The Research Consumption is short on interesting academic work.
The great idea
The plan of the Centers for Disease Control and Prevention that receives vaccines and in which order saved almost as many lives and prevented almost as many infections as theoretically perfect spread, according to a new mathematical model we developed to assess COVID-19 damage -inoculation. 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 was decided to divide the U.S. population into four groups for vaccine priority based on age, occupation, living condition, and known risk factors of COVID-19.
Using a new model and supercomputer from Iowa State University, we compared the actual CDC recommendations with 17.5 million possible strategies, which also staggered the launch in up to four phases. To calculate how well a vaccine allocation strategy worked, our model measured total deaths, cases, infections, and years of life lost.
We found that the CDC allocation strategy worked exceptionally well – within 4% of perfect – in all four measures.
According to our model, the CDC’s decisions not to vaccinate children initially and prioritize health care and other essential workers over non-essential workers were both correct. But our model also showed that giving individuals with known risk factors earlier access to vaccines would lead to slightly better outcomes.
No single launch could simultaneously minimize deaths, cases, infections and years of life lost. 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 does it matter
Many other studies have looked at a small number of alternative vaccine countries of COVID-19. Our project incorporated more features of the current pandemic and considered 17.5 million possible strategies. We believe this gives our results more authority.
Our model includes differences in bleeding and susceptibility to coronavirus due to age. It also includes social distancing levels that change over time, as well as varying infectivity rates to account for more contagious viral strains such as the delta variant.
All of this has given us the ability to accurately assess the CDC’s past decisions. But the greater value of our model approach lies in how it could help guide future policy.
By changing model inputs, we were able to show how optimal launch strategies should change due to different vaccine hesitations and for different vaccines that can protect in various ways against infection or death. For countries currently planning vaccination strategies on COVID-19, our model could help decision makers develop the most effective strategies according to their local resources and specifications. And even in the United States, our modeling technique can inform allocation strategies for accelerating shots and future vaccine countries, so that health care administrators can make the best use of limited resources.
What is not yet known
Any model is a simplification of reality. Our model was not responsible for reinfections or changing levels of vaccine hesitation based on socioeconomic status, political ideology, or race. We also assumed that the level of hesitation was constant over time.
Additionally, some important factors about how the coronavirus spreads – such as contact rates between individuals of different age and demographic groups and the contagion of asymptomatic and vaccinated individuals – are still unknown. Better data on these parameters would improve the accuracy of our results.
Now that we have the model built, we can extend it. For example, we can study how decreasing immunity and strengthening shots could affect the spread of the disease. Our computer code is available to the public, and we hope it will guide health care policy makers in the United States and around the world.
This article is reprinted by The Conversation, a nonprofit newsroom dedicated to sharing ideas from academic experts. It was written by: Audrey L. McCombs, Iowa State University and Claus Kadelka, Iowa State University.
The authors do not work for, consult, own shares or receive funding from any company or organization that would benefit from this article, and have not disclosed any relevant submissions beyond their academic appointment.