Simulation and Mathematical Modeling to Support Decision-Making in Urban Vaccination Strategies: A District-Level Study in Lima, Peru

Abstract

Designing effective vaccination strategies remains a significant challenge for urban health systems, particularly in settings characterized by high population density, operational limitations, and pervasive uncertainty. This study addresses this challenge through a simulation-based and mathematical modeling framework aimed at evaluating and comparing alternative vaccination strategies at the district level. The proposed approach begins with an optimization formulation based on systems of differential equations derived from a stratified compartmental model, which is subsequently transformed through linearization into a linear programming optimization framework. The model integrates demographic characteristics, vaccination coverage levels, and operational assumptions to assess the impact of different scenarios on overall system performance. The methodology is applied to a district within the metropolitan area of Lima, Peru—currently home to more than 11 million inhabitants—enabling the mathematical simulation and statistical evaluation of multiple vaccination strategies under varying degrees of service coverage complexity. The results demonstrate that the proposed framework generates actionable quantitative insights to support public health decision-making, facilitating the identification of strategies that enhance coverage efficiency and improve system-wide performance. Overall, the study underscores the value of analytical and modeling tools as effective decision-support mechanisms for vaccination planning in urban contexts across Latin America.