Toru Kitagawa, Guanyi Wang
arXiv 7 Dec 2020 · Econometrics · publishedJournal of Econometrics (2021) · 21 citations (OpenAlex)
arXiv:2012.04055 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Nemhauser, G. L., L. A. Wolsey, and M. L. Fisher (1978) An analysis of approximations for maximizing submodular set functions—I | 0.874 | 7 | 2 | 100% |
| 2 | Bach, F (2011) Learning with submodular functions: A convex optimization perspective | 0.737 | 3 | 3 | 67% |
| 3 | Fisher, M. L., G. L. Nemhauser, and L. A. Wolsey (1978) An analysis of approximations for maximizing submodular set functions—II, in | 0.644 | 2 | 2 | 100% |
| 4 | Kitagawa, T. and A. Tetenov (2018) Who should be treated? empirical welfare maximization methods for treatment choice self | 0.644 | 2 | 2 | 100% |
| 5 | Manski, C. F (2004) Statistical treatment rules for heterogeneous populations | 0.644 | 2 | 2 | 100% |
| 6 | Ananth, A (2020) Optimal Treatment Assignment Rules on Networked Populations | 0.585 | 3 | 1 | 100% |
| 7 | Viviano, D (2019) Policy targeting under network interference | 0.585 | 3 | 1 | 100% |
| 8 | Cunningham, W. H (1985) Minimum cuts, modular functions, and matroid polyhedra | 0.511 | 2 | 2 | 50% |
| 9 | Hoeffding, W (1963) Probability inequalities for sums of bounded random variables | 0.511 | 2 | 2 | 50% |
| 10 | Hannan, J (1957) APPROXIMATION TO BAYES RISK IN REPEATED PLAY | 0.511 | 2 | 1 | 100% |
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