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Title: Effective Social Network-Based Allocation of COVID-19 Vaccines
ABSTRACT We study allocation of COVID-19 vaccines to individuals based on the structural properties of their underlying social contact network. Using a realistic representation of a social contact network for the Commonwealth of Virginia, we study how a limited number of vaccine doses can be strategically distributed to individuals to reduce the overall burden of the pandemic.We show that allocation of vaccines based on individualsโ€™ degree (number of social contacts) and total social proximity time is significantly more effective than the usually used age-based allocation strategy in reducing the number of infections, hospitalizations and deaths. The overall strategy is robust even: (๐‘–) if the social contacts are not estimated correctly; (๐‘–๐‘–) if the vaccine efficacy is lower than expected or only a single dose is given; (๐‘–๐‘–๐‘–) if there is a delay in vaccine production and deployment; and (๐‘–๐‘ฃ) whether or not non-pharmaceutical interventions continue as vaccines are deployed. For reasons of implementability, we have used degree, which is a simple structural measure and can be easily estimated using several methods, including the digital technology available today. These results are significant, especially for resource-poor countries, where vaccines are less available, have lower efficacy, and are more slowly distributed.  more » « less
Award ID(s):
1633028 1916805 1918656 2028004 2027541
NSF-PAR ID:
10403978
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;
Date Published:
Journal Name:
Proceedings of the KDD Health Day (2022)
Page Range / eLocation ID:
4675 to 4683
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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