Prioritizing High-Risk Individuals for Vaccines May Not Be Necessary, Research Says
A new study suggests that prioritizing vaccination of high-risk individuals has only a marginal effect on the number of COVID-19 deaths. To obtain significant improvements, it would be better if a very large fraction of a city or town population is vaccinated.
What’s more, the research found that the benefits of the restrictive measures in place during the first coronavirus wave greatly surpass those from any of the selective vaccination scenarios. Even with a vaccine available, social distancing, masks and mobility restrictions will still be key tools to fight COVID-19.
A research team led by Maurizio Porfiri, Institute Professor at the NYU Tandon School of Engineering, developed a COVID-19 model for the entire town of New Rochelle, located in Westchester County in New York State, which has a population of about 80,000. New Richelle experienced one of the first coronavirus outbreaks registered in the United States.
In the paper “High-Resolution Agent-Based Modeling of COVID-19 Spreading in a Small Town,” published in Advanced Theory and Simulations, the team trains its system, developed at the resolution of a single individual, on the city of New Rochelle — one of the first outbreaks registered in the United States. The model replicates, geographically and demographically, the town structure and superimposes a high-resolution representation of the epidemic at the individual level, considering physical locations as well as unique features of communities, like human behavioral trends or local mobility patterns.
Porfiri said that focusing on a city of New Rochelle’s size was crucial to the research because most cities in the U.S. have comparable population sizes and concentrations. “We chose New Rochelle not only because of its place in the COVID timeline, but because agent-based modelling for mid-size towns is relatively unexplored despite the U.S. being largely composed of such towns and small cities,” he said.
The model incorporates detailed elements of pandemic spread within a statistically realistic population. Along with testing, treatment, and vaccination options, the model also accounts for the burden of other illnesses with symptoms similar to those of COVID-19. The model has various potential uses in tracking and mitigating the spread of the virus. Porfiri said the model is unique in that it could possibility be used to explore different testing approaches — in hospitals or drive-through facilities— as well as vaccination strategies.
In their vaccination simulation analyzing prospective “what‐if” questions, the researchers compared the impact of vaccination of select group of vulnerable individuals, including school employees, retirement home employees and residents, and the totality of the two thousand hospital employees in the town, a randomly selected group of 2000 individuals, and 20,000 randomly selected individuals out of the 80,000 people living in New Rochelle.
They performed six sets of simulations with vaccinations of: hospital employees only, school employees only, retirement home employees only, retirement home residents only, randomly selected fraction of the population, with the same size as the number of hospital employees, and about a quarter of the town, corresponding to ten times the number of hospital employees.
The results from these six “what‐if” scenarios showed in part:
- The importance of closures is evident, with numbers of infections and fatalities exceeding reality many times.
- The vaccination of hospital employees resulted in only minor differences compared with the vaccination of an equivalent number of individuals among the general population. Similar observations can be made about targeted immunization of other vulnerable groups.
- Significant differences only occur in mortality when vaccinating the elderly residents of retirement homes.
- Although both targeted and random approaches had some effect on COVID‐19 spread, massive immunization was the only truly impactful strategy. This finding is consistent with “herd immunity” predictions where effective containment of COVID‐19 can only be achieved with the large majority of the population acquiring immunity.
According to the researchers, the results “suggest that prioritizing vaccination of high‐risk individuals has a marginal effect on the count of COVID‐19 deaths. Predictably, a much more significant improvement is registered when a quarter of the town is vaccinated. Importantly, the benefits of the restrictive measures in place during the first wave greatly surpass those from any of these selective vaccination scenarios.”
“We think decision making by public authorities could benefit from this model, not only because it is ‘open source,’ but because it offers a ‘fine-grain’ resolution at the level of the individual and a wide range of features,” noted Porfiri.
The research team included Zhong-Ping Jiang, professor of electrical and computer engineering; post-docs Agnieszka Truszkowska, who led the implementation of the computational framework for the project, and Brandon Behring; and graduate student Jalil Hasanyan; as well as Lorenzo Zino from the University of Groningen, Sachit Butail from Southern Illinois University, Emanuele Caroppo from the Università Cattolica del Sacro Cuore, and Alessandro Rizzofrom Turin Polytechnic, and visiting professor of mechanical and aerospace engineering at NYU Tandon.
The work was partially supported by the National Science Foundation, Compagnia di San Paolo, MAECI, the European Research Council, and the Netherlands Organisation for Scientific Research.
The World Health Organization reported that as of January 19, 2021, there are approximately 94 million cases of COVID-19 globally, with over 2 million deaths. According to Porfiri, in the face of these numbers — driven in part by an aggressive resurgence of the virus in the U.S. — “health authorities face a tenuous balancing act: how to enact policies to keep citizens safe while doing the least possible damage to quality of life and local economies, especially in smaller cities and towns, where short supply of intensive care units and tight budgets make the thin line between precautionary measures and normalcy even thinner.”
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