SUMMARY

COVID-19 cases in Oregon are increasing rapidly in the Willamette valley with doubling times of ~ 4 to 6 days. Total cases in Greater Oregon is still below 100, so measurements of growth rates are not as reliable, but the doubling times appear to be shorter (in the range of 3 to 5).

GET DATA

Get data from the NYTimes Github Repo.

METRO DATA SUMMARIES

Create regions by specficying specific counties. The Willamette Valley is where about 75% of Oregon’s 4 Million people live.

## define region by county

puget_region <- c("Thurston", "Island", "Kitsap", "Pierce", "King", "Snohomish", "Whatcom", "Skagit" )


state_metro_data <- county_data %>%
  filter(state == "Washington") %>%
  mutate(region = ifelse(county %in% puget_region, "Puget Sound", "Greater Washington")) %>%
  yo

Plots of Cases by Region

Plot data is created by summarizing data by metro area. Daily case rates are also computed adn shown as the gray area plot. (the colored line is essnetially the integral of the grey area).

Linear view of the data.

Cummulative cases are rising rapidly, with the number of daily cases shown in grey below the curve.

Logarithmic view

Reveals the underlying behavior more clearly. Doubling times are shown for reference. Longer doubling times mean slower case progression.

RATE CONSTANTS

Doubling Time Trend

Here is the (noisy) doubling time data computed on a daily basis with a smoothed trend line overlayed. Although there is a fair amount of noise in the data, trends appear.

Longer doubling times are better. As new daily cases drops to zero the duoubling time will trend to infinity.

## `geom_smooth()` using formula 'y ~ x'

Estimated Doubling Rate

An estimate of the doubling rate with uncertainy, by averaging last week of data.


fin