Jeremy - This is Jeremy Benson from NIU steam. We're back here with Dr. Sachit Butail, who's been talking with us about agent-based modeling. And today, we're going to talk a little more about how we're using that technology to model things relating to the COVID-19 pandemic. So, tell us a little bit about how has this modeling been used in determining reopening protocols.
Dr. Butail - This is a study that was led by Dynamical Systems laboratory at New York University with NIU as a collaborator. Back when vaccines had just started getting approved, we simultaneously began hearing opinions on new cycles about how quickly we should reopen. Our group had already developed a high-fidelity agent-based model of a small town in New York, where agents in a simulation could get exposed, infected, recover etc. Now we were interested in the interplay between vaccination rates and reopening efforts. And to model reopening efforts the team at NIU augmented this agent-based model by enabling agents to now travel to work, to leisure, by car, train, bus etc. And so now we took that model, and we simulated a wide range of scenarios, taking possibilities between different vaccination rates and different reopening efforts. Our results confirm that we should be careful reopening too fast, and we may just offset the effect of the current vaccination rates.
Jeremy - So what types of agent-based modeling are you working on currently?
Dr. Butail – We’re augmenting the current model to include even more details in terms of agent response to different types of vaccines and the presence of different viral strains like the delta. We're also modeling different cities within the US to see if our results that we have now simulated in the small town are generalizable to a wide variety of scenarios. Our lab at NIU is also using agent-based models to study effective strategies in robotics especially for human swarm robotics scenarios.
Jeremy - This has been The Sound of Science on WNIJ, where you learn something new everyday.
