After comparing 17.5 million strategies, researchers validate recommendations for CDC vaccine deployment • News Services • Iowa State University

Ames, Iowa — A year ago, the US Centers for Disease Control and Prevention faced major problems as the number of cases of COVID-19 surged and the supply of newly developed vaccines was limited. Are you a student living in a college dormitory or a person with chronic kidney disease? Grandma or teacher? In addition to the goal of reducing overall infection and mortality, the CDC aimed to prevent hospitals from being overwhelmed and to ensure equitable access to the COVID-19 vaccine.

Researchers at Iowa State University were part of a team that evaluated the 17.5 million strategies that the CDC might have recommended to states, territories, tribes, and local governments for the deployment of the COVID-19 vaccine last winter.so Newly published paperResearchers generally validate the CDC’s final recommendations, but the mathematical model developed by the team also highlights some minor improvements. This work may help inform the design of future vaccination strategies in the United States and abroad.

The CDC recommended four staggered priority groups for the COVID-19 vaccine.

• Phase 1a It included medical personnel and residents of long-term care facilities.
• Phase 1b Includes the forefront of non-health care Key worker (Examples: police officers, firefighters, childcare workers) and people over the age of 75.
• Phase 1c Other essential workers (bus drivers, bank tellers, etc.), 16-64 years old Increased risk of severe COVID-19 disease And 65-74 years old.
• Phase 2 Includes 16-64 years without high-risk conditions or comorbidities.

“The CDC strategy worked very well, especially in preventing deaths across age groups, when compared to all other possible strategies,” said Klaus Kadelka, an assistant professor of mathematics and corresponding author. Says. Papers published in PLOS ONE.. “Our study shows that the high priority of CDC’s front-line key workers, the elderly, and those with underlying health factors is the death of COVID-19, while reducing the overall number of cases. It was a very effective strategy to control the rate. “

To evaluate the CDC’s recommendations, Kadelka and the research team developed a mathematical model that incorporates four staggered phases of the institution for vaccine deployment and 17 subpopulations based on factors such as age, living conditions, and occupation. I built it. Individuals are “prone to virus infection”, “fully vaccinated”, “currently infected”, “infected but without symptoms of COVID-19”, “recovered”, etc. 20 It was classified into one of the categories of. Researchers also incorporated some key features of the COVID-19 pandemic, including vaccine hesitation, social distance levels based on the current number of cases in the United States, and different infection rates for different viral variants. ..

“I ran the model 17.5 million times. ISU supercomputer, And for each run, we recorded several outcome indicators and finally compared them: predicted mortality, predicted number of cases, etc. “Kadelka said.

When the child was vaccinated outside of the final stages of vaccine deployment, the model results were not always optimal. Researchers said the CDC recommendation could have been optimal if more individuals with known COVID-19 risk factors were prioritized over people in a cohort without health risks. .. However, the benefits are modest (that is, less than 1% deaths and years of death, 4% fewer cases and infections), and Kadelka says the model does not consider potential logistics challenges. Said.

“We don’t know enough about the situation in nursing homes to see how easy it is to distinguish which residents have greater risk factors that are at the forefront of the vaccination line. This can be done with a mathematical model, but it can be difficult in practice, “added Kadelka.

One of the reasons this model is unique, according to Kadelka, is that it takes into account the extent to which the vaccine prevents someone from getting infected, developing symptoms, or transmitting the virus. All of these can change over time and in some cases. Specific vaccine. Researchers have shown that ideal vaccination strategies are sensitive to these parameters, but these are still largely unknown.

Md Rafiul Islam, a postdoctoral fellow in Kadelka’s group and lead author of the paper, said mathematical models could help design current and future vaccination strategies.

“Our model helps identify the optimal vaccine allocation strategy and can be easily extended to answer questions related to booster allocation in the face of weakened immunity and increased viral variability. “Islam says.

Kadelka said: What are the consequences when a decision is made regarding who was first vaccinated? “

Vaccine strategy development is complex and there are always trade-offs between conflicting goals, such as minimizing mortality and incidence. However, Kadelka believes that mathematical models such as those created by him and his colleagues help ensure that life-saving vaccines have the greatest impact.