In this week’s Brief, we look deeper into the question of whether Covid-19 is a senior citizen disease. Our course spotlight is twofold:
See you in class!
– Peter Bruce
Founder, Author, and Senior Scientist
Coronavirus: Age and Health
Although you read in the news of young people dying from Covid, the disease in its serious form is overwhelmingly a disease of older people. Might this be simply because older people have more health issues? Read on for the answer […]
Word of the Week
Decision Stumps
A decision stump is a decision tree with just one decision, leading to two or more leaves. Decision stumps are used in […]
Historical Spotlight
Miasma
As more information arrives about the Coronavirus, researchers point more and more to airborne particles and aerosols as the mechanism of spread. Photographic images of a sneeze, such as […]
IMAGES BY LYDIA BOUROUIBA, MIT
Course Spotlight
- Regression Analysis (starts May 8)
- Generalized Linear Models (starts June 19)
- Count Data Modeling (starts Oct. 23)
- Modeling in R (starts July 3)
- Mixed and Hierarchical Linear Models (Starts Sept 25)
We’ll cover analysis of Covid data broadly, and focus on the epidemiological and statistical models used to forecast the spread of the pandemic. In this seminar-style course for statistically-literate* researchers, you will
- Explore key rates and features of the Coronavirus data
- Learn how to specify epidemiological models
- Learn how to fit statistical models
- The strengths and weaknesses of each type of model
The instructors are
- James Hardin, Epidemiology and Biostatistics Associate Professor and Associate Dean for Faculty Affairs and Curriculum for the Arnold School of Public Health, University of South Carolina.
- Wayne Folta a Lead Data Scientist at Elder Research, Inc. where he develops and deploys models for clients. His current work involves the design and implementation of text mining and deep learning models at the U.S. Dept. of Health and Human Services; he has also been active within Elder Research in exploring and assessing epidemiological models in R.
See you in class!
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