Optimizing spatial allocation of seasonal influenza vaccine under temporal constraints

Prophylactic interventions such as vaccine allocation are some of the most effective public health policy planning tools. The supply of vaccines, however, is limited and an important challenge is to optimally allocate the vaccines to minimize epidemic impact. This resource allocation question (which we refer to as VaccIntDesign) has multiple dimensions: when, where, to whom, etc. Most of the existing literature in this topic deals with the latter (to whom), proposing policies that prioritize individuals by age and disease risk. However, since seasonal influenza spread has a typical spatial trend, and due to the temporal constraints enforced by the availability schedule, the when and where problems become equally, if not more, relevant. In this paper, we study the VaccIntDesign problem in the context of seasonal influenza spread in the United States. We develop a national scale metapopulation model for influenza that integrates both short and long distance human mobility, along with realistic data on vaccine uptake. We also design GreedyAlloc, a greedy algorithm for allocating the vaccine supply at the state level under temporal constraints and show that such a strategy improves over the current baseline of pro-rata allocation, and the improvement is more pronounced for higher vaccine efficacy and moderate flu season intensity. Further, the resulting strategy resembles a ring vaccination applied spatiallyacross the US.Author summary: Annual vaccination campaigns continue to be one of the prime measures which help alleviate the burden of seasonal influenza. Due to production and logistic constraints, there is a need for prioritization policies associated with vaccine deployment. While there is general consensus on age-based or risk-based prioritization, spatial optimization of vaccine allocation has not yet been explored in sufficient detail. In order to do this, we develop a mechanistic model of influenza spread across the United States, and propose a greedy mechanism for spatial optimization. We test the methodology on different realistic scenarios with temporal constraints on vaccine production.