Master of Science in Data Science and Analytics

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11951/1204

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Browsing Master of Science in Data Science and Analytics by Subject "immunization coverage"

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    Data-driven precision public health: leveraging machine learning to track and reduce zero-dose and partially vaccinated children in Nakifuma, Uganda
    (Uganda Christian University, 2026) Kenneth Michael Ogwok
    Despite global progress, 14.3 million infants remain zero-dose (ZD) and 5.6 million are partially vaccinated (PV) worldwide (World Health Organization, 2024). In Uganda, where full immunization coverage stands at only 54% (Uganda Bureau of Statistics, 2022), precision public health approaches are urgently needed. This study applies data science to develop a community-level risk profiling framework in a resource-limited Ugandan setting. This study aimed to: (1) identify socio-demographic, health system, and behavioral factors distinguishing ZD, PV, and fully immunized (FI) children; (2) develop and validate machine learning (ML) models predicting vaccination status; and (3) propose data-driven interventions to increase FI coverage.A mixed-methods, cross-sectional study sampled 115 children and their caregivers under five in Nakifuma Sub-county. For objective one, 35 variables were analyzed using chi-square and Mann-Whitney U tests to identify significant predictors. For objective two, four supervised ML algorithms were trained on a stratified 70:30 split and evaluated using precision, recall, F1-score, and AUC. For objective three, validated model-derived risk scores informed targeted, parish-level interventions.The presence of ZD children (10.4%) was associated with negative attitudes of health workers (p=0.013), waiting time >60 minutes (p=0.021), importance of vaccines (p=0.018), and non-parent caregivers (p=0.026). The presence of PV children (40.9%) was associated with increasing child age (p<0.001) and vaccine stock-out (p=0.031), while FI children (48.7%) possessed vaccination cards (p=0.005). The best-performing algorithm was Random Forest, with an F1-score of 0.97 for ZD, 0.74 for PV, and 0.94 for FI. The clustering of ZD/PV children beyond 2 km from health facilities was used for designing a three-tier intervention matrix for sensitizing health workers, supply chain interventions, and SMS reminders.ML models were effective in triaging zero-dose, partially vaccinated, and fully immunized children. The precision public health strategy has immense scope for achieving 90% full immunization by 2030 in Uganda.