Looks like we have a viable COVID vaccine. Now the task is to find the best way to distribute it and vaccinate as many people in as effective a way as possible. Like the savvy geeks we are, we turn to computers for the answer.
Researchers at Washington State University (WSU) and Pacific Northwest National Laboratory have done just that, using the Summit supercomputer at Oak Ridge National Laboratory, which is the world’s second quickest supercomputer. In work supported by the U.S. Department of Energy Office of Science, National Science Foundation, and the National Institutes of Health, the team developed a fast, scalable algorithm to optimize the distribution of vaccines in a simulated epidemic network.
The researchers tested their computer algorithm with a synthetic social contact network for residents of Portland, Oregon. In the test case, the team compared the strategy provided by the algorithm with a scenario in which doctors vaccinated the same number of people randomly. The result? The algorithm decreased the number of infected people by three to seven times.
Ananth Kalyanarama, WSU’s Boeing Centennial Chair in Computer Science who helped to lead the team, commented, “In the past, large computer models have used mathematical programming to try to optimize vaccine solutions. But the spreading of a disease is incredibly complex, and the mathematical-based models aren’t well suited to take a network view of people actually interacting with other people. Our algorithm provides a network view of a population on the move.”
The researchers explained that mathematical programs only work for groups of about 10,000 people at a time. And the evaluation process can take hours or even days. The method isn’t feasible for a solution for hundreds of millions of people.
The WSU team said the algorithm is a good start, but it will need to be significantly improved to be used for a real-life scenario.