CCHMP COVID-19 Data: Global UV Radiation and Infection Growth Rates

Replication material for a 2020 preprint by researchers from the University of Chicago analyzing global COVID-19 growth rates. The release includes code in R, Stata, and Matlab for reproducing figures, regression analyses, and seasonal simulations. Data and scripts are organized into specific directories for plots, regressions, and SEIR models.

Use Cases

• Replicate distributed lag regression analyses based on the described Stata scripts.
• Generate seasonal simulation plots of COVID-19 growth based on the R and Stata code provided.
• Run SEIR model simulations based on the described Matlab scripts.
• Reproduce data visualizations of global COVID-19 and UV radiation trends.

Strengths

• Code is provided in three languages (R, Stata, Matlab) for flexibility.
• Scripts are organized into dedicated folders for specific analysis types (plots, regressions, simulations).
• The dataset supports replication of all main findings from the associated preprint.

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The manuscript and data are described as a preprint under review and are likely to change.
• Last update date is unknown; freshness unverified.

Provenance

Source

Tamma Carleton, Jules Cornetet, Peter Huybers, Kyle C. Meng, Jonathan Proctor (University of Chicago).

Collection Method

Data and code compiled for academic paper replication.

Geography

Global