MATHEMATICAL AND STATISTICAL MODELING OF INFECTIOUS DISEASE TRANSMISSION DYNAMICS IN URBAN AND RURAL COMMUNITY SETTINGS

Abstract

This study investigates the impact of increased vaccination coverage on peak infection rates across urban and rural settings using SEIR and SIR epidemiological modeling frameworks. Baseline analysis revealed significantly higher peak infection rates in urban areas (23.4%) compared to rural areas (14.7%), primarily due to higher population densities and contact rates. Scenario simulations demonstrated that increasing vaccination coverage by 10% reduced urban peaks to 18.7% and rural peaks to 11.9%, while a 20% increase further lowered these rates to 14.5% and 9.4%, respectively. Sensitivity analysis identified R0R₀R0​ and contact rate as the most influential parameters affecting total infections, highlighting the importance of targeted interventions. The SEIR model consistently provided more accurate projections than the SIR model, supported by lower RMSE and AIC values. These findings underscore the value of vaccination campaigns as a primary control measure, especially in high-density areas. Policy recommendations include integrating real-time R0R₀R0​ monitoring, expanding immunization programs, and employing public awareness initiatives to reduce contact rates. The research further suggests that hybrid modeling approaches incorporating socio-behavioral and environmental factors could enhance predictive accuracy. Overall, this study contributes to evidence-based public health strategies by demonstrating the quantifiable benefits of increased vaccination coverage in mitigating disease peaks and optimizing resource allocation.