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.


COVID-19 Publications

S Zhao, et al. Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak.  Int J Infect Dis. 2020 Mar;92:214-217. DOI: 10.1016/j.ijid.2020.01.050. Epub 2020 Jan 30.

S Zhao, et al. Estimating the Unreported Number of Novel Coronavirus (2019-nCoV) Cases in China in the First Half of January 2020: A Data-Driven Modelling Analysis of the Early Outbreak. J Clin Med. 2020 Feb 1;9(2). pii: E388. DOI: 10.3390/jcm9020388.

Q Lin, et al. A conceptual model for the outbreak of Coronavirus disease 2019 (COVID-19) in Wuhan, China with individual reaction and governmental action. Int J Infect Dis. 2020. DOI: 10.1016/j.ijid.2020.02.058.