  How to allocate vaccines over heterogeneous individuals is one of the
important policy decisions in pandemic times. This paper develops a procedure
to estimate an individualized vaccine allocation policy under limited supply,
exploiting social network data containing individual demographic
characteristics and health status. We model spillover effects of the vaccines
based on a Heterogeneous-Interacted-SIR network model and estimate an
individualized vaccine allocation policy by maximizing an estimated social
welfare (public health) criterion incorporating the spillovers. While this
optimization problem is generally an NP-hard integer optimization problem, we
show that the SIR structure leads to a submodular objective function, and
provide a computationally attractive greedy algorithm for approximating a
solution that has theoretical performance guarantee. Moreover, we characterise
a finite sample welfare regret bound and examine how its uniform convergence
rate depends on the complexity and riskiness of social network. In the
simulation, we illustrate the importance of considering spillovers by comparing
our method with targeting without network information.
