Robust Evaluation of Vaccination Strategies under Epidemiological and Logistical Uncertainty
Keywords:
stochastic SIR model, vaccination strategies, epidemiological uncertainty, logistical uncertainty, cost-effectivenessAbstract
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.References
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