Updated: 2020-12-18 08:22:01 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 distribution of \(R_e\) across regions and counties. The distributions in the graph below looks roughly symmetrical because the x-scale is logarithmic.

National Maps

State Level Data

There are several maps below. These include:

  • pandemic total cases (How many people have been sick?)
  • pandemic total cases per capita (What fraction of people have been sick?)
  • daily cases per capita (what fraction of people are getting sick?)
  • forecast short term cases per capita (based on \(R_e\)) (how fast is the disease growning or shrinking?)

Pandemic Totals

Current Status of Active Disease

Computed Reproduction Rate \(R_e\).

County Data

While the State-Level Data tell as remarkable story, outbreaks tend to be highly localized to communities - County-level data can help decode this.


state R_e cases daily cases daily cases per 100k
Rhode Island 1.37 66708 1704 161.3
Tennessee 1.27 477469 9408 141.5
California 1.25 1751468 42594 108.8
Arizona 1.06 439490 7128 102.6
Indiana 0.95 450434 5888 88.7
Nevada 0.98 197380 2577 88.2
Delaware 1.03 48721 836 88.0
Pennsylvania 1.03 537137 10746 84.0
Utah 0.97 243144 2555 83.9
Idaho 0.94 127187 1367 81.0
Oklahoma 1.02 248478 3117 79.6
Ohio 0.85 597992 9126 78.4
New Mexico 1.00 126069 1631 78.0
Connecticut 1.05 159689 2755 76.9
Alabama 1.04 309778 3731 76.7
Arkansas 1.06 191176 2242 75.0
Mississippi 1.04 187876 2181 73.0
West Virginia 1.07 68370 1329 72.7
Kansas 0.92 198837 2095 72.0
Texas 1.07 1555685 19701 70.7
Massachusetts 1.01 296508 4736 69.3
South Dakota 0.87 91378 586 68.9
New Hampshire 1.05 33749 870 64.8
Kentucky 0.94 237424 2848 64.1
Nebraska 0.90 153712 1180 62.0
Illinois 0.93 881633 7938 61.9
Wisconsin 0.91 482529 3534 61.2
Georgia 1.10 544408 6242 60.6
South Carolina 1.03 263206 2955 59.6
Montana 0.88 75523 620 59.5
North Carolina 1.02 459535 5975 58.8
Louisiana 1.08 278064 2732 58.6
Colorado 0.86 302316 3216 58.1
Missouri 1.00 346923 3427 56.3
Wyoming 0.83 40690 325 55.9
New Jersey 1.00 420571 4829 54.4
Minnesota 0.76 389666 2880 52.1
New York 1.01 820098 10221 52.1
Florida 1.07 1163669 10620 51.6
Iowa 0.96 261649 1602 51.1
North Dakota 0.61 89247 344 45.7
Michigan 0.87 482174 4528 45.5
Virginia 0.99 235418 3040 43.6
Maryland 0.95 244830 2589 43.1
Washington 0.92 219868 2668 36.6
Maine 1.23 17777 480 36.0
Oregon 0.96 98965 1300 31.8
Vermont 0.96 6138 104 16.6

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 859 seconds to compute.
2020-12-18 08:36:20

version history

Today is 2020-12-18.
212 days ago: plots of multiple states.
204 days ago: include \(R_e\) computation.
201 days ago: created color coding for \(R_e\) plots.
196 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.
196 days ago: “persistence” time evolution.
189 days ago: “In control” mapping.
189 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.
181 days ago: Added census API functionality to compute per capita infection rates. Reduced spline spar = 0.65.
176 days ago: Added Per Capita US Map.
174 days ago: Deprecated national map. can be found here.
170 days ago: added state “Hot 10” analysis.
165 days ago: cleaned up county analysis to show cases and actual data. Moved “Hot 10” analysis to separate web page. Moved “Hot 10” here.
163 days ago: added per capita disease and mortality to state-level analysis.
151 days ago: changed to county boundaries on national map for per capita disease.
146 days ago: corrected factor of two error in death trend data.
142 days ago: removed “contained and uncontained” analysis, replacing it with county level control map.
137 days ago: added county level “baseline control” and \(R_e\) maps.
133 days ago: fixed normalization error on total disease stats plot.
126 days ago: Corrected some text matching in generating county level plots of \(R_e\).
120 days ago: adapted knot spacing for spline.
106 days ago:using separate knot spacing for spline fits of deaths and cases.
104 days ago: MAJOR UPDATE. Moved things around. Added per capita severity map.
76 days ago: improved national trends with per capita analysis.
75 days ago: added county level per capita daily cases map. testing new color scheme.
48 days ago: changed to daily mortaility tracking from ratio of overall totals.
41 days ago: added trend line to state charts.
13 days ago: decreased max value of Daily Cases per 100k State map.
6 days ago: increased max total state cases to 2,000,000 as California passes 1.5Million diagnosed cases.

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.