Qianying Lin

Data Science Fellow

Michigan Institute for Data Science

With the explosion of the volume and variety of epidemic related data, we propose an efficient, theoretically sound, and easily-implemented framework, which employs both the regularly reported infections and the asynchronous genetic samples to make inference on the epidemic parameters (eg. transmission rate) and forecast the epidemic trends and genealogical patterns.