Date on Master's Thesis/Doctoral Dissertation

8-2021

Document Type

Master's Thesis

Degree Name

M.S.

Department

Bioinformatics and Biostatistics

Degree Program

Biostatistics, MS

Committee Chair

Kong, Maiying

Committee Co-Chair (if applicable)

Rai, Shesh

Committee Member

Huang, Jiapeng

Committee Member

Mitra, Riten

Author's Keywords

COVID-19; predictive modeling; CyTOF

Abstract

In December 2019, an outbreak of a novel coronavirus initiated a global pandemic. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a virus that causes the disease coronavirus disease 2019 (COVID-19). Symptoms of infection with COVID-19 vary widely between individuals. While some infected individuals are asymptomatic, others need more extensive care and require hospitalization. Indeed, the COVID-19 pandemic was characterized by a shortage of hospital beds which presented additional complications in providing adequate care for patients. In this study, we used a combination of T cell population data collected from mass cytometry analysis and clinical markers to form a predictive model of clinical outcomes for hospitalized COVID19 patients. This thesis details the steps and analysis towards the design of the final model including data acquirement and preprocessing, missing data handling via multiple imputation, and repeated imputations inferences.

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