Public health researchers around the world have an opportunity to learn from some of the leading infectious disease modelers in the United States who are now analyzing the spread of the coronavirus disease 2019 (COVID-19), projecting the future of the pandemic and informing policy responses.
Early registration for the 2020 Online Summer Institute in Statistics and Modeling for Infectious Diseases (SISMID) closes Monday, June 22. The Summer Institute, sponsored by the Department of Biostatistics in the University of Washington School of Public Health, is headed by Elizabeth “Betz” Halloran, a professor of biostatistics.
“Most of this year’s SISMID instructors are currently hard at work on the COVID-19 pandemic,” says Halloran, who notes that many past attendees are also working on the novel coronavirus. Ironically, the first SISMID was held in 2009 during the H1N1 influenza pandemic. “This year we celebrate SISMID’s 12th year, pandemic to pandemic.”
Participants can sign up for short course where they will learn to use genetic sequence data to track the speed at which COVID-19 spreads; or integrate information from mobility data, social media updates and other novel data streams into infectious disease modeling; or evaluate COVID-19 intervention strategies, such as vaccines and drug treatment.
Halloran is part of the Global Epidemic and Mobility Model (GLEAM) team that uses real-world data to analyze the COVID-19 pandemic in the U.S. Several of her GLEAM colleagues also teach short courses, including GLEAM Principal investigator Alessandro Vespignani and Ira Longini. Vespignani is director and Sternberg Family Distinguished Professor of Health Sciences and Computer Sciences at Northeastern University. Longini is a professor and co-director of the Center for Statistics and Quantitative Infectious Diseases Emerging Pathogens Institute at the University of Florida.
The Summer Institute in Statistics and Modeling for Infectious Diseases will offer 15 modules and run July 13-29. Courses are also offered in Summer Institutes for Statistics in Clinical and Epidemiological Research, Statistics for Big Data, and Statistical Genetics.