Can algorithms help us develop a more effective strategy for our vaccine rollout in the Covid-19 pandemic? On the face of it, one might have doubts. As SFI External Professor Melanie Moses (University of New Mexico) points out in her recent op-ed for Nautilus, “prioritization algorithms have led to the most privileged being prioritized over the most exposed, and strict adherence to priority pyramids has been disastrously slow.”
At the same time, Moses argues, algorithmic thinking might hold the key to a far more successful rollout. Moses is a computational biologist who studies scalable distribution systems; she also designs algorithms for robot systems. In the kind of thinking Moses engages in when she builds algorithms, she found a set of strategies that are ideally suited for upping our vaccination game.
The first strategy is parallelization. As Moses explains, “vaccination pipelines run fastest when they are run in parallel.” With parallel pipelines, we can also ameliorate some of the systemic injustice that our current prioritization algorithms cause.
The second is exponential rollout. Since we cannot solve exponential problems with linear solutions, we must match the exponential growth of the virus with exponential growth in vaccinations.
The third is match supply and demand. Our strategies must continue to locate and resolve the supply and demand asymmetries—to grasp why people are reluctant to take vaccines in some places and discover why clinics are running out of them in others.
Ultimately, algorithmic thinking is the kind of thinking that analyses and adapts to the evolving problems it addresses. It can help us assess our challenges continuously, and in so doing solve them with far greater speed.
Read the article in Nautilus (January 20, 2021)