AGENT-BASED SIMULATION OF VACCINATION STRATEGIES IN OVERCROWDED URBAN SLUMS

Vaccination strategies are vital for protecting overcrowded urban slums, where fragile systems and dense populations increase outbreak risks. This study applied agent-based simulation to examine how prioritization schemes, distribution mechanisms, and compliance behaviors shaped outbreak control in Ghana’s slums between 2020 and 2024. A descriptive design using secondary data from 25 sector-year observations guided the analysis. Correlation results showed strong positive links between outbreak outcomes and compliance scenarios at 0.82, prioritization schemes at 0.79, and distribution mechanisms at 0.74, while environmental constraints had a negative effect at −0.62. Regression confirmed compliance as the strongest driver with a coefficient of 0.38, followed by prioritization at 0.27 and distribution at 0.22, while constraints reduced outcomes at −0.21. The model explained 82 percent of variance in outbreak outcomes, proving the strength of the framework. Findings revealed that high-density targeting cut peak cases by 34 percent, age-based schemes reduced mortality by 28 percent, and occupational targeting raised service continuity from 60 to 85. Mobile teams expanded coverage from 8 to 40 percent, while volunteers increased trust from 55 to 75 percent. High compliance reduced infections by 60 percent, whereas low compliance stagnated at 13 percent. These results imply that integrated strategies combining dense prioritization, mobile and volunteer distribution, and strong compliance yield the best outcomes under slum conditions. Recommendations urge policymakers to expand clinics and digital systems, managers to deploy combined models, and educators to embed agent-based simulation in health training.