Abstract
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.
Highlights
Infectious diseases are the largest cause of human mortality worldwide, leading to over 13 million deaths a year [1]
Respiratory diseases alone account for a large fraction of these infections—CDC reports that the buden of illness during 2017-18 influenza season was high in the United States, with an estimated 48.8 million illnesses and 959,000 hospitalizations [33], higher than any season since the 2009 pandemic
The vaccine allocation problem is temporally constrained by the production and availability schedule, while most prior studies on epidemic interventions have primarily focused on static allocation before the start of the epidemic [2]
Summary
Infectious diseases are the largest cause of human mortality worldwide, leading to over 13 million deaths a year [1]. Controlling the spread of infectious diseases, especially influenza, remains an important priority for local, state, and federal governments in the US and countries worldwide. Pharmaceutical interventions (PI) such as the use of prophylactic vaccinations and anti-viral drugs remain one of the most effective methods for controlling the spread of infectious diseases, e.g., [2], [3]. These interventions are constrained by limited resource supply and the high logistics cost of delivering them over a large geographical region. These limitations have been an actively studied topic in public health policy research. While the authors define possible alternatives for the pro-rata vaccine distribution using axiomatic Operations Research (OR) models, in this paper, we adopt a simulation optimization approach, by using a mechanistic model of influenza spread across the US
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