REGIONAL COVID-19 STATISTICS
Saunders
Updated: 2020-11-12 13:37:38 PDT
Original version created 2020-05-03. See below for revision history
Intro
The spread of the SARS-COV-19 viral disease defies description in terms of a single statistic. To be informed about personal risk we need to know more than how many people have been sick at a national level or even state level, we need information about how many people are currently sick in our communicty and how the number of sick people is changing is changing at a state and even county level. It can be hard to find this information.
This analysis seeks to fill partially that gap. It includes:
1. Several national pictures of disease trends to enable a “large pattern” view of how disease has and is evolving a on country-wide scale.
2. A per capita analysis of disease spread.
3. A more granular analysis of regions, states, and counties to shed light on local disease pattern evolution.
4. Details of the time evolution of growth statistics.
This computed document is part of a constantly evolving analysis, so please “refresh” for the latest updates. If you have suggestions or comments please reach out on twitter @WinstonOnData or facebook.
You are welcome to visit my code repository on Github.
You are also welcome to visit my analysis on the Politics of COVID
Finally, you can alway check my Rpubs for new documents and updates.
National Statistics
Total & Active Cases, and Deaths
These trend charts show the national disease statistics. Note that raw daily trends are systematically related to the M-F work week.
Mortality and \(R_e\)
Distribution of \(R_e\) Values
There is a wide dirstubtion of \(R_e\) across regions and counties. The distributions in the graph below looks roughly symmetrical because the x-scale is logarithmic.
National Maps
The key indicator for disease forcasting is the Effective Reproduction Rate \(R_e\), which is a measure of how many new cases each existing case of disease creates.
When a lot of people are sick in a population without mass-immunity you want \(R_e \ll 1\) or \(log_2\)(\(R_e\)) < 0) to acheive negative disease growth.
After achieving negative growth, the next phase of recovery is maintaining consistently lower levels of disease to a level where disease cases can be micro-managed. There’s no clear agreement on how few that is, but I’ve seen estimates as low as 500 cases per day across the US (about 0.16 cases per 100k population).
An estimate of the disease “toll” is the number of officially tallied deaths. It is fully agreed this vastly underestimates that actual cost of the disease. Death counts are almost certainly underestimated and this does not reflect any long lasting health effects on those who recover.
State Level Data
Pandemic Totals
Current Status of Active Disease
Computed Reproduction Rate \(R_e\).
How many cases are there per day, per capita, in each state? You can see the number of current cases varies widely. I also include a forecast of the number of cases about a week from now given current trends. Most states currently show improvement.
County Level Data
While the State-Level Data Tell as remarkable story, it is also interesting to look at County-level data
| state | R_e | cases | daily_cases |
|---|---|---|---|
| Vermont | 1.43 | 2500 | 42 |
| Rhode Island | 1.37 | 32934 | 548 |
| Wyoming | 1.34 | 19525 | 739 |
| Iowa | 1.33 | 168232 | 4738 |
| Kansas | 1.33 | 109586 | 2682 |
| Connecticut | 1.32 | 83804 | 1452 |
| Tennessee | 1.31 | 287821 | 3650 |
| New York | 1.28 | 544629 | 3735 |
| Minnesota | 1.27 | 194581 | 5254 |
| Illinois | 1.26 | 525415 | 12939 |
| Pennsylvania | 1.26 | 247478 | 3917 |
| New Hampshire | 1.25 | 13048 | 227 |
| New Mexico | 1.25 | 58900 | 1380 |
| Ohio | 1.25 | 266792 | 5688 |
| Michigan | 1.24 | 249588 | 6046 |
| New Jersey | 1.24 | 264023 | 2766 |
| Delaware | 1.23 | 27260 | 267 |
| Maine | 1.23 | 8212 | 171 |
| West Virginia | 1.23 | 30080 | 630 |
| Colorado | 1.22 | 143262 | 3874 |
| Indiana | 1.22 | 226805 | 4952 |
| Massachusetts | 1.22 | 171659 | 1942 |
| Missouri | 1.22 | 209623 | 4018 |
| Nebraska | 1.22 | 89427 | 2085 |
| Maryland | 1.21 | 158594 | 1409 |
| Oregon | 1.21 | 52779 | 854 |
| Oklahoma | 1.20 | 142360 | 2078 |
| Arizona | 1.18 | 265118 | 2070 |
| California | 1.18 | 999352 | 6728 |
| Texas | 1.18 | 1048582 | 10108 |
| Utah | 1.18 | 139900 | 2687 |
| Idaho | 1.17 | 77374 | 1314 |
| Washington | 1.17 | 127180 | 1517 |
| Kentucky | 1.15 | 129925 | 2185 |
| Nevada | 1.14 | 114013 | 1359 |
| Wisconsin | 1.14 | 300896 | 6570 |
| Arkansas | 1.11 | 124032 | 1408 |
| Mississippi | 1.11 | 129273 | 943 |
| Louisiana | 1.10 | 189613 | 703 |
| Virginia | 1.09 | 153824 | 1258 |
| Georgia | 1.08 | 396162 | 2495 |
| Alabama | 1.07 | 208320 | 1571 |
| South Dakota | 1.07 | 57510 | 1254 |
| South Carolina | 1.06 | 188946 | 1174 |
| Florida | 1.05 | 855999 | 5173 |
| Montana | 1.05 | 42215 | 903 |
| North Carolina | 1.01 | 300633 | 2442 |
| North Dakota | 1.01 | 57869 | 1298 |
Regional Snapshots
Regional snapshots reveal the highly nuanced behavior of disease spread. Each snaphot includes multiple states and selected counties.
How to read the charts
There are four components:
1. State Maps show the number of active cases and with the Reproduction rate encoded as color.
2. State Graphs State-wide trend graphs.
3. Severity Ranking These is a table of counties where the highest number of new cases are expected. Severity is a compounded function \(f(R, cases(t))\). This is useful for finding new (often unexpected) “hot spots.” Added per capita rates.
4. County Graphs encode the R-value in the active number of cases. R is the Reproduction Rate.
(NOTE: R < 1 implies a shrinking number of active cases, R > 1 implies a growing number of active cases. For R = 1, active cases are stable. ).
Washington and Oregon
California
Four Corners
Mid-Atlantic
Deep South
FL and GA
Texas & Oklahoma
Michigan & Wisconsin
Minnesota, North Dakota, and South Dakota
Connecticut, Massachusetts, and Rhode Island
New York
Vermont, New Hampshire, and Maine
Carolinas
North-Rockies
Midwest
Tennessee and Kentucky
Missouri and Arkansas
Conclusions
It’s in control some places, but not all places. And many places are completely out-of-control.
Stay Safe!
Be Diligent!
…and PLEASE WEAR A MASK
Built with R Version 4.0.3
This document took 705.1 seconds to compute.
2020-11-12 13:49:23
version history
Today is 2020-11-12.
176 days ago: plots of multiple states.
168 days ago: include \(R_e\) computation.
165 days ago: created color coding for \(R_e\) plots.
160 days ago: reduced \(t_d\) from 14 to 12 days. 14 was the upper range of what most people are using. Wanted slightly higher bandwidth.
160 days ago: “persistence” time evolution.
153 days ago: “In control” mapping.
153 days ago: “Severity” tables to county analysis. Severity is computed from the number of new cases expected at current \(R_e\) for 6 days in the future. It does not trend \(R_e\), which could be a future enhancement.
145 days ago: Added census API functionality to compute per capita infection rates. Reduced spline spar = 0.65.
140 days ago: Added Per Capita US Map.
138 days ago: Deprecated national map. can be found here.
134 days ago: added state “Hot 10” analysis.
129 days ago: cleaned up county analysis to show cases and actual data. Moved “Hot 10” analysis to separate web page. Moved “Hot 10” here.
127 days ago: added per capita disease and mortality to state-level analysis.
115 days ago: changed to county boundaries on national map for per capita disease.
110 days ago: corrected factor of two error in death trend data.
106 days ago: removed “contained and uncontained” analysis, replacing it with county level control map.
101 days ago: added county level “baseline control” and \(R_e\) maps.
97 days ago: fixed normalization error on total disease stats plot.
90 days ago: Corrected some text matching in generating county level plots of \(R_e\).
84 days ago: adapted knot spacing for spline.
70 days ago:using separate knot spacing for spline fits of deaths and cases.
68 days ago: MAJOR UPDATE. Moved things around. Added per capita severity map.
40 days ago: improved national trends with per capita analysis.
39 days ago: added county level per capita daily cases map. testing new color scheme.
12 days ago: changed to daily mortaility tracking from ratio of overall totals.
5 days ago: added trend line to state charts.
Appendix: Methods
Disease data are sourced from the NYTimes Github Repo. Population data are sourced from the US Census census.gov
Case growth is assumed to follow a linear-partial differential equation. This type of model is useful in populations where there is still very low immunity and high susceptibility.
\[\frac{\partial}{\partial t} cases(t, t_d) = a \times cases(t, t_d) \] \(cases(t)\) is the number of active cases at \(t\) dependent on recent history, \(t_d\). The constant \(a\) and has units of \(time^{-1}\) and is typically computed on a daily basis
Solution results are often expressed in terms of the Effective Reproduction Rate \(R_e\), where \[a \space = \space ln(R_e).\]
\(R_e\) has a simple interpretation; when \(R_e \space > \space 1\) the number of \(cases(t)\) increases (exponentially) while when \(R_e \space < \space 1\) the number of \(cases(t)\) decreases.
Practically, computing \(a\) can be extremely complicated, depending on how functionally it is related to history \(t_d\). And guessing functional forms can be as much art as science. To avoid that, let’s keep things simple…
Assuming a straight-forward flat time of latent infection \(t_d\) = 12 days, with \[f(t) = \int_{t - t_d}^{t}cases(t')\; dt' ,\] \(R_e\) reduces to a simple computation
\[R_e(t) = \frac{cases(t)}{\int_{t - t_d}^{t}cases(t')\; dt'} \times t_d .\]
Typical range of \(t_d\) range \(7 \geq t_d \geq 14\). The only other numerical treatment is, in order to reduce noise the data, I smooth case data with a reticulated spline to compute derivatives.
DISCLAIMER: Results are for entertainment purposes only. Please consult local authorities for official data and forecasts.