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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These approaches consider factors such as population density, Skribbl IO mobility patterns, and environmental conditions to provide tailored insights for specific communities.