Who Should Get the Vaccine First? Ask a Machine

Prioritizing recipients of eventual COVID-19 vaccines will pose a massive ethical dilemma, but data-driven tools can provide guidance.


There are over 165 COVID-19 vaccine candidates in various stages of development around the world. But even as researchers are in a global race to help society build immunity, any candidates that make it through clinical trials and gain approval will create an ethical dilemma that policymakers need to grapple with now.

Even with an approved vaccine in hand, manufacturing enough doses to vaccinate everyone who wants to be immunized will take many months. Then there’s the logistics of distribution and personnel to administer the shots. And that all begs complex questions about who should get vaccinated first.

Many possible options, none of them perfect

There is general consensus on the highest priority group for a COVID-19 vaccine: front-line healthcare workers who put themselves at daily risk by caring for COVID-19 patients need to be vaccinated first — both in recognition of their high-risk work and because their health is critical to treating infected patients.

After that, ranking priorities gets murkier. We will need to do the most we can with a scarce resource, and there are many groups that we could choose to put first.

There are the elderly who are at greatest risk of death if they become infected, but unfortunately they also tend to have the least robust response to vaccination. That means that even if they get the vaccine, they may not generate the necessary antibodies to fight an infection.

There are the staff who work in long-term care facilities, shelters, and group homes whose immunity could help protect vulnerable groups. There are other essential workers like firefighters and anyone involved in the food or medicine supply chains.

There are people whose occupations put them at greater risk because their jobs require close contact, like construction workers, schoolteachers, and food packers.

There are people whose chronic health conditions — like obesity, asthma, and diabetes — put them at greater risk of severe complications if they get infected. By the same token, there are racial and socioeconomic groups that are disproportionately affected by the pandemic.

Then there’s the question of whether targeting the hardest hit geographic regions is the most equitable choice. There’s also the question of whether the overall impact of vaccinating young people has the largest benefit for society, as the demographic that will likely have the best immune response to vaccination, and the one that is currently driving the highest infection rates. At the same time, they’re the least likely to personally require hospitalization.

What do we owe to the people who volunteered to participate in clinical trials, but who received a placebo in place of the real vaccine? And in the face of vaccine nationalism, what do we owe to countries that can’t afford to secure priority contracts with vaccine developers?

These are all possible considerations, but no matter what path Canada chooses, there are sure to be disagreements. That’s why transparency matters.

“Those conversations should hopefully start to be communicated with the public… so people aren’t taken aback,” said Matthew Miller, associate professor of biochemistry and biomedical sciences at McMaster University, in an interview with the National Post.

“Getting out in front of these things is going to be really useful, explaining who is going to get this first and why.”

Data-driven analysis gives objective insight on benefits

It will be important for any vaccination plan to be flexible, as new information could shift priorities. For instance, if an approved vaccine has been tested with different demographics and is found to be ineffective for older recipients, priority might shift elsewhere. We may also learn more about outbreak patterns and who might be most at risk, or whose immunity might lower community risk.

Tools that consider the data, used in conjunction with moral and ethical considerations, will help guide the response. Daniel Ashlock, professor of mathematics at the University of Guelph, is using mathematical models and AI to get an objective and data-driven sense of the choices that would make the biggest impact on the course of the pandemic.

His model considers numerous factors, including vaccination, public health mitigation strategies, testing, and data on known cases and asymptomatic carriers. It can be adapted to new information as we collect it.

“If this model works, it could have significant implications for how public officials distribute vaccines across Canada,” said Ashlock in a press release. “This will be an open source software, so anyone can use it. Knowing who, how and where to vaccinate first is critically important to mitigating the spread of the virus.”

The simulations use “hyperheuristics” — a method of selecting which common-sense techniques best apply to current conditions, such as prioritizing COVID testing for people who have been in close contact with an infected person. It also takes into account elements like exposure risk, vulnerability, and vaccine availability.

“Think of it as a ‘good advice generator’ that can swallow a lot of data to generate that advice,” added Ashlock.

“Each of the hyperheuristics gives decision support to health units deciding who to test or who to vaccinate. The work we do on our own test networks creates general principles in the form of advice: for example, ‘vaccinating grocery store workers has more impact than vaccinating members of vulnerable populations.’”

In the debate over vaccine priorities, what is good for our communities will good for us all, as we drive down active case loads and lower everyone’s risk. As with all things related to this pandemic, empathy and patience will be key to getting through it.

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Karyn Ho is a science animator and engineer who thrives at the interface between science, engineering, medicine, and art. She earned her MScBMC (biomedical communications) and PhD (chemical engineering and biomedical engineering) at the University of Toronto. Karyn is passionate about using cutting edge discoveries to create dynamic stories as a way of supporting innovation, collaboration, education, and informed decision making. By translating knowledge into narratives, her vision is to captivate people, spark their curiosity, and motivate them to share what they learned.