date: 2020-04-05 15:34:19 PDT

Data are retrieved from the NYTimes Github Repo. This plot is similar to the online chart published by the NYTimes with the addtion that points are encoded for daily estimated case doubling time (the inverse of the growth rate).

Growth is assumed to be exponential. \(t_{double}(t)\) is estimated by:

\[t_{double}(t) = \frac{\int_{0}^{t}cases(t')\; dt'}{cases(t)} \times \log(2)\]

where \(cases(t)\) is the daily reported cases of COVID-19 for a specific location.

The latest date recorded in the data is 2020-04-04.

Plot data is summarized for select metro areas.

## Regional Definitions by County
puget_region <- c("Thurston", "Island", "Kitsap", "Pierce", "King", "Snohomish", "Whatcom", "Skagit" )
bay_area <- c("Alameda", "Contra Costa", "Santa Clara", "San Mateo", "San Francisco", "Santa Cruz")
southland <- c("Santa Barbara", "Ventura", "Los Angeles", "Orange", "San Diego")
willamette_region <- c("Multnomah", "Clackamas", "Washington", "Marion", "Yamhill", "Linn", "Benton", "Lane" )
denver <- c("Arapahoe", "Adams", "Boulder", "Douglas", "Jefferson", "El Paso")
miami <- c("Dade", "Broward", "Palm Beach", "Brovard", "Indian River", "St Lucie", "Saint Lucie", "Monroe")

In general, the redder the data point, the faster the number cases are growing in that location. The larger the data point, the more cases there are. Large red circles are of most concern. Small dark red circles can turn into larger circles quickly unless proactive steps are taken. Pale circles indicate slower grwoth rates, generally in locations where recommended contraventions have been followed.