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“What’s really distinguishing [our study} from a lot of the studies that are being quoted by the national press . . . and the Administration is that we look at the local connections inside of communities, and those are usually ignored by bigger studies,” Junge tells WAMC’s Hudson Valley Bureau Chief Allison Dunne. “Our study’s taking this opposite perspective of really finally modelling person-to-person connections that come up in our day-to-day lives, like who we socialize with, where we work, connections of that sort, and we ask how the disease spreads in this sort of zoomed-in picture.”

The National Science Foundation (NSF) has awarded Bard College professors Matthew Junge, mathematics, and Felicia Keesing, biology; and Grinnell College professor Nicole Eikmeier, computer science, a $60,000 grant to develop network models that—by more accurately incorporating social distancing measures—better capture the geographic and social complexity of the COVID-19 pandemic. Awarded through the NSF’s Rapid Response Research (RAPID) program, which provides support for urgent scientific research that responds to emergencies and unexpected events, the grant includes funding for salaries, publishing costs, and several undergraduate research assistants over a six-month period.

Junge, Bard assistant professor of mathematics and lead investigator on the project, says their project aims to develop network models and mathematical theory to test the robustness of some prominent models being used by governments to justify the extreme levels of intervention we are living through. One advantage of a network model, which tries to accurately describe the face-to-face interactions each individual in a society has and how an infection might spread, is that it is relatively easy to implement social distancing into the network.

“Mathematicians are fairly adept at modeling the natural evolution of epidemics, but most ‘off the shelf’ models were not built to describe the dramatic levels of intervention—business closures, travel limitations, and social distancing—that we are living through during the COVID-19 pandemic,” says Junge. “The grant brings together a biologist (Felicia), computer scientist (Nicole), and mathematician (myself) as well as a few undergrad research assistants to tackle this problem over the next six months. Felicia is an expert in infectious disease, Nicole in modeling real world networks, and I am experienced in network infection models.”

Matthew Junge, assistant professor of mathematics, comes to Bard from Duke University, where he served as William W. Elliott Research Assistant Professor. He received his doctorate in mathematics from the University of Washington, where he also earned MS, BS, and BA degrees. His areas of interest include probability, statistical physics, and mathematical biology. Junge’s research takes a probabilistic approach to particle systems from physics and biology, including models for chemical reactions, species proliferation, and epidemic outbreaks. He also studies random structures from classical mathematics and computer science, such as permutations and fragmented spaces.

Felicia Keesing, David and Rosalie Rose Distinguished Professor of Science, Mathematics, and Computing, has been on the Bard faculty since 2000. She has a B.S. from Stanford University and a Ph.D. from the University of California, Berkeley. Since 1995, she has studied how African savannas function when the large, charismatic animals like elephants, buffaloes, zebras, and giraffes disappear. She also studies how interactions among species influence the probability that humans will be exposed to infectious diseases. Keesing also studies Lyme disease, another tick-borne disease. She is particularly interested in how species diversity affects disease transmission.

Nicole Elkmeier is an assistant professor of computer science at Grinnell College. She has a PhD in Mathematics from Purdue University and a BA from in mathematics and computer science from Concordia College. Her research is in the field of Network Analysis, specifically focused on studying features of real data and constructing and analyzing graph models which maintain those features. A network, in this case, is a set of nodes (people, web pages, etc.) connected by edges (physical connection, collaboration, etc). She is interested in random graph models, which are used to study how well an algorithm may do on a real-world network, and for testing properties that may further improve algorithms. Her research is at the intersection of math and computer science.