Updated: 2020-08-21 06:42:25 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 constantly evolving, so please “refresh” for the latest updates. If you have suggestions or comments please reach out on twitter @WinstonOnData or facebook.

National Maps

There are plenty of online maps. I’ve deprecated a few of the ones I’ve computer since they are no longer relevant to the analysis of disease trends. They are published:
- here.

Cases and Deaths per Capita

This chart reveals a more interesting pattern of disease spread. I haven’t found one of these online.
Groups of cities (e.g. Chicago, Indianapolis, and Detroit) and paths between connected communities are clearly visible.

Reproduction and Control

\(R_e\) is a measure of disease growth. For recovery to begin disease growth must turn from positive to negative (i.e. from \(log_2\)(\(R_e\)) > 0 to \(log_2\)(\(R_e\)) < 0).

After achieving negative growth growth, the next phase of recovery is maintaining consistently lower levels of disease. Control can be measured as a ratio of current disease levels to maximum disease levels. If disease levels are currently at a maximum, control is 0 %.

\[ control = 100 \times (1 - \frac{active \space disease}{max(active \space disease)} ) \% \]

State Level Data


County Level Data


state R_e cases daily_cases
Massachusetts 1.99 123080 452
Rhode Island 1.34 19060 134
Wyoming 1.34 3464 52
Maine 1.31 4261 25
Iowa 1.16 54609 615
North Dakota 1.16 9100 163
South Dakota 1.14 10483 115
Connecticut 1.11 51220 100
Mississippi 1.09 75205 817
Illinois 1.08 214535 1967
North Carolina 1.03 150134 1346
Missouri 1.02 64287 1050
Kentucky 1.01 43660 667
Kansas 1.00 36534 462
Utah 1.00 48024 358
Tennessee 0.99 136264 1560
Nebraska 0.98 31153 249
Minnesota 0.97 67173 590
Oklahoma 0.97 50622 631
West Virginia 0.97 8941 116
Alabama 0.96 112377 1010
Arkansas 0.96 54004 573
Texas 0.96 589758 6888
Washington 0.96 72283 619
Wisconsin 0.96 68524 700
New York 0.95 432547 607
Michigan 0.94 104395 663
Georgia 0.92 227390 2557
Oregon 0.92 24188 250
Montana 0.91 6032 94
Nevada 0.91 63858 640
Ohio 0.91 111954 928
Indiana 0.90 85317 789
New Jersey 0.90 190197 345
New Mexico 0.90 23900 134
Pennsylvania 0.90 131717 672
Colorado 0.89 54240 286
Virginia 0.89 87262 644
Arizona 0.87 196409 750
Idaho 0.87 29222 348
South Carolina 0.87 109270 730
Vermont 0.86 1527 6
Maryland 0.84 102862 536
California 0.82 654756 6678
Louisiana 0.80 140535 738
Florida 0.78 588443 4144
Delaware 0.76 16495 79
New Hampshire 0.72 7053 15

National Statistics

Total & Active Cases, and Deaths

These trend charts show the national disease statistics. The raw data are shown. since these showdaily trends that are systematically related ot the M-F work week, possibly due to reporting delays, numbers showsn

Mortality Trend

\(R_e\) Trend

National effective reproduction rate

Distribution of \(R_e\) Values

Howver, there is a wiude dirstubtion of \(R_e\) across regions and counties. The distributions in the graph below looks roughly symmetrical because the x-scale is logarithmic.

Distribution of Baseline Control

Similarly for disease control, when take at the county level, there is a wide distribution of Baseline Control.

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

WA
county ST case rank severity R_e cases cases/100k daily cases
Klickitat WA 25 1 1.8 160 750 8
King WA 1 2 1.1 18328 850 165
Grant WA 9 3 1.1 2036 2150 49
Grays Harbor WA 26 4 1.5 159 220 6
Kitsap WA 16 5 1.3 865 330 16
Snohomish WA 4 6 1.0 6705 850 48
Pierce WA 3 7 1.0 6988 810 67
Clark WA 8 9 1.1 2374 510 30
Benton WA 6 11 1.1 4100 2110 21
Yakima WA 2 14 0.8 11391 4570 36
Franklin WA 7 16 0.8 3926 4330 22
Spokane WA 5 19 0.6 4913 990 31
OR
county ST case rank severity R_e cases cases/100k daily cases
Union OR 14 1 1.8 404 1550 2
Jackson OR 9 2 1.3 634 300 20
Marion OR 3 3 1.1 3369 1000 45
Klamath OR 18 4 1.5 219 330 2
Multnomah OR 1 5 0.9 5513 690 49
Clackamas OR 5 6 1.0 1753 430 19
Washington OR 2 7 0.9 3446 590 29
Malheur OR 6 8 1.0 970 3190 16
Umatilla OR 4 11 0.8 2514 3270 17
Lane OR 8 17 0.8 642 170 4
Deschutes OR 7 18 0.8 657 360 4
## Warning: Removed 1 rows containing missing values (geom_col).

California

CA
county ST case rank severity R_e cases cases/100k daily cases
Fresno CA 7 1 1.1 22127 2260 456
San Bernardino CA 4 2 1.0 43604 2040 653
Los Angeles CA 1 3 0.8 227454 2250 1431
Sonoma CA 25 4 1.2 4845 970 130
Riverside CA 2 5 0.8 49233 2070 561
Alameda CA 8 6 1.0 16164 980 270
Monterey CA 21 7 1.1 6940 1600 150
San Diego CA 5 8 0.9 35720 1080 251
Orange CA 3 13 0.7 44976 1420 335
Kern CA 6 21 0.6 27470 3110 222
San Joaquin CA 9 23 0.6 15713 2150 157

Four Corners

AZ
county ST case rank severity R_e cases cases/100k daily cases
Pinal AZ 4 1 1.4 9053 2160 79
Maricopa AZ 1 2 0.9 130817 3080 400
Pima AZ 2 3 0.8 20239 1980 164
Mohave AZ 6 4 0.9 3439 1670 20
Yuma AZ 3 5 0.7 11974 5760 27
Apache AZ 7 6 0.9 3275 4580 9
Yavapai AZ 10 7 0.8 2209 980 15
Coconino AZ 8 8 0.8 3214 2290 9
Navajo AZ 5 11 0.6 5479 5040 6
Santa Cruz AZ 9 12 0.7 2718 5830 3
CO
county ST case rank severity R_e cases cases/100k daily cases
Saguache CO 30 1 2.2 108 1670 0
Adams CO 3 2 1.1 7013 1410 51
El Paso CO 4 3 0.9 5682 830 46
Denver CO 1 4 0.9 10736 1550 43
Arapahoe CO 2 5 0.9 7710 1210 33
Douglas CO 8 6 1.0 1876 570 12
Elbert CO 29 7 1.4 116 460 2
Weld CO 6 8 0.9 3886 1320 15
Jefferson CO 5 9 0.8 4503 790 24
Larimer CO 9 10 0.8 1745 520 16
Boulder CO 7 11 0.9 2181 680 9
UT
county ST case rank severity R_e cases cases/100k daily cases
Summit UT 7 1 1.9 779 1920 11
Utah UT 2 2 1.1 9794 1660 104
Salt Lake UT 1 3 1.0 22382 2000 152
Washington UT 5 4 1.2 2669 1660 18
Juab UT 17 5 1.4 89 810 2
Davis UT 3 6 0.9 3494 1030 23
Weber UT 4 7 0.9 3024 1220 20
Tooele UT 9 8 1.1 634 970 5
Cache UT 6 9 0.9 2000 1630 7
San Juan UT 8 10 1.2 658 4310 1
NM
county ST case rank severity R_e cases cases/100k daily cases
Santa Fe NM 8 1 1.3 734 490 10
Bernalillo NM 1 2 1.0 5457 810 30
Lea NM 7 3 0.9 989 1410 17
Rio Arriba NM 14 4 1.3 334 850 2
Chaves NM 11 5 1.0 578 880 11
Sandoval NM 5 6 1.1 1174 830 4
Roosevelt NM 16 7 1.2 182 950 2
Cibola NM 9 9 1.0 718 2660 4
San Juan NM 3 11 0.9 3123 2450 6
Doña Ana NM 4 13 0.6 2695 1250 14
McKinley NM 2 14 0.7 4143 5690 6
Otero NM 6 17 0.9 1112 1690 1

Mid-Atlantic

NJ
county ST case rank severity R_e cases cases/100k daily cases
Hunterdon NJ 19 1 1.9 1230 980 4
Essex NJ 2 2 1.0 20281 2560 29
Ocean NJ 7 3 1.0 10910 1840 23
Atlantic NJ 14 4 1.0 3663 1360 17
Passaic NJ 5 5 0.9 18218 3610 34
Burlington NJ 12 6 1.0 6262 1400 20
Mercer NJ 10 7 1.1 8274 2240 12
Camden NJ 9 8 0.9 8961 1770 28
Middlesex NJ 4 10 0.9 18394 2220 24
Bergen NJ 1 11 0.8 21593 2320 40
Hudson NJ 3 12 0.8 20147 3010 23
Union NJ 6 17 0.7 17100 3090 16
Monmouth NJ 8 18 0.7 10638 1710 16
PA
county ST case rank severity R_e cases cases/100k daily cases
Susquehanna PA 41 1 1.9 232 560 3
Pike PA 28 2 2.0 530 950 1
Philadelphia PA 1 3 0.9 32680 2070 110
Delaware PA 3 4 1.0 9944 1760 60
Montgomery PA 2 5 1.0 10567 1290 43
Carbon PA 34 6 1.4 412 640 5
Perry PA 46 7 1.5 155 340 4
Berks PA 7 8 1.0 5707 1370 31
Chester PA 8 9 1.0 5428 1050 27
Allegheny PA 4 10 0.8 9676 790 63
Bucks PA 5 14 0.9 7515 1200 27
Lancaster PA 6 20 0.8 6327 1180 31
Lehigh PA 9 37 0.7 5104 1410 9
MD
county ST case rank severity R_e cases cases/100k daily cases
Baltimore MD 3 1 0.9 14338 1730 98
Prince George’s MD 1 2 0.8 25515 2820 103
Somerset MD 23 3 1.5 163 630 3
Kent MD 22 4 1.6 252 1290 2
Montgomery MD 2 5 0.8 19309 1860 69
Anne Arundel MD 5 6 0.9 7826 1380 40
Frederick MD 7 7 1.0 3296 1330 20
Baltimore city MD 4 8 0.7 13748 2240 77
Harford MD 8 9 0.9 2232 890 22
Charles MD 9 11 0.9 2231 1410 17
Howard MD 6 13 0.8 4160 1320 22
VA
county ST case rank severity R_e cases cases/100k daily cases
Frederick VA 20 1 1.6 718 840 5
Fairfax VA 1 2 1.0 17328 1520 87
Franklin VA 55 3 1.5 196 350 4
Smyth VA 56 4 1.4 191 610 6
Appomattox VA 73 5 1.5 118 760 4
Prince William VA 2 6 0.9 10099 2210 56
Arlington VA 8 7 1.1 3314 1430 24
Chesterfield VA 5 11 0.9 4749 1400 34
Henrico VA 6 14 0.9 4236 1300 31
Virginia Beach city VA 4 15 0.8 5572 1240 46
Loudoun VA 3 18 0.9 5613 1460 28
Newport News city VA 9 20 0.9 2054 1140 20
Norfolk city VA 7 21 0.8 4097 1670 31
WV
county ST case rank severity R_e cases cases/100k daily cases
Marshall WV 20 1 2.9 132 420 0
Jackson WV 18 2 1.9 175 600 2
Taylor WV 28 3 1.7 90 530 5
Logan WV 5 4 1.1 415 1230 19
Kanawha WV 1 5 1.0 1155 620 20
Monongalia WV 2 6 1.1 1013 960 7
Cabell WV 4 7 1.0 485 510 8
Jefferson WV 7 11 1.2 312 560 2
Berkeley WV 3 14 0.8 754 660 5
Wood WV 8 18 0.8 292 340 3
Ohio WV 9 20 0.9 286 670 2
Raleigh WV 6 21 0.7 316 410 5
DE
county ST case rank severity R_e cases cases/100k daily cases
New Castle DE 1 1 1.0 7709 1390 45
Sussex DE 2 2 0.5 6252 2850 20
Kent DE 3 3 0.6 2534 1450 14

Deep South

AL
county ST case rank severity R_e cases cases/100k daily cases
Cleburne AL 66 1 2.5 169 1130 9
Lee AL 9 2 1.4 3203 2010 46
Tuscaloosa AL 5 3 1.3 4834 2340 60
Calhoun AL 12 4 1.3 2196 1910 42
St. Clair AL 17 5 1.4 1605 1840 21
Etowah AL 11 6 1.2 2491 2420 34
Coffee AL 35 7 1.4 928 1810 16
Jefferson AL 1 10 0.9 14785 2240 126
Marshall AL 8 17 1.1 3432 3610 22
Shelby AL 7 27 0.9 3855 1820 32
Montgomery AL 3 32 0.8 7464 3290 44
Mobile AL 2 33 0.6 11443 2760 78
Madison AL 4 38 0.7 5963 1670 34
Baldwin AL 6 45 0.6 4038 1940 26
MS
county ST case rank severity R_e cases cases/100k daily cases
Leflore MS 20 1 2.3 1137 3810 42
Jefferson MS 78 2 2.3 207 2820 2
DeSoto MS 2 3 1.3 4117 2340 56
Carroll MS 69 4 1.9 276 2720 3
Jackson MS 5 5 1.3 2624 1850 42
Yalobusha MS 65 6 1.8 339 2730 4
Madison MS 4 7 1.4 2662 2570 29
Rankin MS 6 13 1.2 2525 1670 27
Hinds MS 1 15 1.1 6092 2520 47
Lee MS 9 16 1.1 1879 2210 42
Harrison MS 3 21 0.9 2915 1440 35
Jones MS 7 46 0.9 2032 2970 11
Forrest MS 8 52 0.8 1963 2600 12
LA
county ST case rank severity R_e cases cases/100k daily cases
West Feliciana LA 51 1 1.4 452 2940 12
Calcasieu LA 5 2 1.1 7181 3590 37
Tangipahoa LA 9 3 1.0 3867 2960 38
East Feliciana LA 43 4 1.3 668 3430 10
Rapides LA 10 5 1.0 3569 2710 28
East Baton Rouge LA 2 6 0.8 13114 2950 75
Vernon LA 37 7 1.2 848 1660 9
St. Tammany LA 7 8 0.8 5713 2270 41
Caddo LA 6 9 0.9 7052 2840 32
Ouachita LA 8 10 0.9 5259 3370 33
Jefferson LA 1 13 0.7 15966 3670 47
Orleans LA 3 14 0.8 11102 2850 28
Lafayette LA 4 19 0.6 8072 3360 29

FL and GA

FL
county ST case rank severity R_e cases cases/100k daily cases
Miami-Dade FL 1 1 0.7 149500 5510 1078
Lafayette FL 46 2 1.1 1314 15030 109
Broward FL 2 3 0.8 68106 3570 429
Hillsborough FL 4 4 0.9 35071 2540 221
Orange FL 5 5 0.9 34156 2590 209
Jackson FL 35 6 1.2 2174 4490 34
Marion FL 15 7 0.9 7696 2210 113
Palm Beach FL 3 8 0.7 39919 2760 209
Polk FL 9 9 0.8 15881 2380 120
Pinellas FL 7 10 0.8 19096 1990 98
Duval FL 6 13 0.8 25122 2720 126
Lee FL 8 17 0.8 17719 2470 89
GA
county ST case rank severity R_e cases cases/100k daily cases
Stewart GA 113 1 2.0 289 4780 6
Wheeler GA 150 2 1.8 124 1560 5
Fulton GA 1 3 1.0 23009 2250 254
Lumpkin GA 82 4 1.5 483 1510 18
Gwinnett GA 2 5 0.9 22423 2490 223
Bibb GA 10 6 1.1 4377 2850 76
Richmond GA 8 7 1.0 5477 2720 105
Hall GA 5 8 1.1 6836 3490 74
Clayton GA 7 9 1.1 5805 2080 76
DeKalb GA 3 10 0.9 15588 2100 142
Cobb GA 4 22 0.7 15416 2070 130
Chatham GA 6 30 0.8 6498 2260 60
Muscogee GA 9 55 0.8 5202 2650 34

Texas & Oklahoma

TX
county ST case rank severity R_e cases cases/100k daily cases
Montgomery TX 17 1 1.8 7651 1380 159
Dallas TX 2 2 1.2 70553 2730 1488
Jackson TX 97 3 2.0 453 3060 9
Travis TX 5 4 1.2 25291 2100 273
Hidalgo TX 6 5 1.1 23403 2760 367
Fort Bend TX 10 6 1.1 13865 1880 411
Harris TX 1 7 0.9 95618 2080 848
Tarrant TX 4 9 0.9 39340 1950 485
Cameron TX 8 16 1.0 18731 4440 180
El Paso TX 7 17 0.9 19144 2290 222
Bexar TX 3 39 0.8 44560 2310 128
Nueces TX 9 61 0.6 17667 4900 153
OK
county ST case rank severity R_e cases cases/100k daily cases
Haskell OK 47 1 1.8 115 910 9
Cleveland OK 3 2 1.4 3370 1220 47
Pottawatomie OK 14 3 1.4 650 900 25
Tulsa OK 2 4 1.0 11871 1850 125
Kingfisher OK 43 5 1.5 181 1160 6
Oklahoma OK 1 6 0.9 12059 1540 120
Muskogee OK 15 7 1.2 628 910 13
Comanche OK 8 17 1.0 916 750 9
Rogers OK 5 24 0.8 1183 1300 15
Wagoner OK 7 29 0.8 1006 1290 11
McCurtain OK 9 30 1.0 912 2770 5
Canadian OK 4 34 0.7 1364 1000 9
Texas OK 6 48 0.6 1085 5140 2

Michigan & Wisconsin

MI
county ST case rank severity R_e cases cases/100k daily cases
Emmet MI 51 1 1.7 95 290 5
Wayne MI 1 2 1.0 29670 1680 132
Livingston MI 17 3 1.4 1014 540 17
Oakland MI 2 4 0.9 16902 1350 113
Monroe MI 16 5 1.2 1130 750 17
Macomb MI 3 6 0.9 11948 1380 100
Isabella MI 36 7 1.5 240 340 4
Genesee MI 5 8 1.2 3808 930 17
Kent MI 4 13 0.9 7911 1230 32
Washtenaw MI 6 16 1.0 3229 880 16
Saginaw MI 8 17 0.9 2263 1170 21
Ottawa MI 9 20 0.9 1981 700 12
Jackson MI 7 40 0.7 2478 1560 3
WI
county ST case rank severity R_e cases cases/100k daily cases
Iron WI 51 1 2.2 96 1680 4
Oconto WI 31 2 1.5 326 870 12
Milwaukee WI 1 3 1.0 22808 2390 160
Brown WI 4 4 1.2 4685 1800 47
Washington WI 10 5 1.2 1392 1030 36
Waukesha WI 2 6 0.9 5071 1270 72
Outagamie WI 9 7 1.1 1478 800 23
Dane WI 3 12 0.8 4966 940 34
Kenosha WI 6 14 1.0 2843 1690 16
Racine WI 5 16 0.9 3764 1930 22
Walworth WI 8 22 0.8 1558 1510 17
Rock WI 7 26 0.9 1650 1020 7

Minnesota, North Dakota, and South Dakota

MN
county ST case rank severity R_e cases cases/100k daily cases
Le Sueur MN 30 1 1.6 277 990 8
Sibley MN 52 2 1.8 98 660 2
Hennepin MN 1 3 0.9 21052 1700 157
Cottonwood MN 35 4 1.7 192 1690 2
Waseca MN 36 5 1.5 191 1020 6
Stearns MN 5 6 1.4 3007 1920 14
Chisago MN 31 7 1.5 236 430 5
Dakota MN 3 8 1.0 5052 1210 63
Washington MN 6 11 1.0 2478 980 35
Anoka MN 4 12 1.0 4191 1210 47
Ramsey MN 2 13 0.9 8310 1530 66
Scott MN 9 21 0.8 1786 1250 18
Nobles MN 8 26 1.0 1808 8280 4
Olmsted MN 7 27 0.8 1877 1230 11
SD
county ST case rank severity R_e cases cases/100k daily cases
Meade SD 11 1 2.0 124 450 6
Codington SD 7 2 1.5 190 680 7
Minnehaha SD 1 3 1.1 4757 2550 34
Brown SD 5 4 1.2 503 1300 7
Lincoln SD 3 5 1.1 748 1360 11
Pennington SD 2 6 1.1 974 890 8
Brookings SD 9 7 1.2 165 480 3
Yankton SD 8 9 1.0 166 730 4
Union SD 6 11 1.0 227 1500 1
Beadle SD 4 12 1.0 600 3270 1
ND
county ST case rank severity R_e cases cases/100k daily cases
Ward ND 6 1 1.8 328 480 16
Walsh ND 11 2 1.8 130 1200 4
Burleigh ND 2 3 1.2 1542 1650 42
Stark ND 5 4 1.2 460 1490 21
Grand Forks ND 3 5 1.2 852 1210 19
Cass ND 1 6 1.2 3195 1830 18
Benson ND 8 7 1.4 190 2760 6
Morton ND 4 9 0.9 496 1620 11
Williams ND 7 10 0.8 316 930 3
Mountrail ND 9 15 0.4 158 1560 1

Connecticut, Massachusetts, and Rhode Island

CT
county ST case rank severity R_e cases cases/100k daily cases
Tolland CT 7 1 2.6 1081 710 6
Fairfield CT 1 2 1.2 18398 1950 44
Hartford CT 3 3 1.1 12981 1450 19
New Haven CT 2 4 0.9 13418 1560 20
New London CT 5 5 0.8 1505 560 4
Litchfield CT 4 6 0.9 1642 900 2
Middlesex CT 6 7 0.9 1427 870 2
Windham CT 8 8 0.8 768 660 2
MA
county ST case rank severity R_e cases cases/100k daily cases
Suffolk MA 2 1 2.1 22464 2840 121
Franklin MA 12 2 2.9 414 580 1
Essex MA 3 3 2.1 18254 2340 86
Berkshire MA 11 4 2.8 673 530 1
Worcester MA 4 5 2.1 13848 1680 43
Hampden MA 8 6 2.1 7744 1650 29
Plymouth MA 7 7 2.1 9393 1830 28
Middlesex MA 1 8 1.8 26868 1680 79
Bristol MA 6 10 1.7 9503 1700 25
Norfolk MA 5 11 1.6 10785 1540 27
Barnstable MA 9 12 1.6 1812 850 2
RI
county ST case rank severity R_e cases cases/100k daily cases
Washington RI 3 1 2.2 653 520 8
Providence RI 1 2 1.3 16083 2530 112
Kent RI 2 3 1.4 1586 970 11
Bristol RI 5 4 1.5 328 670 2
Newport RI 4 5 1.0 410 490 1

New York

NY
county ST case rank severity R_e cases cases/100k daily cases
Essex NY 51 1 2.9 82 220 5
Tioga NY 38 2 2.2 201 410 2
Rockland NY 5 3 1.5 14083 4350 19
New York City NY 1 4 0.9 236030 2800 289
Otsego NY 46 5 2.0 122 200 1
St. Lawrence NY 34 6 2.2 264 240 0
Nassau NY 3 7 1.1 44103 3250 46
Suffolk NY 2 8 1.0 44350 2980 51
Erie NY 7 9 1.0 9274 1010 35
Westchester NY 4 10 0.9 36592 3780 32
Monroe NY 8 11 0.9 5264 710 24
Orange NY 6 14 1.0 11309 2990 12
Dutchess NY 9 16 1.0 4724 1610 10

Vermont, New Hampshire, and Maine

VT
county ST case rank severity R_e cases cases/100k daily cases
Windham VT 3 1 1.4 110 250 1
Chittenden VT 1 2 0.9 768 470 3
Franklin VT 2 3 1.2 122 250 0
Addison VT 6 4 0.8 76 210 0
Rutland VT 4 5 0.6 102 170 0
Windsor VT 7 6 0.4 75 140 0
Bennington VT 5 7 0.0 92 260 0
ME
county ST case rank severity R_e cases cases/100k daily cases
York ME 2 1 1.7 709 350 6
Penobscot ME 4 2 1.6 205 140 7
Kennebec ME 5 3 1.9 174 140 0
Cumberland ME 1 4 1.0 2151 740 6
Androscoggin ME 3 5 0.6 583 540 1
NH
county ST case rank severity R_e cases cases/100k daily cases
Merrimack NH 3 1 1.1 479 320 2
Hillsborough NH 1 2 0.7 3968 970 8
Rockingham NH 2 3 0.9 1742 570 4
Carroll NH 8 4 1.2 98 200 0
Cheshire NH 6 5 0.4 108 140 0
Belknap NH 5 6 0.3 122 200 0
Strafford NH 4 7 0.1 368 290 0
Grafton NH 7 8 0.3 107 120 0

Carolinas

SC
county ST case rank severity R_e cases cases/100k daily cases
Marlboro SC 33 1 1.5 614 2260 16
Anderson SC 12 2 1.1 2760 1410 42
Richland SC 3 3 0.9 9655 2360 77
Spartanburg SC 6 4 1.0 4557 1510 42
Lexington SC 5 5 1.0 5348 1870 36
Charleston SC 1 6 0.9 13116 3320 64
Orangeburg SC 14 7 1.0 2662 3010 24
Florence SC 9 9 0.9 3896 2810 37
Horry SC 4 10 0.9 8999 2800 34
Berkeley SC 7 15 0.8 4554 2180 28
Greenville SC 2 18 0.7 11392 2290 33
Beaufort SC 8 19 0.7 4450 2440 24
NC
county ST case rank severity R_e cases cases/100k daily cases
Orange NC 24 1 1.9 1613 1130 45
Cherokee NC 73 2 2.2 329 1190 11
Wake NC 2 3 1.2 13241 1270 134
Rockingham NC 55 4 1.5 647 710 14
Mecklenburg NC 1 5 1.0 23780 2260 146
Scotland NC 59 6 1.4 489 1390 17
Washington NC 85 7 1.6 154 1270 3
Guilford NC 4 11 1.0 6138 1170 50
Union NC 8 13 1.0 3569 1570 45
Gaston NC 6 15 1.1 3707 1710 38
Johnston NC 9 16 1.1 3544 1850 25
Cumberland NC 7 21 0.9 3576 1080 42
Durham NC 3 30 0.9 6553 2140 32
Forsyth NC 5 36 0.8 5672 1530 33

North-Rockies

MT
county ST case rank severity R_e cases cases/100k daily cases
Rosebud MT 10 1 1.5 106 1150 9
Yellowstone MT 1 2 1.0 1628 1030 31
Flathead MT 4 3 1.0 429 440 9
Ravalli MT 13 4 1.5 90 210 1
Big Horn MT 3 5 0.9 563 4210 11
Madison MT 14 6 1.4 89 1080 1
Gallatin MT 2 7 0.8 1024 980 6
Cascade MT 7 8 1.0 189 230 2
Lewis and Clark MT 8 9 0.9 185 280 2
Lake MT 6 10 1.0 191 640 1
Missoula MT 5 12 0.7 397 340 4
Silver Bow MT 9 16 0.2 107 310 0
WY
county ST case rank severity R_e cases cases/100k daily cases
Sheridan WY 13 1 2.1 105 350 6
Fremont WY 1 2 1.8 557 1390 9
Carbon WY 7 3 1.5 191 1230 12
Albany WY 10 4 1.7 108 280 3
Uinta WY 5 5 1.6 282 1370 1
Laramie WY 2 6 1.3 535 550 4
Teton WY 3 7 1.2 398 1730 3
Park WY 9 9 1.1 153 530 2
Sweetwater WY 4 10 1.1 286 650 2
Natrona WY 6 11 0.9 254 320 2
Campbell WY 8 13 0.7 155 320 2
ID
county ST case rank severity R_e cases cases/100k daily cases
Nez Perce ID 17 1 1.8 212 530 9
Payette ID 11 2 1.4 503 2180 14
Power ID 27 3 1.6 89 1150 4
Latah ID 23 4 1.4 169 430 8
Canyon ID 2 5 0.9 6653 3130 71
Ada ID 1 6 0.8 10355 2320 98
Bannock ID 7 7 1.1 587 690 15
Bonneville ID 5 11 0.7 1483 1320 28
Cassia ID 8 12 1.1 569 2410 5
Blaine ID 6 15 1.2 593 2700 2
Kootenai ID 3 20 0.6 2050 1330 14
Twin Falls ID 4 21 0.7 1568 1870 11
Jerome ID 9 22 0.8 546 2330 5

Midwest

OH
county ST case rank severity R_e cases cases/100k daily cases
Jackson OH 79 1 2.1 97 300 4
Henry OH 73 2 1.7 142 520 4
Franklin OH 1 3 0.9 20141 1580 147
Belmont OH 32 4 1.5 658 960 6
Sandusky OH 39 5 1.3 493 830 13
Van Wert OH 82 6 1.8 77 270 1
Lucas OH 4 7 1.0 5899 1360 50
Cuyahoga OH 2 9 0.8 14686 1170 89
Butler OH 7 10 1.0 3338 880 38
Hamilton OH 3 12 0.9 10375 1280 59
Summit OH 6 13 1.0 3921 720 33
Montgomery OH 5 18 0.9 4840 910 41
Mahoning OH 9 57 0.7 2728 1180 11
Marion OH 8 71 0.6 2974 4550 3
IL
county ST case rank severity R_e cases cases/100k daily cases
Boone IL 21 1 2.0 792 1480 6
Cook IL 1 2 1.0 118705 2270 703
Cumberland IL 70 3 1.9 85 780 5
Winnebago IL 7 4 1.6 3973 1390 28
McLean IL 18 5 1.5 895 520 33
Randolph IL 26 6 1.6 588 1810 18
Effingham IL 38 7 1.5 310 910 21
DuPage IL 3 8 1.2 13399 1440 124
Will IL 5 9 1.2 10385 1510 119
Lake IL 2 14 1.0 13672 1940 93
St. Clair IL 6 15 1.1 5143 1950 72
Madison IL 9 21 1.0 3375 1270 71
Kane IL 4 22 1.0 10576 1990 69
McHenry IL 8 35 1.0 3561 1160 31
IN
county ST case rank severity R_e cases cases/100k daily cases
Knox IN 55 1 2.0 235 630 14
Greene IN 46 2 1.9 301 930 8
Daviess IN 44 3 1.6 359 1090 12
Fayette IN 45 4 1.5 339 1460 10
Marion IN 1 5 0.9 17224 1820 108
Lake IN 2 6 0.9 8437 1730 71
St. Joseph IN 5 7 1.0 4051 1500 50
Hamilton IN 6 8 0.9 3352 1060 48
Allen IN 4 14 0.8 4455 1200 41
Elkhart IN 3 16 0.8 5337 2620 31
Hendricks IN 8 26 0.8 2094 1300 15
Vanderburgh IN 7 28 0.7 2266 1250 21
Johnson IN 9 33 0.8 1919 1270 11

Tennessee and Kentucky

TN
county ST case rank severity R_e cases cases/100k daily cases
Lake TN 27 1 2.0 822 10920 6
Blount TN 14 2 1.4 1632 1270 42
Hamilton TN 4 3 1.1 7224 2020 110
Hardin TN 52 4 1.5 556 2160 12
Davidson TN 2 5 0.9 24862 3630 171
Cumberland TN 45 6 1.3 653 1110 23
Gibson TN 24 7 1.3 901 1830 22
Shelby TN 1 9 0.8 25706 2740 169
Knox TN 5 11 0.9 5857 1280 88
Sumner TN 7 12 1.1 3763 2100 35
Williamson TN 6 20 1.0 4022 1840 44
Rutherford TN 3 24 0.9 7308 2380 61
Wilson TN 8 35 0.9 2583 1950 26
Bradley TN 9 52 0.8 2254 2160 25
KY
county ST case rank severity R_e cases cases/100k daily cases
Marion KY 52 1 2.2 158 820 7
Harrison KY 56 2 2.2 137 730 3
Green KY 78 3 1.9 86 780 6
Clark KY 39 4 1.7 208 580 6
Jackson KY 49 5 1.8 166 1240 3
Metcalfe KY 82 6 1.8 81 810 3
Scott KY 17 7 1.4 487 910 14
Fayette KY 2 10 1.1 4763 1490 84
Jefferson KY 1 11 0.9 10540 1370 184
Warren KY 3 14 1.2 2894 2290 29
Daviess KY 6 16 1.3 873 870 12
Boone KY 5 25 1.1 1192 920 10
Shelby KY 7 33 1.0 846 1810 8
Kenton KY 4 38 0.9 1592 970 13
Christian KY 9 45 0.8 798 1100 11
Hardin KY 8 48 0.7 800 740 11

Missouri and Arkansas

MO
county ST case rank severity R_e cases cases/100k daily cases
St. Francois MO 17 1 1.5 720 1090 44
St. Charles MO 3 2 1.2 5054 1300 97
Jefferson MO 5 3 1.3 2298 1030 58
Boone MO 7 4 1.3 1743 990 40
Greene MO 6 5 1.2 2230 770 72
McDonald MO 11 6 1.7 1022 4480 3
St. Louis MO 1 7 0.9 17344 1740 193
Jackson MO 4 11 0.9 4838 700 66
St. Louis city MO 2 21 0.8 5784 1860 46
Clay MO 9 40 0.9 1195 500 13
Jasper MO 8 49 0.8 1404 1180 10
AR
county ST case rank severity R_e cases cases/100k daily cases
Stone AR 61 1 1.8 106 850 6
Johnson AR 21 2 1.6 719 2730 8
Polk AR 48 3 1.6 190 940 7
Jefferson AR 5 4 1.2 1806 2560 31
Little River AR 40 5 1.4 246 1980 8
Jackson AR 53 6 1.4 151 880 7
Craighead AR 7 7 1.2 1582 1500 26
Pulaski AR 2 9 0.9 6347 1610 67
Sebastian AR 4 10 1.0 2581 2020 38
Pope AR 9 11 1.1 1519 2390 20
Washington AR 1 12 1.0 6518 2850 23
Benton AR 3 19 0.9 4998 1930 20
Crittenden AR 8 26 0.8 1532 3130 13
Hot Spring AR 6 42 0.7 1666 4970 8

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 4.0.2
This document took 1194 seconds to compute.
2020-08-21 07:02:19

version history

Today is 2020-08-21.
93 days ago: Multiple states.
85 days ago: \(R_e\) computation.
82 days ago: created color coding for \(R_e\) plots.
77 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.
77 days ago: “persistence” time evolution.
70 days ago: “In control” mapping.
70 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.
62 days ago: Added census API functionality to compute per capita infection rates. Reduced spline spar = 0.65.
57 days ago: Added Per Capita US Map.
55 days ago: Deprecated national map.
51 days ago: added state “Hot 10” analysis.
46 days ago: cleaned up county analysis to show cases and actual data. Moved “Hot 10” analysis to separate web page. Moved “Hot 10” here.
44 days ago: added per capita disease and mortaility to state-level analysis.
32 days ago: changed to county boundaries on national map for per capita disease.
27 days ago: corrected factor of two error in death trend data.
23 days ago: removed “contained and uncontained” analysis, replacing it with county level control map.
18 days ago: added county level “baseline control” and \(R-e\) maps.
14 days ago: fixed normalization error on total disease stats plot.

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.