in supporting vaccines against COVID-19 has posed a major challenge on the planning of the health system since the eroding immunity and new variants constantly threaten the security of the entire population. The proposed study is concerned with the need to have transparent, interpretable and reproducible forecasting models, which can be used to assist policymakers in predicting the effect of booster demand on hospital burden. The study constructs an integrated predictive model using Linear Regression and Random Forest models augmented with SHAP-based interpretations with the help of the publicly available multi-country data focused on WHO, OWID, and Kaggle. The models determine demographic, behavioural and policy factors that are important determinants of booster uptake and predict coverage in relation to ultimate trends in hospitalisation. The findings indicate that Random Forest is better at making predictions whereas Linear Regression offers better interpretability so that the policy can be used. The results add new modelling dimensions that bridge current state-of-the-art modelling with open-data reproducibility with the forecast of health outcomes. In practice, the research can provide governments with a scalable tool to optimise vaccine planning, but long-term prediction and quality of the data is still not addressed.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Niculescu, Hamilton UNSPECIFIED
Subjects:	R Medicine > Diseases > Outbreaks of disease > Epidemics > COVID-19 Pandemic, 2020- R Medicine > Healthcare Industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning R Medicine > RA Public aspects of medicine > Public Health System
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	07 Sep 2026 13:10
Last Modified:	07 Sep 2026 13:10
URI:	https://norma.ncirl.ie/id/eprint/9866

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