Ed Ionides and Aaron King will be teaching their short course on Simulation-based Inference for Epidemiological Dynamics for the second time this July at the 8th Summer Institute in Statistics and Modeling in Infectious Diseases (SISMID). The course introduces statistical inference techniques and computational methods for dynamic models of epidemiological systems. It will explore deterministic and stochastic formulations of epidemiological dynamics and develop inference methods appropriate for a range of models. Special emphasis will be on exact and approximate likelihood as the key elements in parameter estimation, hypothesis testing, and model selection. Specifically, the course will cover sequential Monte Carlo and synthetic likelihood techniques. Students will learn to implement these in R to carry out maximum likelihood and Bayesian inference. Knowledge of the material in Module 1 (Probability and Statistical Inference) is assumed. Students new to R should complete a tutorial before the module. The course is listed as Module 9 and will run from 18-20 July 2016 at the University of Washington in Seattle.
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This software has been made possible by support from the U.S. National Science Foundation (Grants #EF-0545276, #EF-0430120), by the “Inference for Mechanistic Models” Working Group supported by the National Center for Ecological Analysis and Synthesis (a Center funded by N.S.F. (Grant #DEB-0553768), the University of California, Santa Barbara, and the State of California), and by the RAPIDD program of the Science & Technology Directorate, Department of Homeland Security and the Fogarty International Center, U.S. National Institutes of Health. |