Research

Parameter estimation for infectious diseases plays an important role in the understanding of disease dynamics and decision-making in public health policies. Generally, parameters of interest are estimated by solving large-scale nonlinear programming (NLP) or mixed-integer nonlinear programming (MINLP) optimization problems based on discrete- or continous-time disease models. Such problems, however, are very challenging due to their large scale and strong nonlinearity. This research focuses on developing advanced modeling techniques and solution strategies for disease-related optimization problems.

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