Audrey L. McCombs and Claus Kadelka
The Centers for Disease Control and Prevention’s plan for who gets vaccines and in what order, saved almost as many lives and prevented almost as many infections as a theoretically perfect rollout, according to a new mathematical model we’ve developed to assess rollout of COVID-19 grafts in the United States
In December 2020, with a limited number of vaccines available, the CDC had to make a difficult decision: Who gets the COVID-19 vaccines first? It decided to divide the US population into four groups for vaccine prioritization based on age, occupation, living conditions and known COVID-19 risk factors.
Using a new model and an Iowa State University supercomputer, we compared the real CDC recommendations with 17.5 million possible strategies that also shifted the rollout into up to four phases. To calculate how well a vaccine allocation strategy worked, our model measured total deaths, cases, infections, and lost life years.
We found that the CDC allocation strategy performed exceptionally well – within 4% of perfect – in all four targets.
According to our model, the CDC’s decisions not to vaccinate children in the first place and to prioritize health care and other important workers over non-essential workers were both correct. But our model also showed that giving people with known risk factors earlier access to vaccines would have led to slightly better results.
No single rollout was able to simultaneously minimize deaths, cases, infections and lost life years. For example, the strategy that minimized deaths led to a higher number of cases. Given these limitations, the CDC plan did a good job of balancing the four goals of vaccination and was particularly good at reducing deaths.
Why it matters
Many other studies have looked at a small number of alternative rollouts of COVID-19 vaccines. Our project incorporated several characteristics of the current pandemic and considered 17.5 million possible strategies. We believe that this gives our results more authority.
Our model includes differences in the severity of the disease and susceptibility to coronavirus due to age. It also incorporates levels of social distance that change over time, as well as variable infectivity to account for more infectious virus strains such as the delta variant.
All of this gave us the opportunity to accurately assess the CDC’s previous decisions. But the greater value of our modeling approach lies in how it can help manage future policy.
By changing model inputs, we were able to show how optimal deployment strategies should change given different vaccination dust and for different vaccines that can protect in different ways against infection or death. For countries currently planning COVID-19 vaccination strategies, our model can help decision makers develop the most effective strategies given their local resources and specifications. And even in the United States, our modeling technology can inform booster shot allocation strategies and future vaccine deployments so healthcare administrators can make the most of limited resources.
What you still do not know
Every model is a simplification of reality. Our model did not take into account re-infections or varying levels of vaccination dust based on socioeconomic status, political ideology, or race. We also assumed that the level of hesitation was constant over time.
In addition, some important factors for the spread of coronavirus – such as contact rates between individuals of different ages and demographic groups and the infectivity of asymptomatic and vaccinated individuals – are still unknown. Better data on these parameters would improve the accuracy of our results.
Now that we’ve built the model, we can expand it. For example, we can study how declining immunity and booster shots can affect the spread of the disease. Our computer code is available to the public and we hope it will guide health politicians in the United States and around the world.
Audrey L. McCombs is a Ph.D. graduate in ecology and statistics from Iowa State University. Claus Kadelka is an assistant professor of mathematics at Iowa State University. This article is republished from The Conversation under a Creative Commons license. Read the original article.