The FluSight Challenge returns in Metaculus’s third annual tournament contributing forecasts to the Centers for Disease Control and Prevention (CDC) FluSight forecasting efforts. After two years of unusual influenza activity during the COVID pandemic, last year’s US flu season saw an early and intense peak, an estimated range of 300,000 to 650,000 flu hospitalizations, and 19,000 to 58,000 deaths. By forecasting the timing and intensity of this year’s flu season, the Metaculus community can aid public health officials and healthcare providers preparing to allocate resources for flu mitigation and care.

CDC: Percentage of Outpatient Visits for Influenza-like Illness (ILI)

This year, the CDC and the Council of State and Territorial Epidemiologists (CSTE) only funded the winning forecasting teams from last year’s FluSight competition. Metaculus is honored to belong to this group and to extend our partnership with Dr. Thomas McAndrew, who leads Lehigh University’s Computational Uncertainty Lab. Dr. McAndrew's team also includes Dr. Maimuna (Maia) Majumder, an Assistant Professor in the Computational Health Informatics Program (CHIP) at Boston Children’s Hospital and Harvard Medical School, as well as Dr. Shaun Truelove, an Assistant Scientist in the International Health and Epidemiology Departments in the Johns Hopkins Bloomberg School of Public Health.

Thomas McAndrew is a computational scientist and leads Lehigh University's Computational Uncertainty Lab. He works at the intersection of biostatistics and data science, studying ensemble models, expert-prediction, and crowdsourcing for forecasting infectious diseases.

Maia Majumder is an Assistant Professor in the Computational Health Informatics Program (CHIP) at Boston Children’s Hospital and Harvard Medical School and the Inaugural Peter Szolovits CHIP Distinguished Scholar. Her research applies artificial intelligence & machine learning methods to public health problems, with a focus on infectious disease surveillance.

Shaun Truelove is an Assistant Scientist in the International Health and Epidemiology Departments in the Johns Hopkins Bloomberg School of Public Health. He studies how to predict, respond to, and prevent outbreaks and epidemics through the use of infectious disease epidemiology, dynamics, and modeling.