June 02, 2017, Webb 1100

Infectious diseases can be viewed as complex spatio-temporal systems that can be controlled and ultimately eradicated by human interventions. Interventions actuate changes through policy and implementation changes that promote treatment and prevention and long-term investment strategy determines the tools that will become available. Considering the magnitude of these decisions, in terms of lives saved and expense, computational epidemiology has emerged as a useful tool for adding quantitative rigor and robustness. This talk will provide an introduction to epidemiological modeling methods, highlight some recent results from the Institute for Disease Modeling, and conclude with iterative algorithms for model optimization and fitting.

Daniel J. Klein is a senior research manager and Chair of the Applied Math section at the Institute for Disease Modeling (IDM). His time is split between HIV modeling and algorithm development. Daniel’s recent HIV work focuses on medical male circumcision for HIV prevention, novel approaches to anti-retroviral treatment and pre-exposure prophylaxis targeting, and solutions to fix the care continuum. His work in applied math enables researchers to explore, calibrate, and optimize large parameter spaces using supercomputing. Daniel has an engineering background, with Ph.D. and M.S. degrees in Aeronautics and Astronautics from the University of Washington, Seattle. He also has a Bachelor of Science in Mechanical Engineering from the University of Wisconsin, Madison, and was a postdoctoral scholar in the Electrical and Computer Engineering department at the University of California, Santa Barbara prior to joining IDM.