Updated: 2020-08-17 06:04:23 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
Montana 1.18 5783 140
North Dakota 1.16 8560 158
Kansas 1.14 34621 491
Delaware 1.13 16125 112
Vermont 1.12 1494 7
West Virginia 1.12 8569 143
California 1.11 629456 9114
Kentucky 1.11 41303 740
Missouri 1.11 60526 1144
Iowa 1.10 52355 533
South Dakota 1.10 10039 102
Illinois 1.09 207169 1931
Indiana 1.09 82516 1014
Idaho 1.08 28236 506
Nebraska 1.08 30357 300
Michigan 1.07 101968 781
Maine 1.06 4152 16
Texas 1.06 562964 8093
Georgia 1.05 219774 3407
Virginia 1.05 85186 874
Wisconsin 1.05 66236 852
Wyoming 1.05 3255 35
Arkansas 1.04 52338 742
Ohio 1.04 108962 1185
Pennsylvania 1.04 129338 835
Rhode Island 1.04 18647 98
Minnesota 1.03 65158 700
New Jersey 1.03 188749 394
New York 1.02 430185 664
Oklahoma 1.02 48821 794
Tennessee 1.02 131481 1845
Washington 1.02 70200 724
Florida 1.00 577658 6714
North Carolina 1.00 145853 1474
Oregon 1.00 23194 295
Maryland 0.99 101177 757
Nevada 0.99 61831 832
New Hampshire 0.99 7005 26
Utah 0.99 46840 396
Alabama 0.96 109868 1298
Colorado 0.96 53464 396
Mississippi 0.96 73049 873
New Mexico 0.96 23561 180
South Carolina 0.95 107536 1070
Louisiana 0.92 139317 1269
Connecticut 0.89 50803 78
Arizona 0.86 194838 1085
Massachusetts 0.80 122104 232

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
King WA 1 1 1.0 17695 820 159
Grant WA 9 2 1.2 1822 1920 41
Clallam WA 24 3 1.5 152 200 6
Spokane WA 5 4 1.0 4876 980 76
Pierce WA 3 5 1.0 6826 790 91
Kittitas WA 20 6 1.4 424 950 8
Chelan WA 10 7 1.1 1539 2030 33
Yakima WA 2 8 1.0 11270 4520 50
Snohomish WA 4 9 1.0 6555 830 51
Clark WA 8 10 1.0 2264 490 30
Franklin WA 7 11 1.0 3835 4230 28
Benton WA 6 20 0.8 4046 2080 22
OR
county ST case rank severity R_e cases cases/100k daily cases
Multnomah OR 1 1 1.0 5306 660 61
Marion OR 3 2 1.0 3160 940 38
Washington OR 2 3 1.0 3339 570 38
Malheur OR 6 4 1.1 900 2960 18
Clackamas OR 5 5 1.0 1666 410 19
Jackson OR 9 6 1.1 557 260 14
Umatilla OR 4 7 0.9 2469 3210 28
Lane OR 8 15 0.9 633 170 8
Deschutes OR 7 16 0.9 647 360 7
## Warning: Removed 1 rows containing missing values (geom_col).

California

CA
county ST case rank severity R_e cases cases/100k daily cases
Los Angeles CA 1 1 1.0 223426 2210 2244
Riverside CA 2 2 1.2 46668 1960 713
Stanislaus CA 13 3 1.3 12370 2290 312
Sacramento CA 11 4 1.3 13871 920 353
San Bernardino CA 4 5 1.2 40724 1910 646
Contra Costa CA 14 6 1.3 11145 980 282
San Joaquin CA 9 7 1.2 14984 2050 289
Orange CA 3 9 1.1 43678 1380 513
Alameda CA 8 11 1.2 15080 920 300
Fresno CA 7 12 1.1 20166 2060 397
Kern CA 6 15 1.0 26998 3060 453
San Diego CA 5 18 1.0 34942 1060 349

Four Corners

AZ
county ST case rank severity R_e cases cases/100k daily cases
Pima AZ 2 1 1.1 19568 1920 239
Maricopa AZ 1 2 0.8 130436 3070 618
Pinal AZ 4 3 0.9 8745 2080 40
Yuma AZ 3 4 0.8 11936 5740 51
Mohave AZ 6 5 1.0 3390 1650 25
Yavapai AZ 10 6 0.9 2171 970 24
Cochise AZ 11 7 1.0 1792 1420 18
Coconino AZ 8 9 0.9 3186 2270 14
Navajo AZ 5 11 0.9 5470 5030 13
Apache AZ 7 12 0.8 3262 4560 13
Santa Cruz AZ 9 14 0.8 2713 5820 6
CO
county ST case rank severity R_e cases cases/100k daily cases
El Paso CO 4 1 1.0 5556 810 61
Adams CO 3 2 1.0 6856 1380 55
Denver CO 1 3 0.9 10615 1530 56
Jefferson CO 5 4 1.0 4434 780 35
Arapahoe CO 2 5 0.9 7612 1200 43
Larimer CO 9 6 1.0 1696 500 22
Mesa CO 16 7 1.2 372 250 9
Weld CO 6 8 1.0 3839 1300 19
Douglas CO 8 10 1.0 1833 560 13
Boulder CO 7 11 0.9 2175 680 15
UT
county ST case rank severity R_e cases cases/100k daily cases
Salt Lake UT 1 1 1.0 21859 1950 171
Utah UT 2 2 1.0 9434 1600 107
Davis UT 3 3 1.0 3426 1010 30
Weber UT 4 4 1.0 2970 1200 26
Washington UT 5 5 0.9 2619 1630 18
Wasatch UT 10 6 1.1 598 1960 5
Cache UT 6 7 0.9 1989 1630 10
Tooele UT 9 9 1.0 616 950 5
Summit UT 7 10 1.0 730 1800 3
San Juan UT 8 17 0.6 660 4320 2
NM
county ST case rank severity R_e cases cases/100k daily cases
Eddy NM 13 1 1.3 371 650 10
Lea NM 7 2 1.1 939 1340 23
Doña Ana NM 4 3 1.0 2672 1240 31
Chaves NM 11 4 1.1 550 840 16
Bernalillo NM 1 5 0.9 5383 790 33
Lincoln NM 17 6 1.2 154 790 4
McKinley NM 2 7 1.0 4120 5660 9
San Juan NM 3 8 1.0 3099 2430 7
Santa Fe NM 9 10 1.0 702 470 8
Sandoval NM 5 12 0.8 1167 830 5
Cibola NM 8 16 0.5 728 2700 5
Otero NM 6 19 0.6 1110 1690 1

Mid-Atlantic

NJ
county ST case rank severity R_e cases cases/100k daily cases
Bergen NJ 1 1 1.1 21377 2300 48
Passaic NJ 5 2 1.1 18056 3580 36
Union NJ 6 3 1.2 17024 3080 23
Hudson NJ 3 4 1.1 20039 3000 29
Camden NJ 9 5 1.0 8850 1740 33
Gloucester NJ 16 6 1.1 3419 1180 24
Essex NJ 2 7 1.0 20165 2540 28
Middlesex NJ 4 8 1.0 18304 2210 27
Monmouth NJ 8 9 1.0 10585 1700 26
Ocean NJ 7 11 0.9 10823 1830 22
PA
county ST case rank severity R_e cases cases/100k daily cases
Philadelphia PA 1 1 1.0 32251 2050 130
York PA 13 2 1.2 2869 650 46
Allegheny PA 4 3 1.0 9477 770 86
Fayette PA 25 4 1.2 640 480 21
Delaware PA 3 5 1.0 9722 1730 64
Northumberland PA 27 6 1.3 553 600 13
Lancaster PA 6 7 1.0 6227 1160 46
Montgomery PA 2 10 1.0 10400 1270 41
Berks PA 7 11 1.1 5584 1340 29
Bucks PA 5 14 1.0 7417 1180 31
Chester PA 8 18 0.9 5337 1030 27
Lehigh PA 9 19 1.0 5082 1400 17
MD
county ST case rank severity R_e cases cases/100k daily cases
Prince George’s MD 1 1 1.0 25176 2780 146
Baltimore city MD 4 2 1.0 13556 2210 146
Montgomery MD 2 3 1.0 19067 1830 95
Baltimore MD 3 4 0.9 14081 1700 128
Allegany MD 21 5 1.4 333 460 6
Howard MD 6 6 1.0 4088 1300 34
Frederick MD 7 7 1.1 3205 1290 17
Anne Arundel MD 5 8 0.9 7715 1360 52
Harford MD 9 10 1.0 2151 860 26
Charles MD 8 11 1.0 2172 1380 22
VA
county ST case rank severity R_e cases cases/100k daily cases
Wise VA 47 1 1.7 237 610 18
Floyd VA 72 2 1.7 124 790 11
Fairfax VA 1 3 1.1 16972 1480 88
Greensville VA 26 4 1.4 571 4900 14
Prince William VA 2 5 1.0 9909 2170 70
Pittsylvania VA 25 6 1.2 588 950 20
Virginia Beach city VA 4 7 0.9 5491 1220 78
Loudoun VA 3 8 1.1 5509 1430 35
Chesterfield VA 5 9 1.0 4649 1370 44
Henrico VA 6 11 1.0 4121 1270 37
Norfolk city VA 7 14 0.9 4036 1640 50
Arlington VA 8 15 1.1 3215 1390 22
Newport News city VA 9 21 1.0 1988 1100 23
WV
county ST case rank severity R_e cases cases/100k daily cases
Logan WV 5 1 1.5 351 1040 22
Kanawha WV 1 2 1.1 1079 580 21
Raleigh WV 6 3 1.2 306 400 10
Wood WV 9 4 1.3 276 320 4
Cabell WV 4 5 1.1 459 480 10
Fayette WV 17 6 1.2 170 390 3
Mercer WV 11 7 1.1 241 400 7
Berkeley WV 3 8 1.0 737 650 6
Monongalia WV 2 9 1.0 984 930 5
Jefferson WV 7 18 1.1 304 540 1
Ohio WV 8 24 0.8 280 660 2
DE
county ST case rank severity R_e cases cases/100k daily cases
Sussex DE 2 1 1.2 6130 2790 41
Kent DE 3 2 1.3 2460 1410 24
New Castle DE 1 3 1.0 7535 1360 47

Deep South

AL
county ST case rank severity R_e cases cases/100k daily cases
Clarke AL 31 1 1.3 942 3860 41
Mobile AL 2 2 1.0 11428 2760 190
Jefferson AL 1 3 1.0 14495 2200 179
Montgomery AL 3 4 1.0 7355 3240 76
Washington AL 46 5 1.2 499 3000 16
Baldwin AL 6 6 0.9 3998 1920 52
Jackson AL 28 7 1.1 1195 2290 28
Tuscaloosa AL 5 8 1.0 4630 2250 45
Madison AL 4 9 0.9 5881 1640 56
Shelby AL 7 10 0.9 3763 1780 41
Lee AL 9 12 1.0 3042 1910 28
Marshall AL 8 25 0.8 3385 3560 24
MS
county ST case rank severity R_e cases cases/100k daily cases
Lee MS 10 1 1.2 1737 2050 43
Union MS 37 2 1.2 782 2760 23
Harrison MS 3 3 1.0 2837 1400 51
Stone MS 73 4 1.3 255 1390 10
DeSoto MS 2 5 0.9 3969 2250 48
Tippah MS 53 6 1.2 443 2010 12
Washington MS 9 7 1.0 1834 3900 26
Hinds MS 1 9 0.8 5982 2470 47
Jackson MS 5 11 0.9 2530 1780 35
Forrest MS 8 15 0.9 1944 2570 22
Madison MS 4 23 0.9 2568 2480 18
Rankin MS 6 26 0.9 2435 1610 20
Jones MS 7 32 0.9 2010 2940 16
LA
county ST case rank severity R_e cases cases/100k daily cases
East Baton Rouge LA 2 1 0.9 13006 2930 127
Jefferson LA 1 2 0.9 15912 3660 94
St. Tammany LA 7 3 1.0 5602 2220 60
Lafayette LA 4 4 0.9 8176 3410 95
West Feliciana LA 51 5 1.4 403 2620 7
Red River LA 61 6 1.3 285 3310 9
Ouachita LA 8 7 0.9 5171 3310 47
Tangipahoa LA 9 8 1.0 3745 2870 42
Orleans LA 3 9 0.9 11031 2830 44
Caddo LA 6 13 0.9 7001 2820 47
Calcasieu LA 5 32 0.7 7138 3570 40

FL and GA

FL
county ST case rank severity R_e cases cases/100k daily cases
Lafayette FL 52 1 3.8 789 9020 164
Suwannee FL 33 2 1.6 2256 5140 126
Miami-Dade FL 1 3 1.0 146210 5380 1814
Baker FL 46 4 1.5 1248 4490 87
Broward FL 2 5 0.9 67048 3510 648
Union FL 62 6 1.5 514 3370 30
Palm Beach FL 3 7 0.9 39476 2730 359
Orange FL 5 9 1.0 33490 2530 260
Hillsborough FL 4 10 0.9 34491 2500 295
Polk FL 9 11 1.0 15555 2330 186
Duval FL 6 12 1.0 24826 2690 214
Lee FL 8 14 1.0 17424 2420 124
Pinellas FL 7 15 0.9 18842 1970 144
GA
county ST case rank severity R_e cases cases/100k daily cases
Gwinnett GA 2 1 1.0 21828 2420 305
Fulton GA 1 2 1.0 22286 2180 301
Cobb GA 3 3 1.0 15219 2040 260
DeKalb GA 4 4 1.0 15194 2040 198
Cherokee GA 11 5 1.1 4047 1670 95
Richmond GA 9 6 1.1 5102 2530 116
Floyd GA 27 7 1.2 1824 1880 48
Clayton GA 7 12 1.1 5538 1990 77
Chatham GA 6 15 1.0 6351 2210 94
Hall GA 5 17 1.0 6604 3370 79
Muscogee GA 8 22 1.0 5109 2600 58

Texas & Oklahoma

TX
county ST case rank severity R_e cases cases/100k daily cases
Stephens TX 174 1 2.9 95 1010 13
Collin TX 13 2 1.4 9699 1030 320
Dallas TX 2 3 1.2 59650 2310 746
Fort Bend TX 10 4 1.3 12198 1650 425
Garza TX 169 5 2.7 100 1590 0
Harris TX 1 6 1.0 93657 2030 1218
Tarrant TX 4 7 1.1 37552 1860 616
Nueces TX 9 8 1.1 17383 4820 385
El Paso TX 8 10 1.1 18256 2180 284
Hidalgo TX 6 12 1.1 22092 2600 331
Travis TX 5 16 1.0 24294 2020 215
Cameron TX 7 17 0.8 19235 4560 405
Bexar TX 3 43 0.8 44435 2310 183
OK
county ST case rank severity R_e cases cases/100k daily cases
Hughes OK 42 1 1.6 179 1330 8
Oklahoma OK 1 2 1.0 11726 1500 172
Tulsa OK 2 3 1.0 11593 1800 173
Osage OK 19 4 1.4 517 1090 16
Beckham OK 56 5 1.6 81 360 4
Pittsburg OK 22 6 1.3 472 1060 25
Garfield OK 13 7 1.3 595 960 21
Rogers OK 5 11 1.0 1143 1260 26
Wagoner OK 7 16 1.0 981 1260 18
Cleveland OK 3 17 0.8 3265 1180 38
Canadian OK 4 19 0.9 1348 990 20
Comanche OK 9 28 1.0 891 730 9
Texas OK 6 36 1.1 1078 5100 3
McCurtain OK 8 38 1.0 893 2710 5

Michigan & Wisconsin

MI
county ST case rank severity R_e cases cases/100k daily cases
Muskegon MI 13 1 1.5 1493 860 39
Oakland MI 2 2 1.1 16444 1310 132
Macomb MI 3 3 1.1 11572 1330 134
Wayne MI 1 4 1.0 29159 1660 137
Saginaw MI 8 5 1.1 2186 1130 26
Bay MI 21 6 1.2 712 680 13
Menominee MI 40 7 1.3 182 780 8
Kent MI 4 8 0.9 7816 1220 42
Ottawa MI 9 14 1.0 1937 680 14
Washtenaw MI 6 15 1.0 3181 870 18
Genesee MI 5 26 0.9 3761 920 16
Jackson MI 7 39 0.8 2468 1550 4
WI
county ST case rank severity R_e cases cases/100k daily cases
Sawyer WI 48 1 1.6 109 670 8
Milwaukee WI 1 2 1.0 22292 2340 180
Waukesha WI 3 3 1.1 4842 1210 93
Green WI 38 4 1.4 208 560 8
Oneida WI 40 5 1.4 185 520 9
Dane WI 2 6 1.0 4842 910 49
Iron WI 53 7 1.8 79 1380 1
Brown WI 4 11 1.0 4508 1740 37
Walworth WI 8 16 1.1 1480 1440 21
Outagamie WI 9 18 1.0 1400 760 22
Racine WI 5 24 0.9 3721 1900 33
Kenosha WI 6 32 0.9 2806 1670 21
Rock WI 7 38 0.9 1625 1000 8

Minnesota, North Dakota, and South Dakota

MN
county ST case rank severity R_e cases cases/100k daily cases
Watonwan MN 21 1 1.6 379 3450 9
Hennepin MN 1 2 1.0 20541 1660 198
McLeod MN 29 3 1.5 259 720 13
Ramsey MN 2 4 1.0 8118 1500 90
Dakota MN 3 5 1.0 4836 1160 68
Anoka MN 4 6 1.1 4013 1160 52
St. Louis MN 18 7 1.2 682 340 22
Washington MN 6 8 1.1 2343 920 35
Scott MN 9 9 1.0 1729 1210 28
Olmsted MN 7 10 1.0 1843 1200 17
Stearns MN 5 20 1.0 2961 1890 10
Nobles MN 8 23 1.1 1787 8180 4
SD
county ST case rank severity R_e cases cases/100k daily cases
Yankton SD 9 1 1.5 142 630 4
Minnehaha SD 1 2 1.0 4621 2470 31
Lawrence SD 19 3 1.4 76 300 4
Charles Mix SD 11 4 1.6 110 1180 1
Codington SD 7 5 1.4 157 560 4
Lincoln SD 3 6 1.0 711 1290 11
Pennington SD 2 7 1.0 941 860 8
Brookings SD 8 8 1.2 153 450 3
Brown SD 5 9 1.1 473 1220 5
Union SD 6 16 0.9 226 1490 2
Beadle SD 4 17 1.0 598 3250 1
ND
county ST case rank severity R_e cases cases/100k daily cases
Stark ND 5 1 1.5 388 1260 20
McLean ND 13 2 1.6 105 1090 8
Grand Forks ND 3 3 1.3 767 1090 13
Burleigh ND 2 4 1.1 1399 1490 35
Morton ND 4 5 1.2 464 1520 16
Rolette ND 14 6 1.2 101 690 6
Ward ND 7 7 1.2 271 390 7
Cass ND 1 8 1.0 3135 1800 15
Mountrail ND 9 9 1.2 154 1520 3
Williams ND 6 10 1.1 307 900 5
Benson ND 8 12 1.0 169 2450 5

Connecticut, Massachusetts, and Rhode Island

CT
county ST case rank severity R_e cases cases/100k daily cases
New Haven CT 2 1 1.0 13331 1550 20
Fairfield CT 1 2 0.9 18203 1930 26
Hartford CT 3 3 0.8 12907 1440 16
New London CT 5 4 1.0 1485 550 6
Windham CT 8 5 0.9 762 650 4
Litchfield CT 4 6 1.0 1633 890 3
Middlesex CT 6 7 1.0 1417 870 2
Tolland CT 7 8 0.5 1066 700 1
MA
county ST case rank severity R_e cases cases/100k daily cases
Suffolk MA 2 1 0.9 22155 2800 49
Middlesex MA 1 2 0.8 26712 1670 50
Essex MA 3 3 0.8 18051 2310 39
Norfolk MA 5 4 0.8 10767 1540 24
Bristol MA 6 5 0.8 9468 1690 19
Worcester MA 4 6 0.8 13753 1670 20
Plymouth MA 7 7 0.8 9322 1820 12
Hampden MA 8 8 0.8 7676 1640 12
Barnstable MA 9 10 0.6 1815 850 3
RI
county ST case rank severity R_e cases cases/100k daily cases
Providence RI 1 1 1.1 15732 2480 85
Kent RI 2 2 0.9 1558 950 9
Washington RI 3 3 1.0 624 490 2
Newport RI 4 4 0.9 408 490 2
Bristol RI 5 5 0.8 324 660 1

New York

NY
county ST case rank severity R_e cases cases/100k daily cases
New York City NY 1 1 1.0 234808 2780 326
Suffolk NY 2 2 1.0 44177 2970 57
Monroe NY 8 3 1.1 5165 690 30
Westchester NY 4 4 1.0 36454 3760 34
Erie NY 7 5 1.0 9164 1000 41
Nassau NY 3 6 1.0 43942 3240 42
Onondaga NY 10 7 1.0 3671 790 14
Orange NY 6 8 1.0 11257 2980 12
Rockland NY 5 10 1.1 14001 4330 9
Dutchess NY 9 12 1.0 4682 1590 11

Vermont, New Hampshire, and Maine

VT
county ST case rank severity R_e cases cases/100k daily cases
Chittenden VT 1 1 1.3 750 460 3
Bennington VT 5 2 1.1 92 260 1
Franklin VT 2 3 1.3 120 240 0
Windham VT 3 4 1.3 105 240 0
Rutland VT 4 5 0.8 103 170 1
ME
county ST case rank severity R_e cases cases/100k daily cases
Penobscot ME 5 1 1.4 167 110 2
Cumberland ME 1 2 1.0 2123 730 5
Androscoggin ME 3 3 1.1 577 540 2
York ME 2 4 0.9 689 340 2
Kennebec ME 4 5 0.5 173 140 0
NH
county ST case rank severity R_e cases cases/100k daily cases
Hillsborough NH 1 1 1.0 3936 960 13
Rockingham NH 2 2 0.9 1730 570 6
Strafford NH 4 3 0.9 372 290 3
Cheshire NH 6 4 1.1 107 140 1
Grafton NH 7 5 1.3 106 120 0
Merrimack NH 3 6 1.0 473 320 1
Belknap NH 5 7 1.0 122 200 1
Carroll NH 8 8 0.8 97 200 0

Carolinas

SC
county ST case rank severity R_e cases cases/100k daily cases
Richland SC 3 1 1.0 9466 2320 103
Charleston SC 1 2 1.0 12935 3280 90
Spartanburg SC 8 3 1.0 4417 1460 48
Aiken SC 15 4 1.0 2153 1290 43
Florence SC 10 5 1.0 3808 2750 57
Anderson SC 13 6 1.0 2630 1340 43
Lancaster SC 22 7 1.1 1374 1530 25
Berkeley SC 6 9 1.0 4471 2140 42
Greenville SC 2 10 0.9 11372 2280 71
Beaufort SC 7 11 0.9 4451 2440 54
York SC 9 12 0.9 3835 1480 42
Horry SC 4 13 0.9 8932 2780 52
Lexington SC 5 15 0.9 5234 1830 39
NC
county ST case rank severity R_e cases cases/100k daily cases
Mecklenburg NC 1 1 0.9 23362 2220 165
Wake NC 2 2 1.0 12827 1230 114
Stanly NC 36 3 1.2 1243 2030 29
Cumberland NC 8 4 1.0 3444 1040 54
Pitt NC 16 5 1.1 2286 1290 42
Union NC 9 6 1.0 3390 1500 44
Forsyth NC 5 7 1.0 5572 1500 47
Guilford NC 4 8 1.0 5992 1140 55
Gaston NC 6 10 1.0 3582 1650 40
Durham NC 3 13 1.0 6444 2100 40
Johnston NC 7 41 0.9 3496 1830 29

North-Rockies

MT
county ST case rank severity R_e cases cases/100k daily cases
Rosebud MT 16 1 2.3 78 840 9
Phillips MT 10 2 2.0 114 2760 16
Yellowstone MT 1 3 1.2 1526 970 38
Big Horn MT 3 4 1.0 525 3920 14
Missoula MT 5 5 1.1 387 330 10
Flathead MT 4 6 1.1 404 410 11
Glacier MT 11 7 1.2 90 660 3
Silver Bow MT 9 8 1.1 114 330 4
Gallatin MT 2 9 0.9 1008 960 8
Lewis and Clark MT 8 10 1.0 180 270 3
Cascade MT 7 11 0.9 184 230 2
Lake MT 6 13 0.7 189 630 1
WY
county ST case rank severity R_e cases cases/100k daily cases
Carbon WY 9 1 1.5 132 850 6
Washakie WY 11 2 1.4 99 1220 5
Campbell WY 7 3 1.4 146 310 3
Fremont WY 2 4 1.0 520 1300 3
Natrona WY 6 5 1.0 249 310 2
Sheridan WY 13 6 1.0 83 280 2
Sweetwater WY 5 7 0.9 276 630 2
Laramie WY 1 8 0.8 521 530 3
Park WY 8 9 0.9 145 500 2
Teton WY 3 11 0.6 391 1700 2
Uinta WY 4 12 0.6 282 1370 1
ID
county ST case rank severity R_e cases cases/100k daily cases
Shoshone ID 21 1 1.7 178 1420 13
Ada ID 1 2 1.1 10033 2250 149
Bonneville ID 5 3 1.2 1434 1280 60
Canyon ID 2 4 1.0 6491 3060 110
Bannock ID 8 5 1.2 531 620 14
Kootenai ID 3 6 1.0 2030 1320 32
Bingham ID 12 7 1.2 371 810 12
Twin Falls ID 4 8 1.0 1554 1860 24
Jerome ID 9 13 1.1 530 2260 8
Cassia ID 7 22 0.8 556 2350 5
Blaine ID 6 23 1.1 585 2660 1

## Warning in FUN(X[[i]], ...): NaNs produced

Midwest

OH
county ST case rank severity R_e cases cases/100k daily cases
Madison OH 35 1 1.6 589 1340 31
Franklin OH 1 2 1.0 19646 1540 184
Cuyahoga OH 2 3 1.0 14391 1150 126
Hamilton OH 3 4 1.0 10164 1250 77
Butler OH 7 5 1.1 3193 840 39
Montgomery OH 5 6 1.0 4718 890 52
Allen OH 25 7 1.2 876 850 20
Lucas OH 4 10 0.9 5783 1340 64
Summit OH 6 11 1.0 3832 710 41
Mahoning OH 9 19 1.0 2700 1170 22
Marion OH 8 53 0.9 2969 4540 6
IL
county ST case rank severity R_e cases cases/100k daily cases
Cook IL 1 1 1.0 115986 2220 695
Morgan IL 29 2 1.6 399 1160 23
Logan IL 46 3 1.7 202 690 14
Madison IL 9 4 1.2 3099 1170 78
Hancock IL 63 5 1.6 90 500 6
LaSalle IL 17 6 1.3 985 890 40
Greene IL 65 7 1.6 83 630 7
Will IL 5 9 1.1 9902 1440 98
DuPage IL 3 11 1.0 12924 1390 105
Lake IL 2 12 1.0 13324 1890 95
St. Clair IL 6 15 1.1 4877 1850 71
Kane IL 4 16 1.0 10320 1940 77
McHenry IL 8 30 1.0 3452 1120 34
Winnebago IL 7 49 0.9 3868 1350 13
IN
county ST case rank severity R_e cases cases/100k daily cases
Henry IN 36 1 1.6 490 1010 14
Sullivan IN 63 2 1.5 186 890 12
Vigo IN 21 3 1.3 853 790 35
Marion IN 1 4 1.0 16914 1790 145
Lake IN 2 5 1.1 8136 1670 80
Hamilton IN 6 6 1.2 3151 1000 54
Allen IN 4 7 1.1 4285 1160 55
St. Joseph IN 5 8 1.1 3876 1440 59
Elkhart IN 3 10 1.1 5215 2560 41
Vanderburgh IN 7 14 1.0 2205 1220 36
Hendricks IN 8 19 1.1 2050 1270 21
Johnson IN 9 29 1.0 1887 1250 15

Tennessee and Kentucky

TN
county ST case rank severity R_e cases cases/100k daily cases
Shelby TN 1 1 0.9 25259 2700 254
Davidson TN 2 2 1.0 24242 3540 188
Overton TN 68 3 1.5 274 1250 15
Weakley TN 38 4 1.3 640 1900 35
Hamilton TN 4 5 1.1 6786 1900 91
Knox TN 5 6 1.0 5582 1220 108
Rutherford TN 3 7 1.0 7080 2310 77
Williamson TN 6 11 1.1 3852 1760 46
Bradley TN 9 13 1.1 2175 2080 37
Wilson TN 8 19 1.0 2500 1880 32
Sumner TN 7 21 1.0 3654 2040 34
KY
county ST case rank severity R_e cases cases/100k daily cases
Lewis KY 79 1 2.1 78 580 7
Jefferson KY 1 2 1.2 9830 1280 234
Clay KY 40 3 1.7 185 900 5
Fayette KY 2 4 1.1 4470 1400 90
Johnson KY 75 5 1.6 85 370 5
Madison KY 12 6 1.3 655 730 24
Hardin KY 8 7 1.2 767 710 20
Christian KY 9 8 1.2 748 1040 15
Kenton KY 4 12 1.0 1546 940 17
Warren KY 3 13 1.0 2802 2220 25
Daviess KY 6 21 1.0 829 830 9
Shelby KY 7 22 1.0 822 1760 8
Boone KY 5 32 0.9 1162 900 10

Missouri and Arkansas

MO
county ST case rank severity R_e cases cases/100k daily cases
Washington MO 55 1 1.7 128 510 9
St. Louis MO 1 2 1.0 16703 1670 260
St. Francois MO 19 3 1.5 529 800 24
Greene MO 6 4 1.2 1940 670 58
Cole MO 18 5 1.4 552 720 23
Polk MO 36 6 1.6 250 790 6
Laclede MO 39 7 1.7 226 640 4
Jackson MO 4 9 1.0 4646 670 92
St. Charles MO 3 10 1.0 4672 1200 75
St. Louis city MO 2 13 1.0 5676 1820 74
Boone MO 7 20 1.1 1602 910 29
Jefferson MO 5 21 1.0 2086 930 44
Jasper MO 8 29 1.1 1360 1140 13
Clay MO 9 33 1.0 1164 490 19
AR
county ST case rank severity R_e cases cases/100k daily cases
Lee AR 19 1 1.8 925 9840 5
Pulaski AR 2 2 1.0 6142 1560 91
Saline AR 12 3 1.2 1258 1070 32
Poinsett AR 31 4 1.3 366 1520 18
Lincoln AR 11 5 1.4 1281 9350 10
Chicot AR 18 6 1.1 929 8580 32
Sebastian AR 4 7 1.0 2497 1960 50
Hot Spring AR 6 11 1.2 1623 4840 14
Jefferson AR 5 12 1.0 1717 2440 28
Craighead AR 7 17 1.0 1522 1440 27
Pope AR 9 18 1.0 1441 2260 18
Crittenden AR 8 20 1.0 1492 3040 20
Benton AR 3 24 0.9 4948 1910 26
Washington AR 1 29 0.8 6468 2830 26

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 1416.1 seconds to compute.
2020-08-17 06:27:59

version history

Today is 2020-08-17.
89 days ago: Multiple states.
81 days ago: \(R_e\) computation.
78 days ago: created color coding for \(R_e\) plots.
73 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.
73 days ago: “persistence” time evolution.
66 days ago: “In control” mapping.
66 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.
58 days ago: Added census API functionality to compute per capita infection rates. Reduced spline spar = 0.65.
53 days ago: Added Per Capita US Map.
51 days ago: Deprecated national map.
47 days ago: added state “Hot 10” analysis.
42 days ago: cleaned up county analysis to show cases and actual data. Moved “Hot 10” analysis to separate web page. Moved “Hot 10” here.
40 days ago: added per capita disease and mortaility to state-level analysis.
28 days ago: changed to county boundaries on national map for per capita disease.
23 days ago: corrected factor of two error in death trend data.
19 days ago: removed “contained and uncontained” analysis, replacing it with county level control map.
14 days ago: added county level “baseline control” and \(R-e\) maps.
10 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.