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Prioritizing High-Risk Individuals for Vaccines May Not Be Necessary, Research Says

2021-01-22 06:00:18
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A new study suggests that prioritizing vaccination of high-risk individuals has only a marginal effect on COVID-19 deaths. In order to achieve significant improvements, it would be better if a very large part of the urban or village population is vaccinated.

In addition, the study found that the benefits of the restrictive measures in place during the first wave of the coronavirus were significantly greater than those of all selective vaccination scenarios. Even if a vaccine is available, social detachment, masks and mobility restrictions will still be the main tools to combat COVID-19.

A research team led by Maurizio Porfiri, Institute professor at NYU Tandon School of Engineering, developed a COVID-19 model for the entire city of New Rochelle, located in Westchester County, New York State, with a population of approximately 80,000. New Richelle experienced one of the first coronavirus outbreaks to be recorded in the United States.

In the newspaper "High-resolution agent-based modeling of COVID-19 diffusion in a small townPublished in Advanced Theory and Simulations, the team trains its system, developed from a single person's resolution, in the city of New Rochelle – one of the first recorded outbreaks in the United States. The model replicates, geographically and demographically, the urban structure and presents a high-resolution representation of the epidemic at an individual level, taking into account both physical locations and unique characteristics of communities, such as human behavior trends or local mobility patterns.

Porfiri said focusing on a city the size of New Rochelle was crucial to the study, as most cities in the US have similar population sizes and concentrations. “We chose New Rochelle not only because of its place in the COVID timeline, but also because agent-based modeling for mid-sized cities is relatively unexplored, despite the US being largely made up of such cities and small towns,” he said.

The model contains detailed elements of pandemic distribution within a statistically realistic population. In addition to testing, treatment and vaccination options, the model also takes into account the burden of other diseases with symptoms similar to COVID-19. The model can be used in various ways to detect and limit the spread of the virus. Porfiri said the model is unique in that it can be used to explore different testing approaches – in hospitals or drive-through facilities – as well as vaccination strategies.

Vaccination study

In their vaccination simulation analyzing potential 'what-if' questions, the researchers compared the impact of vaccination on a select group of vulnerable individuals, including school staff, nursing home workers and residents, and the total of 2,000 hospital workers in the city. a randomly selected group of 2,000 individuals and 20,000 randomly selected individuals out of the 80,000 people living in New Rochelle.

They ran six sets of simulations with vaccinations of: hospital staff only, school staff only, retirement home staff only, retirement home residents only, randomly selected fraction of the population, the same size as the number of hospital workers, and about a quarter of the city, which equates to ten times the number of hospital employees.

The results of these six 'what-if' scenarios showed in part:

  • The importance of closures is clear: the number of infections and fatalities is many times greater than reality.
  • The vaccination of hospital staff showed only minor differences from the vaccination of an equal number of individuals in the general population. Similar observations can be made about targeted immunization of other vulnerable groups.
  • Only when the elderly in retirement homes are vaccinated, significant differences in mortality occur.
  • While both targeted and random approaches had some effect on the spread of COVID-19, mass immunization was the only truly impactful strategy. This finding is consistent with predictions of "herd immunity," where effective COVID-19 containment can only be achieved if the vast majority of the population obtains immunity.

According to the researchers, the results suggest “that prioritizing vaccination of high-risk individuals has a marginal effect on the number of COVID-19 deaths. It is predictable that a much greater improvement will be recorded when a quarter of the city is vaccinated. Importantly, the benefits of the restrictive measures in place during the first wave far outweigh those of any of these selective vaccination scenarios. "

“We believe that decision-making by government agencies can benefit from this model, not only because it is 'open source', but also because it provides 'fine-grained' resolution at the level of the individual and a wide range of functions, ”said Porfiri.

The research team consisted of Zhong-Ping Jiang, professor of electrical engineering and computer technology; 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 Università Cattolica del Sacro Cuore and Alessandro Rizzof from Turin Polytechnic, and visiting professor of mechanical and aerospace engineering at NYU Tandon.

The work was supported in part by the National Science Foundation, Compagnia di San Paolo, MAECI, the European Research Council and the Netherlands Organization for Scientific Research.

The World Health Organization reported that on January 19, 2021, there are approximately 94 million cases of COVID-19 worldwide, with more than 2 million deaths. According to Porfiri, in the light of these numbers – driven in part by an aggressive resurgence of the virus in the US – "health authorities face a weak balancing act: how to implement policies to protect citizens while minimizing quality. life and local economies, especially in smaller cities and towns, where a shortage of intensive care units and tight budgets further thin the thin line between precautionary measures and normalcy. ”

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