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.
Data are reticulated with a spline to reduce noise and derivatives are computed from this smoothed data.
The latest date recorded in the data is 2020-04-19.
The maps shows, for counties with > 10 cases, the location, number of current cases, and the calculated growth rate (expressed as doubling time). Shorter doubling times are obviously worse thaan longer doubling times.
As case growth goes to zero, doubling times will tend to infinity. Plots are currently capped at 25 days. Early in the crisis doubling times were on the order of ~ 3 days.
This results saved as heat_map2020_04_20_16_11_30.jpg
We can use this data to compute expected new cases in the short term. THis is a ranking of “severity.”
As an approximation, the severity \(S\) is
\[{cases(t)} = {cases(t_0)} \times \ 2^{(t-t_0)/t_d}\]
where \(t_0\) is today and \(t\) is some date in the future. So the severity \(S\) is the extrapoloated number of new cases over the next \(t - t_0 = 14 \; days\). Fourteen days is about the maximum time it takes for CoronaVirus to incubate.
county | state | current cases | double_time | forecast new cases |
---|---|---|---|---|
New York City | New York | 137949.3 | 14.95 | 126060.775 |
Cook | Illinois | 21781.5 | 11.21 | 29983.904 |
Marion | Ohio | 1153.4 | 3.00 | 28141.134 |
Nassau | New York | 30850.2 | 16.49 | 24718.760 |
Suffolk | New York | 27337.6 | 16.12 | 22573.924 |
Providence | Rhode Island | 3379.9 | 5.38 | 17143.688 |
Westchester | New York | 24009.9 | 18.18 | 16935.642 |
Middlesex | Massachusetts | 8992.1 | 9.28 | 16593.830 |
Union | New Jersey | 9605.4 | 11.21 | 13222.569 |
Suffolk | Massachusetts | 8265.6 | 10.21 | 13116.390 |
Philadelphia | Pennsylvania | 9585.4 | 11.69 | 12399.536 |
Hudson | New Jersey | 10699.5 | 12.87 | 12042.269 |
Essex | New Jersey | 10568.7 | 13.16 | 11524.888 |
Los Angeles | California | 12470.3 | 15.05 | 11292.894 |
Essex | Massachusetts | 5303.8 | 8.94 | 10399.783 |
Passaic | New Jersey | 8459.6 | 12.28 | 10184.595 |
New Haven | Connecticut | 5197.5 | 8.96 | 10154.108 |
Pickaway | Ohio | 411.7 | 3.00 | 10044.828 |
Bergen | New Jersey | 12776.3 | 16.84 | 9957.266 |
Minnehaha | South Dakota | 1573.7 | 4.90 | 9828.999 |
The table generally follows the ranking of the number of cases, but there are some surprises.