date: 2020-04-13

GET DATA

Data are retrieved from the NYTimes Github Repo.

The latest available data are from 2020-04-12.

METRO DATA SUMMARIES

Create regions by specficying specific counties. The Puget Sound is where 4.2 Million (56%) of Washington’s 7.4 Million people live.

## define state

select_state = "Washington"

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



state_metro_data <- county_data %>%
  filter(state == select_state) %>%
  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.

## Warning in self$trans$transform(x): NaNs produced
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 2 rows containing missing values (position_stack).

RATE CONSTANTS

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.


fin