date: 2020-04-24


Data are retrieved from the NYTimes Github Repo.

The latest available data are from 2020-04-23.


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

## define region by county

willamette_region <- c("Multnomah", "Clackamas", "Washington", "Marion", "Yamhill", "Linn", "Benton", "Lane" )

state_metro_data <- county_data %>%
  filter(state == "Oregon") %>%
  mutate(region = ifelse(county %in% willamette_region, "Willamette Valley", "Greater Oregon")) %>%

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.


Growth rates analyzed below with highly anomolous data (three sigma) removed to improve fit behavior.

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 cases drop to zero the doubling time will trend to infinity).

Estimated Doubling Rate

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