Spatially Heterogeneous Vaccine Coverage and Externalities in a Computational Model of Epidemics

Myong-Hun Chang () and Troy Tassier ()
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Myong-Hun Chang: Cleveland State University
Troy Tassier: Fordham University

Computational Economics, 2021, vol. 58, issue 1, No 3, 27-55

Abstract: Abstract We develop an agent-based computational model of epidemics with varying amounts of heterogeneity in regional vaccination coverage. We examine the severity of the resulting epidemics and the externalities created between high and low vaccination regions. The resulting size of the epidemics depends upon the heterogeneity of coverage, the interaction structure in the population, and how close the population is to herd immunity. Holding overall vaccination levels constant, the mean epidemic size increases and a larger level of vaccinations is needed to reach herd immunity when there is greater variability in the regional vaccination rates as long as the vaccination level is sufficiently high. This relationship amplifies when the contacts of individuals in the model have a greater share of inter-regional linkages. However, when inter-regional linkages and vaccination levels are sufficiently low, the mean epidemic size can decrease when there is greater variability in the regional vaccination rates. Further, we find that the magnitude of externalities between regions with different levels of vaccine coverage depends crucially on how close the population is to herd immunity. If population level vaccination coverage is sufficiently high (low) shifting connections from low coverage regions to high coverage regions will decrease (increase) mean epidemic size.

Keywords: Epidemics; Vaccine coverage; Infectious disease; Influenza; Externality; Agent-based model (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10614-019-09918-7

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