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The COVAX program aims to provide global equitable access to life-saving vaccines. Despite calls for increased sharing, vaccine protectionism has limited progress towards vaccine sharing goals. For example, as of April 2022 only ~20% of the population in Africa had received at least one COVID-19 vaccine dose. Here we use a two-nation coupled epidemic model to evaluate optimal vaccine-sharing policies given a selfish objective: in which countries with vaccine stockpiles aim to minimize fatalities in their own population. Computational analysis of a suite of simulated epidemics reveal that it is often optimal for a donor country to share a significant fraction of its vaccine stockpile with a recipient country that has no vaccine stockpile. Sharing a vaccine stockpile reduces the intensity of outbreaks in the recipient, in turn reducing travel-associated incidence in the donor. This effect is intensified as vaccination rates in a donor country decrease and epidemic coupling between countries increases. Critically, vaccine sharing by a donor significantly reduces transmission and fatalities in the recipient. Moreover, the same computational framework reveals the potential use of hybrid sharing policies that have a negligible effect on fatalities in the donor compared to the optimal policy while significantly reducing fatalities in the recipient. Altogether, these findings provide a self-interested rationale for countries to consider sharing part of their vaccine stockpiles. 

In this paper, target tracking is achieved through a variable gain integrator for a system whose dynamics are unknown or uncertain. Towards that, a predictor-based dynamic controller is utilized which, in the case of dynamical systems, is implemented via a simulation subsystem which operates alongside the plant. To relax the need for complete knowledge of the system model by the simulator, we augment the model-based variable gain integrator with a learning approximator. The lookahead simulation defines the control input based on the current approximation of the system, which is improved as the approximator learns. Finally, in order to decrease the usage of the controller’s resources, we implement an event-triggered control strategy. The efficacy of the approach is shown through simulation examples on nonlinear systems.