In this Perspective, the authors used a network simulation model to illustrate five different vaccination strategies for coronavirus disease 2019 (COVID-19), showing that vaccinating the most-active members of the population might be the best way to save lives overall and still protect the vulnerable.

Recent models of COVID-19 vaccination have tended not to take into account the person-to-person contact structure that results in the disease's spread. The authors used a model based on a realistic contact network (derived from a data set of 2.2 billion mobile device location points) to run five different vaccination models. What varied between each case was the number of contacts of the people who were vaccinated. The base case assumes no vaccination; the low-contact model vaccinates those with the fewest contacts (corresponding to those already identified as high risk who are able to limit contacts); the uniform model vaccinates 15 percent of the population at random; and the final two models vaccinate those with the most contacts—the high-contact model vaccinates the 15 percent of people with the most contacts, and the high-contact imperfect model vaccinates one-half of the 30 percent of people with the most contacts.

The authors found that the high-contact imperfect strategy is at least as effective (and probably more effective) at protecting the vulnerable than direct vaccination and indicates that the United States might be able to provide more protection for vulnerable people by vaccinating people with many contacts than by vaccinating vulnerable people directly.

Tim Gulden is a policy researcher at the RAND Corporation and a CISSM research associate. His research focuses on modeling complex systems in the context of data as well as more general policy analysis including cost/benefit analysis, organizational design, and cybersecurity policy.

Gulden is the past president of the Computational Social Science Society of the Americas (CSSSA). Before coming to RAND, he was a research professor with the Center for Social Complexity and Department of Computational Social Science at George Mason University. His PhD is from the University of Maryland School of Public Policy where he explored agent-based modeling as a tool for policy analysis. He has held research positions at the MITRE Corporation, the Center for International and Security Studies at Maryland (CISSM), and the Brookings Institution's Center on Social and Economic Dynamics (CSED). He attended the Santa Fe Institute's Complex Systems Summer School in 2002. During the 1990s he was the technical director of the GIS program for Westchester County, New York.

Join WebEx meeting:

Meeting # (access code): 161 962 8856

Meeting password: rQapg4SdH83

Join by video system: Dial 1619628856@gmu.webex.com

Or dial 173.243.2.68 and enter the meeting #

Join by phone: +1-415-655-0003 US Toll

+1-202-860-2110 United States Toll (Wash., D.C.)

Access code: 161 962 8856

All are welcome to apply!