论文标题
谁应该接种疫苗？通过SIR网络的个性化疫苗分配
Who Should Get Vaccinated? Individualized Allocation of Vaccines Over SIR Network
论文作者
论文摘要
如何将疫苗分配给异质个体是大流行时期的重要政策决定之一。本文制定了一个程序，以估算有限供应下的个性化疫苗分配政策，从而利用包含个人人口统计学特征和健康状况的社交网络数据。我们基于异质相互作用网络模型对疫苗的溢出效应进行建模，并通过最大化估计的社会福利（公共卫生）标准来估算个性化的疫苗分配政策，该标准纳入了溢出。尽管此优化问题通常是NP坚固的整数优化问题，但我们表明SIR结构会导致子模具目标函数，并提供了一种计算有吸引力的贪婪算法，用于近似具有理论性能保证的解决方案。此外，我们表征了有限的样本福利遗憾的束缚，并检查其统一收敛率如何取决于社交网络的复杂性和风险。在模拟中，我们说明了通过将我们的方法与无网络信息的定位进行比较来考虑溢出物的重要性。
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.
