Comparing 17.5 Million Options Shows CDC Got COVID-19 Vaccine Rollout Right

A new model of 17.5 million possible strategies generally validates the US Center for Disease Control and Prevention’s recommendations to state, territorial, tribal, and local governments for last winter’s rollout of COVID-19 vaccines.

The research also highlights some minor improvements. The work could help inform the design of future vaccination strategies in the US and abroad.

A year ago, amid surging COVID-19 cases and a limited supply of newly developed vaccines, the CDC faced a big question: Who should be at the front of the line to get a vaccine? Students living in college dorms or people living with chronic kidney disease? Grandmas or teachers?

Along with goals of reducing overall infections and deaths, the CDC aimed to prevent hospitals from getting overwhelmed and ensure equitable access to COVID-19 vaccines.

The CDC recommended four staggered priority groups for COVID-19 vaccines:

• Phase 1a included health care personnel and residents of long-term care facilities.
• Phase 1b included non-health care frontline essential workers (e.g. police officers, firefighters, child care workers), and people 75 and older.
• Phase 1c included other essential workers (e.g. bus drivers, bank tellers), 16-64 year-olds with increased risk of severe COVID-19 disease, and 65-74 year-olds.
• Phase 2 included 16-64 year-olds without high risk conditions or comorbidities.

“The CDC strategy did really well when we compared it to all the other possible strategies, especially in preventing deaths across age groups,” says Claus Kadelka, an assistant professor of mathematics at Iowa State University and the corresponding author of the paper published in PLOS ONE.

“Our research shows the CDC’s higher prioritization of frontline essential workers, people in older age groups, and people with underlying health factors was a highly effective strategy for curbing COVID-19 mortality, while keeping overall case numbers at bay.”

To evaluate the CDC’s recommendations, Kadelka and the research team built a mathematical model that incorporated the agency’s four staggered phases for a vaccine rollout and 17 sub-populations based on factors like age, living conditions, and occupation.

Individuals fell into one of 20 categories, such as “susceptible to the virus,” “fully vaccinated,” “currently infected,” “infected but without COVID-19 symptoms,” and “recovered.” The researchers also incorporated several important characteristics of the COVID-19 pandemic, such as vaccine hesitancy, social distancing levels based on current caseloads in the US, and different infection rates for different virus variants.

“We ran the model 17.5 million times on the ISU supercomputer, and for each run, we recorded and finally compared several outcome metrics: predicted number of deaths, predicted number of cases, and so on,” Kadelka says.

Vaccinating children in any but the last phase of the vaccine rollout always led to a non-optimal outcome in the model. The researchers says the CDC’s recommendations could have been optimal if more individuals with known COVID-19 risk factors had been prioritized over people in their cohort without health risks.

However, the gains would have been small (i.e. less than 1% fewer deaths and overall years of life lost, and 4% fewer cases and infections). Kadelka says the model does not take into account possible logistical challenges.

“We don’t know enough about the situation in nursing homes to know how easy it would be to distinguish which residents have greater risk factors that would put them at the front of the vaccination line. That’s something you can do in a mathematical model, but it could be hard in practice,” Kadelka adds.

Part of what makes the model unique is that it takes into account the extent to which a vaccine prevents someone from getting infected, developing symptoms, and passing the virus on to others, all of which can change over time, or even vary depending on the particular vaccine, Kadelka says.

The researchers show that the ideal vaccination strategy is sensitive to these parameters, which are still mainly unknown.

The mathematical model could help inform the design of current and future vaccination strategies, says lead author Md Rafiul Islam, a postdoc in Kadelka’s group.

“Our model is useful to identify an optimal vaccine allocation strategy and can be easily expanded to answer questions related to booster allocation in the face of waning immunity and increasing virus variability,” Islam says.

“If the (SARS-CoV-2) virus mutated enough that it rendered the current vaccines ineffective or we have a new pandemic, whether that’s in another 100 years or two years from now, we need to be able to accurately predict what the outcome will be when decisions are made regarding who’s vaccinated first,” adds Kadelka.

Developing a vaccine strategy is complicated, and there will always be tradeoffs between opposing goals like minimizing mortality or incidence. But Kadelka believes mathematical models like the one he and his colleagues created can help ensure lifesaving vaccines have the greatest impact.

Source: Iowa State University

This article was originally published in Futurity. Edits have been made to this republication. It has been republished under the Attribution 4.0 International license.