Robust Evaluation of Vaccination Strategies under Epidemiological and Logistical Uncertainty

Abstract

Infectious disease outbreaks present persistent challenges for public health policy, particularly when key factors such as transmission intensity, vaccine efficacy, and rollout logistics are uncertain. This study develops a stochastic Susceptible-Infectious-Recovered (SIR) framework to conduct a robust evaluation of vaccination strategies under joint epidemiological and logistical uncertainty. Randomness is incorporated into transmission, recovery, and vaccine uptake processes, enabling the model to reflect the variability of real-world epidemic dynamics. Monte Carlo simulations are performed to compare fixed-rate, phased, and threshold-triggered vaccination strategies across a wide range of plausible outbreak scenarios. Sensitivity analysis highlights the dominant influence of transmission rate and vaccine efficacy on epidemic outcomes, while a cost-effectiveness framework balances epidemiological benefits against resource constraints. Results indicate that adaptive, threshold-triggered vaccination strategies consistently reduce worst-case epidemic peaks and improve resilience under uncertainty. These findings provide actionable guidance for policymakers seeking robust, resource-efficient interventions in rapidly evolving epidemic contexts.