Investigating COVID-19 with R: data analysis and simulations

In this presentation we discuss different aspects of COVID-19 modeling in R.

The presentation has significant technical elements, but most of the workflows are generalized and presented in ways that are easy to understand and transfer to other fields.

The presentation has three parts: data analysis, simulations, and framework design.

1. Data analysis (15-20 min)

– Data analysis of data from well known repositories.

– Like, New York Times COVID-19 and Apple COVID-19 Mobility data.

– Application of different data summarizations, visualizations, and machine learning algorithms.

– This is the “warm-up” part of the talk and a fair amount of didactic explanations are given.

2. Simulations (25-30 min)

– Epidemiology models overview.

– Basic compartmental models: SIR, SEI2R.

– Advanced single-site models: SEI2HR-Econ.

– Quarantines modeling.

– Limited resources modeling.

– Interactive interfaces.

– Multi-site modeling and simulations.

– Typical workflows.

3. Framework design (10-15 min)

– This is a more code-technical part that discusses the principles behind the main, novel R packages used in the presentation.

*The Speaker:*
Anton Antonov
Accendo Data, LLC
Senior Research Scientist

Anton is an applied mathematician (PhD) with 28+ years of experience in algorithm development, scientific computing, mathematical modeling, operations research, natural language processing, machine learning, data science, and data mining.

In the last twelve years, he focused on developing machine learning algorithms and workflows for different industries (music, movies, points of interests, recruiting, healthcare.) Currently, he is working on operations research and data science applications to manufacturing and healthcare.

Anton is a former kernel developer of Mathematica.