New Model and Tool Incorporating Local Features in Covid-19 Spread
- Jeffrey Morris
- Apr 27, 2020
- 1 min read
Penn Biostatistics colleague Jing Huang has worked with CHOP collaborators Gregory Tasian and David Rubin, director of PolicyLab, to model incidence data as a function of local characteristics, which may be useful for making future projections.
The approach monitors spread rate as a function of key demographic, local population density, and mobility variables.
Here is a report on the Penn Department of Biostatistics, Epidemiology, and Informatics website.
Here is a link to the results on the CHOP policy lab website
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Interesting stuff! Seems like a solid approach to modeling COVID spread. All this data analysis makes me think, if they can predict viruses this well, could they make a Fruit Ninja simulator for real life? 😂