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An Introduction to Bayesian Modeling for Biomedical Research via COVID-19 Models

Overview:

This lecture will first introduce basic Bayesian modeling using COVID-19 data. It will then discuss a novel Bayesian approach that was recently published to estimate hospitalization risk for COVID-19 patients with comorbidities. The results indicate that cardiovascular diseases carry the highest hospitalization risk for COVID-19 adult patients, followed by diabetes, chronic respiratory disease, hypertension, and obesity.

Speaker:

Qunfeng Dong's research interests include analyzing high-throughput omics data, developing bioinformatics & biostatistical methods, and building integrative biological databases. Since March 2020, his research has mainly focused on COVID-19. More information is available at Center for Biomedical Informatics (CBMI).

Watch previous presentations and to find more information about future seminars.

Originally recorded on Wednesday, December 16, 2020, as part of

Overview:

This lecture will first introduce basic Bayesian modeling using COVID-19 data. It will then discuss a novel Bayesian approach that was recently published to estimate hospitalization risk for COVID-19 patients with comorbidities. The results indicate that cardiovascular diseases carry the highest hospitalization risk for COVID-19 adult patients, followed by diabetes, chronic respiratory disease, hypertension, and obesity.

Speaker:

Qunfeng Dong's research interests include analyzing high-throughput omics data, developing bioinformatics & biostatistical methods, and building integrative biological databases. Since March 2020, his research has mainly focused on COVID-19. More information is available at Center for Biomedical Informatics (CBMI).

Watch previous presentations and to find more information about future seminars.