Cerner has created a Reopening Risk Index (CRRI) tool to understand COVID-19 risk trends and forecasts. This information can be used as inputs into reopening of elective non-COVID-19 care services, planning for potential re-surges and for shaping public policy and guidance.

Use the tab selector below to display information by region type.

The two bar graphs below show data for the most populated regions (and all US states).

Highest CRRI and Re in Last 2 Weeks for the 60 Most Populous Countries

Re is an epidemiological measure of the rate of spread of the epidemic. Values less than one represent decreasing case counts, while values greater than one represent increasing case counts. Clicking on a bar will open the summary plot for that country.

According to the WHO:

Without careful planning, and in the absence of scaled up public health and clinical care capacities, the premature lifting of physical distancing measures is likely to lead to an uncontrolled resurgence in COVID-19 transmission and an amplified second wave of cases.

Model details

Historical Cerner Reopening Risk Index for the 60 Most Populous Countries

This plot shows the historical CRRI in each country since March, sorted by CRRI scores over the last 2 weeks.

Clicking on a bar or slot will open the summary plot for that country

Reopening Data by Country

Use the historical trends history and forecasts timeline to view detailed graphs for countries of interest. Use the "Search" box to explore countries of interest further and to find countries not displayed in the charts above.
Country R0 Re Re 2 Wk High Recent Days Re < 1.0 Past Data | Forecast
Afghanistan 1.60
1.13
1.36
0%
Albania 1.50
1.30
1.32
0%
Algeria 2.27
0.89
1.03
79%
Andorra 2.61
0.98
0.98
100%
Argentina 2.11
1.05
1.30
0%
Armenia 1.74
1.13
1.40
0%
Australia 2.00
1.03
1.03
79%
Austria 2.65
0.69
0.96
100%
Azerbaijan 2.18
1.26
1.27
0%
Bahrain 1.40
1.12
1.15
0%
Bangladesh 2.00
1.08
1.16
0%
Belarus 1.85
0.96
0.97
100%
Belgium 2.78
0.84
0.93
100%
Benin 2.09
0.97
1.06
57%
Bolivia (Plurinational State of) 1.39
1.11
1.21
0%
Bosnia and Herzegovina 1.87
1.04
1.04
86%
Brazil 2.48
1.18
1.30
0%
Bulgaria 2.49
0.61
0.84
100%
Burkina Faso 2.78
1.10
1.10
71%
Cabo Verde 1.37
1.22
1.22
57%
Cameroon 1.84
1.03
1.38
0%
Canada 2.48
0.77
0.96
100%
Central African Republic 1.76
1.35
1.57
0%
Chad 2.11
0.96
1.04
86%
Chile 2.30
1.07
1.27
0%
China 6.00
0.99
1.31
36%
Colombia 2.31
0.97
1.05
79%
Democratic Republic of the Congo 1.54
1.28
1.28
0%
Congo 1.52
1.18
1.33
0%
Costa Rica 2.01
1.20
1.20
0%
Côte d'Ivoire 2.37
1.29
1.29
7%
Croatia 1.71
1.02
1.02
93%
Cuba 2.25
1.17
1.17
43%
Cyprus 2.16
0.79
0.87
100%
Czechia 2.48
1.03
1.03
36%
Denmark 3.12
0.78
0.83
100%
Djibouti 2.28
1.32
1.83
0%
Dominican Republic 2.36
1.09
1.09
0%
Ecuador 2.69
1.03
1.03
86%
Egypt 1.87
1.30
1.34
0%
El Salvador 1.40
1.06
1.11
0%
Equatorial Guinea 2.04
1.44
1.44
0%
Estonia 2.81
1.03
1.11
0%
Ethiopia 1.69
1.34
1.63
0%
Finland 1.81
1.07
1.07
79%
France 3.04
1.49
1.49
29%
Gabon 1.74
1.15
1.25
0%
Georgia 1.44
1.32
1.32
64%
Germany 2.91
1.00
1.00
100%
Ghana 2.93
1.11
1.11
50%
Greece 2.13
0.95
0.95
100%
Guatemala 1.63
1.15
1.57
0%
Guinea 2.31
1.24
1.24
0%
Guinea–Bissau 2.59
1.11
1.11
71%
Haiti 2.04
1.51
1.81
0%
Honduras 1.53
1.01
1.28
0%
Hungary 1.95
0.88
0.88
100%
Iceland 2.68
0.87
1.13
50%
India 2.08
0.99
1.17
14%
Indonesia 2.51
0.86
1.06
71%
Iran (Islamic Republic of) 6.00
0.97
1.07
71%
Iraq 1.48
1.39
1.48
0%
Ireland 2.67
0.71
0.71
100%
Israel 2.03
1.60
1.60
29%
Italy 3.35
0.85
0.85
100%
Jamaica 1.88
0.99
0.99
100%
Japan 1.53
0.96
0.96
100%
Jordan 2.45
0.77
1.06
86%
Kazakhstan 3.06
0.98
1.21
21%
Kenya 1.73
1.24
1.29
0%
Republic of Korea 2.41
1.06
1.22
0%
Kuwait 1.52
1.01
1.20
0%
Kyrgyzstan 2.07
1.22
1.26
0%
Latvia 2.25
0.95
0.95
100%
Lebanon 1.85
0.80
1.32
50%
Lithuania 2.69
0.77
1.08
71%
Luxembourg 2.30
0.68
0.97
100%
Madagascar 1.76
1.25
1.66
0%
Malawi 2.45
2.11
2.45
0%
Malaysia 1.99
1.18
1.50
0%
Maldives 2.43
1.07
1.07
64%
Mali 1.63
1.19
1.25
0%
Malta 1.76
0.77
1.15
86%
Mauritania 2.52
1.57
2.21
0%
Mexico 2.30
1.16
1.21
0%
Republic of Moldova 1.92
1.15
1.15
0%
Morocco 2.07
0.63
0.63
100%
Nepal 1.84
1.45
1.55
0%
Netherlands 5.31
0.85
0.85
100%
New Zealand 2.81
1.03
1.04
57%
Nicaragua 2.97
0.92
2.67
14%
Niger 3.03
0.41
0.74
100%
Nigeria 1.52
0.87
0.99
100%
North Macedonia 1.81
1.34
1.34
0%
Norway 2.27
0.82
0.88
100%
Oman 1.96
1.30
1.36
0%
Pakistan 2.29
1.11
1.16
0%
Panama 2.19
1.20
1.22
0%
Paraguay 1.68
1.19
1.19
64%
Peru 1.95
1.16
1.20
0%
Philippines 2.10
1.39
1.43
7%
Poland 2.96
0.97
0.97
100%
Portugal 3.58
1.14
1.14
14%
Qatar 4.29
1.07
1.09
0%
Romania 2.43
0.85
0.85
100%
Russian Federation 2.00
0.88
0.89
100%
San Marino 1.29
1.03
1.03
93%
Sao Tome and Principe 2.61
1.14
1.58
0%
Saudi Arabia 2.13
0.80
1.05
64%
Senegal 1.57
0.95
0.96
100%
Serbia 2.47
0.77
0.89
100%
Sierra Leone 1.53
0.95
1.16
36%
Singapore 1.81
0.76
0.98
100%
Slovakia 3.15
0.82
0.90
100%
Slovenia 3.89
1.01
1.01
86%
Somalia 2.38
1.13
1.13
57%
South Africa 2.02
1.12
1.23
0%
South Sudan 1.84
1.07
1.76
0%
Spain 3.33
1.03
1.03
86%
Sri Lanka 1.74
1.34
1.59
0%
Sudan 1.80
1.03
1.29
0%
Sweden 2.42
1.09
1.09
43%
Switzerland 4.47
1.02
1.04
79%
China, Taiwan Province of China 2.09
1.01
1.05
0%
Tajikistan 4.50
0.93
1.48
36%
United Republic of Tanzania 2.43
0.98
1.01
86%
Thailand 2.79
1.84
1.84
21%
Togo 1.73
1.05
1.09
64%
Tunisia 2.22
1.66
1.75
0%
Turkey 6.00
0.88
0.90
100%
United States of America 2.77
0.93
0.93
100%
Uganda 1.81
1.40
1.46
0%
Ukraine 2.27
1.04
1.04
79%
United Arab Emirates 1.81
0.93
1.11
50%
United Kingdom 2.72
0.83
0.83
100%
Uruguay 6.00
0.93
1.23
21%
Uzbekistan 2.44
1.09
1.13
0%
Venezuela (Bolivarian Republic of) 2.00
1.19
1.84
0%
Yemen 1.75
1.10
1.38
0%
Zambia 2.20
1.03
1.03
79%
Zimbabwe 2.34
1.87
2.34
0%

Highest CRRI and Re in Last 2 Weeks for All States

Re is an epidemiological measure of the rate of spread of the epidemic. Values less than one represent decreasing case counts, while values greater than one represent increasing case counts. Clicking on a bar will open the summary plot for that state.

The White House recommends that states have consistently declining case counts for 2 weeks before reopening. States meeting this criterion have a Cerner Reopening Risk Index (CRRI) of 1 and shown in blue, and states that are close to meeting this criterion have a CRRI of 2 and are shown in purple.

Model details

Historical Cerner Reopening Risk Index for All States

This plot shows the historical CRRI in each state since March, sorted by CRRI scores over the last 2 weeks.

Clicking on a bar or slot will open the summary plot for that state

Reopening Data by State

Use the historical trends history and forecasts timeline to view detailed graphs for states of interest. Use the search box to explore states of interest further.
State R0 Re Re 2 Wk High Recent Days Re < 1.0 Past Data | Forecast Abbrev
Alabama 4.87
1.16
1.17
0% AL
Alaska 1.92
1.64
1.65
0% AK
Arizona 2.34
1.22
1.22
36% AZ
Arkansas 3.89
1.12
1.13
0% AR
California 2.31
1.09
1.09
36% CA
Colorado 3.59
0.98
0.98
100% CO
Connecticut 4.67
0.83
0.83
100% CT
Delaware 1.94
1.08
1.08
57% DC
District of Columbia 4.83
1.00
1.00
100% DE
Florida 2.82
1.09
1.09
79% FL
Georgia 2.78
0.96
1.02
71% GA
Hawaii 2.02
1.17
1.17
79% HI
Idaho 3.88
1.12
1.12
7% ID
Illinois 2.81
0.83
0.83
100% IL
Indiana 2.67
1.06
1.06
71% IN
Iowa 1.75
0.98
0.98
100% IA
Kansas 2.05
1.02
1.02
86% KS
Kentucky 2.22
1.20
1.20
50% KY
Louisiana 6.00
0.99
0.99
100% LA
Maine 4.29
0.96
1.16
43% ME
Maryland 2.61
0.93
0.93
100% MD
Massachusetts 2.48
1.21
1.21
64% MA
Michigan 6.00
1.11
1.11
64% MI
Minnesota 2.14
0.86
0.91
100% MN
Mississippi 4.19
0.99
0.99
100% MS
Missouri 2.69
0.79
1.01
79% MO
Montana 2.37
1.58
1.59
0% MT
Nebraska 1.49
1.09
1.09
64% NE
Nevada 2.28
0.84
0.84
100% NV
New Hampshire 1.67
1.19
1.19
64% NH
New Jersey 4.06
0.88
0.88
100% NJ
New Mexico 1.71
1.00
1.00
93% NM
New York 4.61
0.83
0.83
100% NY
North Carolina 2.31
1.07
1.07
64% NC
North Dakota 1.77
0.92
1.14
71% ND
Ohio 4.57
0.99
0.99
100% OH
Oklahoma 2.20
1.12
1.12
71% OK
Oregon 1.70
1.14
1.14
64% OR
Pennsylvania 3.29
0.88
0.88
100% PA
Puerto Rico 2.36
1.19
1.19
0% PR
Rhode Island 1.94
0.85
0.85
100% RI
South Carolina 2.42
1.20
1.20
43% SC
South Dakota 2.11
0.80
0.87
100% SD
Tennessee 2.64
1.12
1.12
14% TN
Texas 2.81
1.10
1.10
50% TX
Utah 1.94
1.26
1.26
0% UT
Vermont 2.44
0.85
0.99
100% VT
Virginia 2.66
0.96
0.96
100% VA
Washington 2.13
1.09
1.16
0% WA
West Virginia 4.04
1.29
1.35
0% WV
Wisconsin 4.26
0.97
0.97
100% WI
Wyoming 1.70
1.02
1.02
79% WY

Highest CRRI and Re in Last 2 Weeks for the 60 Most Populous CSA

Re is an epidemiological measure of the rate of spread of theepidemic. Values less than one represent decreasing case counts, while values greater than one representincreasing case counts. Clicking on a bar will open the summary plot for that combined statistical area (CSA).

The White House recommends that states have consistently declining case counts for 2 weeks before reopening. States meeting this criterion have a Cerner Reopening Risk Index (CRRI) of 1 and shown in blue, and states that are close to meeting this criterion have a CRRI of 2 and are shown in purple.

Model details

Historical Cerner Reopening Risk Index for the 60 Most Populous CSA

This plot shows the historical CRRI in each CSA since March, sorted by CRRI scores over the last 2 weeks.

Clicking on a bar or slot will open the summary plot for that combined statistical area (CSA)

Reopening Data by CSA

Use the historical trends history and forecasts timeline to view detailed graphs for CSA of interest. Use the "Search" box to explore CSA of interest further and to find CSA not displayed in the charts above.
CSA Name R0 Re Re 2 Wk High States Recent Days Re < 1.0 Past Data | Forecast
Albany–Schenectady, NY 2.14
0.75
0.75
New York 100% 104
Albuquerque–Santa Fe–Las Vegas, NM 1.50
1.04
1.04
New Mexico 86% 106
Amarillo–Borger, TX 1.59
0.59
0.72
Texas 100% 108
Appleton–Oshkosh–Neenah, WI 1.46
0.93
1.11
Wisconsin 43% 118
Asheville–Brevard, NC 1.25
0.92
1.12
North Carolina 71% 120
Atlanta—Athens–Clarke County—Sandy Springs, GA 2.75
1.21
1.25
Georgia 0% 122
Birmingham–Hoover–Talladega, AL 2.80
1.21
1.27
Alabama 0% 142
Bloomsburg–Berwick–Sunbury, PA 2.11
0.78
0.93
Pennsylvania 100% 146
Boise City–Mountain Home–Ontario, ID–OR 2.87
1.13
1.14
Idaho,Oregon 57% 147
Boston–Worcester–Providence, MA–RI–NH–CT 2.41
1.04
1.04
Connecticut,Massachusetts,New Hampshire,Rhode Island 86% 148
Bowling Green–Glasgow, KY 1.95
1.03
1.03
Kentucky 79% 150
Brownsville–Harlingen–Raymondville, TX 1.81
1.05
1.05
Texas 71% 154
Buffalo–Cheektowaga, NY 3.55
0.75
0.90
New York 100% 160
Cape Coral–Fort Myers–Naples, FL 1.59
1.11
1.11
Florida 43% 162
Cedar Rapids–Iowa City, IA 1.96
0.79
0.82
Iowa 100% 168
Charleston–Huntington–Ashland, WV–OH–KY 1.80
1.38
1.38
Kentucky,Ohio,West Virginia 50% 170
Charlotte–Concord, NC–SC 2.52
1.20
1.20
North Carolina,South Carolina 21% 172
Chattanooga–Cleveland–Dalton, TN–GA–AL 1.79
1.10
1.45
Alabama,Georgia,Tennessee 0% 174
Chicago–Naperville, IL–IN–WI 2.85
0.76
0.82
Illinois,Indiana,Wisconsin 100% 176
Cincinnati–Wilmington–Maysville, OH–KY–IN 2.54
0.98
0.98
Indiana,Kentucky,Ohio 100% 178
Cleveland–Akron–Canton, OH 3.40
1.05
1.05
Ohio 71% 184
Columbia–Orangeburg–Newberry, SC 1.64
1.13
1.13
South Carolina 57% 192
Columbus–Auburn–Opelika, GA–AL 2.11
1.13
1.25
Alabama,Georgia 0% 194
Columbus–Marion–Zanesville, OH 2.69
0.93
0.93
Ohio 100% 198
Corpus Christi–Kingsville–Alice, TX 1.67
0.64
0.89
Texas 100% 204
Dallas–Fort Worth, TX–OK 3.00
1.03
1.03
Oklahoma,Texas 79% 206
Davenport–Moline, IA–IL 2.39
0.85
0.85
Illinois,Iowa 100% 209
Dayton–Springfield–Sidney, OH 2.53
0.95
0.95
Ohio 100% 212
Denver–Aurora, CO 2.72
0.99
0.99
Colorado 100% 216
Des Moines–Ames–West Des Moines, IA 1.52
0.91
0.91
Iowa 100% 218
Detroit–Warren–Ann Arbor, MI 6.00
1.14
1.14
Michigan 64% 220
Dothan–Enterprise–Ozark, AL 1.49
1.13
1.28
Alabama 0% 222
Edwards–Glenwood Springs, CO 1.91
0.94
1.12
Colorado 36% 233
Elmira–Corning, NY 1.78
0.86
0.86
New York 100% 236
El Paso–Las Cruces, TX–NM 1.72
0.98
1.10
New Mexico,Texas 64% 238
Fargo–Wahpeton, ND–MN 1.54
0.90
1.07
Minnesota,North Dakota 86% 244
Fayetteville–Lumberton–Laurinburg, NC 1.63
1.19
1.19
North Carolina 50% 246
Fort Wayne–Huntington–Auburn, IN 1.77
1.27
1.28
Indiana 0% 258
Fresno–Madera, CA 1.66
1.08
1.08
California 71% 260
Gainesville–Lake City, FL 1.22
1.02
1.02
Florida 86% 264
Grand Rapids–Wyoming–Muskegon, MI 1.79
1.03
1.03
Michigan 79% 266
Green Bay–Shawano, WI 2.10
0.67
0.67
Wisconsin 100% 267
Greensboro—Winston–Salem—High Point, NC 1.66
1.12
1.12
North Carolina 50% 268
Greenville–Spartanburg–Anderson, SC 2.29
1.43
1.43
South Carolina 29% 273
Harrisburg–York–Lebanon, PA 1.71
1.11
1.11
Pennsylvania 64% 276
Harrisonburg–Staunton–Waynesboro, VA 1.87
0.98
0.98
Virginia 100% 277
Hartford–West Hartford, CT 3.70
0.84
0.84
Connecticut 100% 278
Hickory–Lenoir, NC 1.29
1.21
1.28
North Carolina 0% 280
Houston–The Woodlands, TX 2.19
1.17
1.18
Texas 21% 288
Huntsville–Decatur–Albertville, AL 2.44
1.27
1.27
Alabama 21% 290
Indianapolis–Carmel–Muncie, IN 2.58
0.95
0.95
Indiana 100% 294
Jackson–Vicksburg–Brookhaven, MS 1.91
1.03
1.03
Mississippi 64% 298
Jacksonville–St. Marys–Palatka, FL–GA 2.85
1.15
1.15
Florida,Georgia 57% 300
Kalamazoo–Battle Creek–Portage, MI 1.94
0.98
0.98
Michigan 100% 310
Kansas City–Overland Park–Kansas City, MO–KS 2.05
0.94
0.97
Kansas,Missouri 100% 312
Knoxville–Morristown–Sevierville, TN 2.06
1.11
1.12
Tennessee 0% 314
Kokomo–Peru, IN 1.72
1.13
1.13
Indiana 64% 316
Lafayette–Opelousas–Morgan City, LA 2.18
1.03
1.03
Louisiana 79% 318
Lafayette–West Lafayette–Frankfort, IN 1.72
0.67
0.93
Indiana 100% 320
Lansing–East Lansing–Owosso, MI 2.42
1.01
1.02
Michigan 71% 330
Las Vegas–Henderson, NV–AZ 2.15
0.91
0.91
Arizona,Nevada 100% 332
Lexington–Fayette—Richmond—Frankfort, KY 2.05
1.28
1.28
Kentucky 50% 336
Lima–Van Wert–Celina, OH 1.23
0.90
0.98
Ohio 100% 338
Lincoln–Beatrice, NE 1.68
0.96
0.96
Nebraska 100% 339
Little Rock–North Little Rock, AR 3.89
0.83
0.83
Arkansas 100% 340
Longview–Marshall, TX 1.28
1.28
1.28
Texas 43% 346
Los Angeles–Long Beach, CA 2.34
1.13
1.13
California 29% 348
Louisville/Jefferson County—Elizabethtown—Madison, KY–IN 1.96
1.21
1.21
Indiana,Kentucky 36% 350
Lubbock–Levelland, TX 2.08
0.83
0.96
Texas 100% 352
Macon–Warner Robins, GA 1.96
0.89
0.96
Georgia 100% 356
Madison–Janesville–Beloit, WI 2.53
1.00
1.03
Wisconsin 7% 357
McAllen–Edinburg, TX 1.84
1.21
1.21
Texas 0% 365
Memphis–Forrest City, TN–MS–AR 2.14
1.01
1.01
Arkansas,Mississippi,Tennessee 71% 368
Miami–Fort Lauderdale–Port St. Lucie, FL 3.47
1.09
1.09
Florida 79% 370
Milwaukee–Racine–Waukesha, WI 4.45
1.06
1.06
Wisconsin 0% 376
Minneapolis–St. Paul, MN–WI 1.86
0.82
0.92
Minnesota,Wisconsin 100% 378
Mobile–Daphne–Fairhope, AL 1.72
0.99
0.99
Alabama 100% 380
Modesto–Merced, CA 1.63
1.05
1.05
California 71% 382
Monroe–Ruston–Bastrop, LA 3.35
1.10
1.10
Louisiana 64% 384
Myrtle Beach–Conway, SC–NC 1.33
1.08
1.25
North Carolina,South Carolina 0% 396
Nashville–Davidson—Murfreesboro, TN 2.14
1.09
1.09
Tennessee 21% 400
New Orleans–Metairie–Hammond, LA–MS 5.36
1.05
1.05
Louisiana,Mississippi 71% 406
New York–Newark, NY–NJ–CT–PA 4.34
0.87
0.87
Connecticut,New Jersey,New York,Pennsylvania 100% 408
North Port–Sarasota, FL 1.77
1.10
1.10
Florida 79% 412
Oklahoma City–Shawnee, OK 2.26
0.95
0.95
Oklahoma 100% 416
Omaha–Council Bluffs–Fremont, NE–IA 1.68
1.23
1.23
Iowa,Nebraska 14% 420
Orlando–Deltona–Daytona Beach, FL 2.11
1.13
1.13
Florida 57% 422
Philadelphia–Reading–Camden, PA–NJ–DE–MD 3.07
1.05
1.05
Delaware,Maryland,New Jersey,Pennsylvania 86% 428
Pittsburgh–New Castle–Weirton, PA–OH–WV 2.78
0.92
0.96
Ohio,Pennsylvania,West Virginia 100% 430
Portland–Lewiston–South Portland, ME 2.96
0.97
1.04
Maine 79% 438
Portland–Vancouver–Salem, OR–WA 1.76
1.19
1.19
Oregon,Washington 43% 440
Raleigh–Durham–Chapel Hill, NC 1.58
1.18
1.18
North Carolina 29% 450
Rapid City–Spearfish, SD 1.77
1.12
1.77
South Dakota 0% 452
Reno–Carson City–Fernley, NV 1.92
0.87
0.87
Nevada 100% 456
Rochester–Austin, MN 1.21
1.11
1.21
Minnesota 0% 462
Rochester–Batavia–Seneca Falls, NY 2.53
0.69
1.02
New York 93% 464
Rockford–Freeport–Rochelle, IL 1.68
0.89
0.89
Illinois 100% 466
Rocky Mount–Wilson–Roanoke Rapids, NC 1.65
0.82
0.94
North Carolina 100% 468
Sacramento–Roseville, CA 1.85
1.29
1.30
California 14% 472
Saginaw–Midland–Bay City, MI 1.76
1.00
1.00
Michigan 93% 474
St. Louis–St. Charles–Farmington, MO–IL 2.60
0.86
0.93
Illinois,Missouri 100% 476
Salt Lake City–Provo–Orem, UT 3.06
1.22
1.22
Utah 0% 482
San Jose–San Francisco–Oakland, CA 2.01
1.02
1.02
California 71% 488
Savannah–Hinesville–Statesboro, GA 2.32
0.89
1.28
Georgia 29% 496
Seattle–Tacoma, WA 2.09
0.99
1.00
Washington 93% 500
Sioux City–Vermillion, IA–SD–NE 2.91
0.69
0.69
Iowa,Nebraska,South Dakota 100% 512
South Bend–Elkhart–Mishawaka, IN–MI 1.49
1.08
1.08
Indiana,Michigan 43% 515
Spokane–Spokane Valley–Coeur d'Alene, WA–ID 2.52
1.39
1.76
Idaho,Washington 0% 518
Springfield–Greenfield Town, MA 3.48
1.02
1.02
Massachusetts 86% 521
Springfield–Jacksonville–Lincoln, IL 1.57
1.20
1.20
Illinois 71% 522
Syracuse–Auburn, NY 2.90
0.75
1.00
New York 100% 532
Tallahassee–Bainbridge, FL–GA 1.84
1.02
1.08
Florida,Georgia 71% 533
Toledo–Port Clinton, OH 1.98
0.95
0.95
Ohio 100% 534
Tucson–Nogales, AZ 1.79
1.30
1.30
Arizona 14% 536
Tulsa–Muskogee–Bartlesville, OK 2.01
0.97
1.00
Oklahoma 100% 538
Virginia Beach–Norfolk, VA–NC 1.60
0.98
0.98
North Carolina,Virginia 100% 545
Visalia–Porterville–Hanford, CA 1.92
1.07
1.07
California 50% 546
Washington–Baltimore–Arlington, DC–MD–VA–WV–PA 2.93
0.98
0.98
District of Columbia,Maryland,Pennsylvania,Virginia,West Virginia 100% 548
Wichita–Arkansas City–Winfield, KS 1.67
1.21
1.21
Kansas 57% 556
Youngstown–Warren, OH–PA 1.87
1.12
1.14
Ohio,Pennsylvania 7% 566

Highest CRRI and Re in Last 2 Weeks for the 60 Most Populous CBSA

Re is an epidemiological measure of the rate of spread of theepidemic. Values less than one represent decreasing case counts, while values greater than one representincreasing case counts. Clicking on a bar will open the summary plot for that core-based statistical area (CBSA).

The White House recommends that states have consistently declining case counts for 2 weeks before reopening. States meeting this criterion have a Cerner Reopening Risk Index (CRRI) of 1 and shown in blue, and states that are close to meeting this criterion have a CRRI of 2 and are shown in purple.

Model details

Historical Cerner Reopening Risk Index for the 60 Most Populous CBSA

This plot shows the historical CRRI in each CBSA since March, sorted by CRRI scores over the last 2 weeks.

Clicking on a bar or slot will open the summary plot for that core-based statistical area (CBSA)

Reopening Data by CBSA

Use the historical trends history and forecasts timeline to view detailed graphs for CBSA of interest. Use the "Search" box to explore CBSA of interest further and to find CBSA not displayed in the charts above.
CBSA Name R0 Re Re 2 Wk High States Recent Days Re < 1.0 Past Data | Forecast
Albertville, AL μSA 1.39
1.14
1.14
Alabama 86% 10700
Alexander City, AL μSA 1.84
0.94
1.03
Alabama 79% 10760
Auburn–Opelika, AL MSA 1.42
0.91
1.32
Alabama 21% 12220
Birmingham–Hoover, AL MSA 2.05
1.16
1.24
Alabama 0% 13820
Bullock County, AL 1.98
1.16
1.98
Alabama 0% CN0101100000000
Butler County, AL 1.77
0.79
0.97
Alabama 100% CN0101300000000
Columbus, GA–AL MSA 2.06
1.15
1.29
Alabama,Georgia 0% 17980
Decatur, AL MSA 1.58
1.30
1.58
Alabama 0% 19460
Franklin County, AL 1.94
0.95
1.07
Alabama 36% CN0105900000000
Mobile, AL MSA 1.80
1.03
1.03
Alabama 64% 33660
Montgomery, AL MSA 1.44
1.05
1.31
Alabama 0% 33860
Tuscaloosa, AL MSA 1.47
1.25
1.47
Alabama 0% 46220
Valley, AL μSA 1.63
1.46
1.50
Alabama 21% 46740
North Slope Borough, AK 1.58
1.58
1.58
Alaska,Illinois,Iowa,Kansas,Missouri,Nebraska,North Carolina,Tennessee,Texas 0% 00185
Apache County, AZ 1.85
1.40
1.40
Arizona 29% CN0400100000000
Flagstaff, AZ MSA 1.66
1.00
1.00
Arizona 93% 22380
Nogales, AZ μSA 1.76
1.46
1.76
Arizona 0% 35700
Phoenix–Mesa–Scottsdale, AZ MSA 2.22
1.20
1.20
Arizona 43% 38060
Prescott, AZ MSA 1.86
0.60
0.60
Arizona 100% 39140
Show Low, AZ μSA 2.17
1.28
1.28
Arizona 21% 43320
Tucson, AZ MSA 1.77
1.25
1.25
Arizona 36% 46060
Yuma, AZ MSA 1.56
1.20
1.39
Arizona 0% 49740
Fayetteville–Springdale–Rogers, AR–MO MSA 1.83
1.41
1.83
Arkansas,Missouri 0% 22220
Forrest City, AR μSA 2.15
0.46
1.24
Arkansas 71% 22620
Little Rock–North Little Rock–Conway, AR MSA 1.09
1.09
1.09
Arkansas 36% 30780
Memphis, TN–MS–AR MSA 2.13
1.11
1.11
Arkansas,Mississippi,Tennessee 29% 32820
Pine Bluff, AR MSA 2.04
0.74
0.74
Arkansas 100% 38220
Russellville, AR μSA 2.11
1.12
1.95
Arkansas 0% 40780
Sevier County, AR 1.87
1.63
1.87
Arkansas 0% CN0513300000000
Bakersfield, CA MSA 2.46
1.18
1.18
California 14% 12540
El Centro, CA MSA 1.40
1.26
1.26
California 0% 20940
Fresno, CA MSA 1.51
1.06
1.06
California 71% 23420
Hanford–Corcoran, CA MSA 1.81
1.50
1.50
California 0% 25260
Los Angeles–Long Beach–Anaheim, CA MSA 2.34
1.07
1.07
California 21% 31080
Modesto, CA MSA 1.34
1.17
1.17
California 50% 33700
Oxnard–Thousand Oaks–Ventura, CA MSA 1.89
1.32
1.32
California 0% 37100
Riverside–San Bernardino–Ontario, CA MSA 2.12
1.06
1.06
California 36% 40140
Sacramento—Roseville—Arden–Arcade, CA MSA 1.80
1.24
1.26
California 7% 40900
Salinas, CA MSA 1.35
1.32
1.35
California 0% 41500
San Diego–Carlsbad, CA MSA 1.80
1.00
1.00
California 100% 41740
San Francisco–Oakland–Hayward, CA MSA 2.27
0.98
1.00
California 71% 41860
San Jose–Sunnyvale–Santa Clara, CA MSA 1.64
0.88
1.06
California 50% 41940
Santa Maria–Santa Barbara, CA MSA 1.93
1.19
1.19
California 64% 42200
Santa Rosa, CA MSA 1.22
0.86
1.20
California 57% 42220
Stockton–Lodi, CA MSA 1.66
1.41
1.41
California 0% 44700
Vallejo–Fairfield, CA MSA 1.32
1.32
1.32
California 29% 46700
Visalia–Porterville, CA MSA 1.96
0.91
0.91
California 100% 47300
Boulder, CO MSA 1.40
0.78
0.84
Colorado 100% 14500
Colorado Springs, CO MSA 2.52
1.01
1.13
Colorado 0% 17820
Denver–Aurora–Lakewood, CO MSA 2.47
1.02
1.02
Colorado 79% 19740
Edwards, CO μSA 1.80
0.98
1.09
Colorado 57% 20780
Fort Collins, CO MSA 1.20
1.05
1.05
Colorado 64% 22660
Fort Morgan, CO μSA 1.83
0.59
0.72
Colorado 100% 22820
Greeley, CO MSA 2.11
0.96
0.96
Colorado 100% 24540
Sterling, CO μSA 2.75
0.90
1.09
Colorado 57% 44540
Bridgeport–Stamford–Norwalk, CT MNECTA 4.39
0.84
0.84
Connecticut 100% 71950
Danbury, CT MNECTA 3.28
0.82
0.82
Connecticut 100% 72850
Hartford–West Hartford–East Hartford, CT MNECTA 2.95
0.79
0.79
Connecticut 100% 73450
Litchfield, CT LMA 1.86
0.85
0.85
Connecticut 100% 81580
New Haven, CT MNECTA 3.23
0.89
0.89
Connecticut 100% 75700
Norwich–New London–Westerly, CT–RI MNECTA 1.56
0.81
0.93
Connecticut,Rhode Island 100% 76450
Springfield, MA–CT MNECTA 4.54
0.92
0.92
Connecticut,Massachusetts 100% 78100
Torrington, CT μNECTA 1.86
0.77
0.77
Connecticut 100% 78400
Waterbury, CT MNECTA 2.66
0.82
0.82
Connecticut 100% 78700
Worcester, MA–CT MNECTA 2.60
1.01
1.01
Connecticut,Massachusetts 93% 79600
Dover, DE MSA 2.12
1.09
1.09
Delaware 57% 20100
Philadelphia–Camden–Wilmington, PA–NJ–DE–MD MSA 3.12
1.06
1.06
Delaware,Maryland,New Jersey,Pennsylvania 79% 37980
Salisbury, MD–DE MSA 1.73
0.99
0.99
Delaware,Maryland 100% 41540
Washington–Arlington–Alexandria, DC–VA–MD–WV MSA 2.66
0.99
0.99
District of Columbia,Maryland,Virginia,West Virginia 100% 47900
Cape Coral–Fort Myers, FL MSA 1.62
1.09
1.09
Florida 64% 15980
Clewiston, FL μSA 1.32
1.32
1.32
Florida 0% 17500
Deltona–Daytona Beach–Ormond Beach, FL MSA 1.93
1.15
1.15
Florida 43% 19660
Hamilton County, FL 2.39
0.99
1.05
Florida 93% CN1204700000000
Jackson County, FL 2.13
0.63
1.10
Florida 86% CN1206300000000
Jacksonville, FL MSA 2.60
1.22
1.22
Florida 50% 27260
Lakeland–Winter Haven, FL MSA 2.03
1.24
1.24
Florida 43% 29460
Liberty County, FL 2.50
0.87
0.87
Florida 100% CN1207700000000
Miami–Fort Lauderdale–West Palm Beach, FL MSA 3.52
1.12
1.12
Florida 71% 33100
Naples–Immokalee–Marco Island, FL MSA 1.85
1.20
1.20
Florida 0% 34940
North Port–Sarasota–Bradenton, FL MSA 1.75
0.93
0.93
Florida 100% 35840
Orlando–Kissimmee–Sanford, FL MSA 4.57
1.23
1.23
Florida 50% 36740
Pensacola–Ferry Pass–Brent, FL MSA 2.16
1.19
1.19
Florida 57% 37860
Port St. Lucie, FL MSA 2.25
1.21
1.27
Florida 0% 38940
Tallahassee, FL MSA 1.77
0.98
1.07
Florida 93% 45220
Tampa–St. Petersburg–Clearwater, FL MSA 1.76
1.15
1.15
Florida 64% 45300
Albany, GA MSA 2.62
0.89
1.02
Georgia 71% 10500
Americus, GA μSA 2.35
1.41
1.44
Georgia 0% 11140
Athens–Clarke County, GA MSA 1.49
1.29
1.49
Georgia 0% 12020
Atlanta–Sandy Springs–Roswell, GA MSA 2.68
1.13
1.25
Georgia 0% 12060
Augusta–Richmond County, GA–SC MSA 1.89
1.26
1.26
Georgia,South Carolina 50% 12260
Chattanooga, TN–GA MSA 1.48
1.27
1.48
Georgia,Tennessee 0% 16860
Cornelia, GA μSA 1.99
0.79
1.03
Georgia 86% 18460
Dalton, GA MSA 1.36
1.14
1.36
Georgia 0% 19140
Gainesville, GA MSA 2.04
1.07
1.08
Georgia 36% 23580
Macon–Bibb County, GA MSA 1.65
0.90
0.98
Georgia 100% 31420
Milledgeville, GA μSA 1.54
0.78
0.96
Georgia 100% 33300
Mitchell County, GA 1.55
1.27
1.28
Georgia 57% CN1320500000000
Moultrie, GA μSA 1.75
1.36
1.75
Georgia 0% 34220
Savannah, GA MSA 2.17
0.80
1.20
Georgia 43% 42340
Thomaston, GA μSA 1.97
0.83
0.98
Georgia 100% 45580
Valdosta, GA MSA 1.51
1.23
1.51
Georgia 0% 46660
Urban Honolulu, HI MSA 1.85
1.28
1.28
Hawaii 57% 46520
Boise City, ID MSA 2.89
1.07
1.07
Idaho 79% 14260
Hailey, ID μSA 2.67
1.18
1.18
Idaho 36% 25200
Twin Falls, ID MSA 1.27
0.98
1.12
Idaho 71% 46300
Champaign–Urbana, IL MSA 1.70
0.93
1.08
Illinois 79% 16580
Chicago–Naperville–Elgin, IL–IN–WI MSA 2.85
0.82
0.82
Illinois,Indiana,Wisconsin 100% 16980
Davenport–Moline–Rock Island, IA–IL MSA 2.17
0.83
0.83
Illinois,Iowa 100% 19340
Kankakee, IL MSA 1.75
1.18
1.18
Illinois 57% 28100
Pulaski County, IL 1.79
1.24
1.79
Illinois,Kansas,Kentucky,Michigan,Minnesota,Mississippi,Missouri,Texas 0% 00153
Rockford, IL MSA 1.56
0.94
0.94
Illinois 100% 40420
St. Louis, MO–IL MSA 2.58
0.89
0.92
Illinois,Missouri 100% 41180
Springfield, IL MSA 1.67
1.03
1.04
Illinois 64% 44100
Cincinnati, OH–KY–IN MSA 2.40
1.04
1.04
Indiana,Kentucky,Ohio 79% 17140
Columbus, IN MSA 1.29
1.01
1.01
Indiana 93% 18020
Elkhart–Goshen, IN MSA 1.37
1.19
1.20
Indiana 0% 21140
Evansville, IN–KY MSA 1.16
1.02
1.02
Indiana,Kentucky 79% 21780
Fort Wayne, IN MSA 1.51
1.29
1.29
Indiana 0% 23060
Indianapolis–Carmel–Anderson, IN MSA 2.56
0.94
0.94
Indiana 100% 26900
Lafayette–West Lafayette, IN MSA 1.77
0.96
0.96
Indiana 100% 29200
Logansport, IN μSA 2.77
0.76
0.76
Indiana 100% 30900
Louisville/Jefferson County, KY–IN MSA 1.96
1.20
1.20
Indiana,Kentucky 36% 31140
Michigan City–La Porte, IN MSA 1.86
0.88
0.96
Indiana 100% 33140
Seymour, IN μSA 1.42
0.89
0.89
Indiana 100% 42980
South Bend–Mishawaka, IN–MI MSA 1.62
0.90
0.95
Indiana,Michigan 100% 43780
Cedar Rapids, IA MSA 3.57
0.78
0.79
Iowa 100% 16300
Crawford County, IA 2.21
0.77
0.91
Iowa 100% CN1904700000000
Des Moines–West Des Moines, IA MSA 1.50
0.89
0.89
Iowa 100% 19780
Iowa City, IA MSA 1.44
0.47
0.79
Iowa 100% 26980
Louisa County, IA 3.64
0.92
1.08
Iowa 86% CN1911500000000
Marshalltown, IA μSA 2.29
0.52
0.73
Iowa 100% 32260
Muscatine, IA μSA 1.62
0.93
0.93
Iowa 100% 34700
Newton, IA μSA 1.61
0.88
0.88
Iowa 100% 35500
Omaha–Council Bluffs, NE–IA MSA 1.67
1.22
1.22
Iowa,Nebraska 14% 36540
Ottumwa, IA μSA 2.52
0.94
0.94
Iowa 100% 36900
Sioux City, IA–NE–SD MSA 2.96
0.67
0.67
Iowa,Nebraska,South Dakota 100% 43580
Storm Lake, IA μSA 2.23
1.74
2.23
Iowa 0% 44740
Tama County, IA 1.48
0.48
0.86
Iowa 100% CN1917100000000
Waterloo–Cedar Falls, IA MSA 2.39
0.87
0.88
Iowa 100% 47940
Wright County, IA 2.07
1.04
2.07
Iowa,Kentucky,Texas,Virginia 0% 00197
Dodge City, KS μSA 2.60
1.09
1.09
Kansas 64% 19980
Emporia, KS μSA 1.98
1.50
1.50
Kansas 29% 21380
Garden City, KS μSA 2.47
0.67
0.67
Kansas 100% 23780
Kansas City, MO–KS MSA 2.09
0.84
0.97
Kansas,Missouri 100% 28140
Liberal, KS μSA 3.02
1.17
1.17
Kansas 57% 30580
St. Joseph, MO–KS MSA 2.23
0.86
0.97
Kansas,Missouri 100% 41140
Topeka, KS MSA 1.36
0.80
1.34
Kansas 50% 45820
Wichita, KS MSA 1.66
0.87
0.93
Kansas 100% 48620
Bowling Green, KY MSA 1.97
1.11
1.11
Kentucky 71% 14540
Central City, KY μSA 1.87
1.28
1.28
Kentucky 64% 16420
Lexington–Fayette, KY MSA 2.01
1.20
1.20
Kentucky 57% 30460
Alexandria, LA MSA 1.28
1.06
1.18
Louisiana 0% 10780
Baton Rouge, LA MSA 4.58
0.86
0.86
Louisiana 100% 12940
Hammond, LA MSA 2.68
0.99
0.99
Louisiana 100% 25220
Houma–Thibodaux, LA MSA 1.81
1.08
1.08
Louisiana 71% 26380
Lafayette, LA MSA 2.05
0.98
1.09
Louisiana 57% 29180
Lake Charles, LA MSA 2.64
0.99
1.00
Louisiana 100% 29340
Monroe, LA MSA 3.13
1.07
1.07
Louisiana 64% 33740
New Orleans–Metairie, LA MSA 6.00
1.06
1.06
Louisiana 71% 35380
Shreveport–Bossier City, LA MSA 2.41
0.92
1.21
Louisiana 50% 43340
Bridgton–Paris, ME LMA 1.38
1.06
1.06
Maine 43% 80460
Brunswick, ME μNECTA 1.30
0.92
0.98
Maine 100% 72250
Dover–Durham, NH–ME MNECTA 1.72
0.90
0.90
Maine,New Hampshire 100% 73050
Farmington, ME LMA 1.37
0.93
1.27
Maine 64% 80980
Lewiston–Auburn, ME MNECTA 1.39
1.05
1.31
Maine 0% 74650
Portland–South Portland, ME MNECTA 2.69
1.04
1.04
Maine 79% 76750
Portsmouth, NH–ME MNECTA 2.01
0.95
0.95
Maine,New Hampshire 100% 76900
Baltimore–Columbia–Towson, MD MSA 2.43
1.00
1.00
Maryland 93% 12580
California–Lexington Park, MD MSA 1.35
1.30
1.30
Maryland 14% 15680
Hagerstown–Martinsburg, MD–WV MSA 1.93
1.03
1.14
Maryland,West Virginia 14% 25180
Athol, MA μNECTA 2.29
0.99
0.99
Massachusetts 100% 70450
Barnstable Town, MA MNECTA 3.36
1.20
1.20
Massachusetts 64% 70900
Boston–Cambridge–Nashua, MA–NH MNECTA 2.39
1.17
1.17
Massachusetts,New Hampshire 71% 71650
Buckland, MA LMA 1.93
1.25
1.25
Massachusetts 64% 80500
Great Barrington, MA LMA 2.05
1.03
1.18
Massachusetts 0% 81100
Leominster–Gardner, MA MNECTA 2.18
0.99
0.99
Massachusetts 100% 74500
New Bedford, MA MNECTA 3.50
1.13
1.13
Massachusetts 71% 75550
North Adams, MA–VT μNECTA 2.08
1.24
1.26
Massachusetts,Vermont 0% 76150
Pittsfield, MA MNECTA 1.72
1.15
1.15
Massachusetts 71% 76600
Providence–Warwick, RI–MA MNECTA 2.18
0.95
0.95
Massachusetts,Rhode Island 100% 77200
Ann Arbor, MI MSA 1.91
1.19
1.19
Michigan 50% 11460
Detroit–Warren–Dearborn, MI MSA 6.00
1.12
1.12
Michigan 71% 19820
Flint, MI MSA 2.84
1.12
1.12
Michigan 71% 22420
Grand Rapids–Wyoming, MI MSA 1.60
1.01
1.02
Michigan 79% 24340
Jackson, MI MSA 2.11
2.07
2.11
Michigan 14% 27100
Kalamazoo–Portage, MI MSA 1.48
1.00
1.00
Michigan 86% 28020
Lansing–East Lansing, MI MSA 2.37
1.10
1.11
Michigan 57% 29620
Muskegon, MI MSA 1.49
0.85
0.95
Michigan 100% 34740
Niles–Benton Harbor, MI MSA 1.13
0.92
0.92
Michigan 100% 35660
Saginaw, MI MSA 1.85
0.93
0.93
Michigan 100% 40980
Austin, MN μSA 2.02
1.39
2.02
Minnesota 0% 12380
Fargo, ND–MN MSA 1.57
0.86
1.05
Minnesota,North Dakota 86% 22020
Faribault–Northfield, MN μSA 2.19
0.84
1.13
Minnesota 71% 22060
Grand Forks, ND–MN MSA 2.40
1.28
1.28
Minnesota,North Dakota 71% 24220
Minneapolis–St. Paul–Bloomington, MN–WI MSA 1.86
0.80
0.99
Minnesota,Wisconsin 100% 33460
Rochester, MN MSA 1.06
0.93
0.97
Minnesota 100% 40340
St. Cloud, MN MSA 2.99
0.58
0.58
Minnesota 100% 41060
Willmar, MN μSA 2.35
0.71
0.71
Minnesota 100% 48820
Worthington, MN μSA 4.26
0.87
0.87
Minnesota 100% 49380
Greenwood, MS μSA 1.27
0.88
1.06
Mississippi 50% 24900
Gulfport–Biloxi–Pascagoula, MS MSA 1.71
1.10
1.10
Mississippi 71% 25060
Hattiesburg, MS MSA 1.51
0.93
0.93
Mississippi 100% 25620
Jackson, MS MSA 3.02
0.90
0.95
Mississippi 100% 27140
Laurel, MS μSA 1.30
1.08
1.12
Mississippi 0% 29860
Leake County, MS 1.55
1.09
1.09
Mississippi 64% CN2807900000000
Meridian, MS μSA 1.82
0.92
0.92
Mississippi 100% 32940
Neshoba County, MS 1.55
1.07
1.07
Mississippi 0% CN2809900000000
Scott County, MS 1.61
0.96
0.96
Mississippi 100% CN2812300000000
Starkville, MS μSA 1.97
1.89
1.97
Mississippi 0% 44260
Colfax County, NE 2.68
0.61
0.61
Nebraska 100% CN3103700000000
Columbus, NE μSA 2.41
1.01
1.01
Nebraska 93% 18100
Fremont, NE μSA 1.21
1.06
1.14
Nebraska 0% 23340
Grand Island, NE MSA 2.71
0.93
0.93
Nebraska 100% 24260
Lexington, NE μSA 2.75
0.80
0.80
Nebraska 100% 30420
Lincoln, NE MSA 1.76
0.93
0.93
Nebraska 100% 30700
Norfolk, NE μSA 2.00
1.16
1.16
Nebraska 79% 35740
Saline County, NE 2.82
0.91
0.91
Nebraska 100% CN3115100000000
Las Vegas–Henderson–Paradise, NV MSA 2.14
0.80
0.80
Nevada 100% 29820
Reno, NV MSA 1.94
0.82
0.92
Nevada 100% 39900
Hillsborough, NH LMA 1.54
1.25
1.25
New Hampshire 50% 81380
Manchester, NH MNECTA 1.60
1.17
1.17
New Hampshire 64% 74950
Peterborough, NH LMA 1.56
1.26
1.26
New Hampshire 50% 82220
Raymond, NH LMA 1.80
1.06
1.06
New Hampshire 86% 82460
Allentown–Bethlehem–Easton, PA–NJ MSA 2.49
0.91
0.91
New Jersey,Pennsylvania 100% 10900
Atlantic City–Hammonton, NJ MSA 1.64
0.90
0.90
New Jersey 100% 12100
New York–Newark–Jersey City, NY–NJ–PA MSA 4.32
0.85
0.85
New Jersey,New York,Pennsylvania 100% 35620
Ocean City, NJ MSA 1.78
1.00
1.08
New Jersey 36% 36140
Trenton, NJ MSA 2.39
0.86
0.86
New Jersey 100% 45940
Vineland–Bridgeton, NJ MSA 1.94
1.00
1.00
New Jersey 100% 47220
Albuquerque, NM MSA 1.52
1.09
1.09
New Mexico 57% 10740
Farmington, NM MSA 1.47
1.02
1.02
New Mexico 93% 22140
Gallup, NM μSA 1.52
1.05
1.05
New Mexico 79% 23700
Las Cruces, NM MSA 1.15
0.84
1.10
New Mexico 64% 29740
Albany–Schenectady–Troy, NY MSA 2.04
0.84
0.84
New York 100% 10580
Binghamton, NY MSA 1.29
0.92
1.13
New York 50% 13780
Buffalo–Cheektowaga–Niagara Falls, NY MSA 3.33
0.70
0.89
New York 100% 15380
Glens Falls, NY MSA 1.36
0.78
0.78
New York 100% 24020
Hudson, NY μSA 1.52
0.76
0.80
New York 100% 26460
Kingston, NY MSA 2.41
0.80
0.80
New York 100% 28740
Rochester, NY MSA 2.56
0.70
1.02
New York 93% 40380
Sullivan County, NY 2.69
0.76
0.77
New York 100% CN3610500000000
Syracuse, NY MSA 2.90
0.67
0.99
New York 100% 45060
Utica–Rome, NY MSA 1.77
0.92
0.97
New York 100% 46540
Asheville, NC MSA 1.24
0.93
1.11
North Carolina 71% 11700
Burlington, NC MSA 1.26
1.26
1.26
North Carolina 0% 15500
Charlotte–Concord–Gastonia, NC–SC MSA 2.56
1.23
1.23
North Carolina,South Carolina 29% 16740
Duplin County, NC 1.52
0.98
1.18
North Carolina 57% CN3706100000000
Durham–Chapel Hill, NC MSA 1.42
1.20
1.20
North Carolina 0% 20500
Fayetteville, NC MSA 1.52
1.17
1.17
North Carolina 36% 22180
Goldsboro, NC MSA 2.43
1.04
1.17
North Carolina 43% 24140
Greensboro–High Point, NC MSA 1.62
0.90
0.94
North Carolina 100% 24660
Henderson, NC μSA 1.38
1.35
1.38
North Carolina 0% 25780
Hickory–Lenoir–Morganton, NC MSA 1.33
1.20
1.25
North Carolina 0% 25860
Lumberton, NC μSA 2.00
1.20
1.20
North Carolina 50% 31300
Myrtle Beach–Conway–North Myrtle Beach, SC–NC MSA 1.27
1.09
1.27
North Carolina,South Carolina 0% 34820
North Wilkesboro, NC μSA 2.21
0.74
0.77
North Carolina 100% 35900
Oxford, NC μSA 1.62
1.20
1.27
North Carolina 0% 37080
Raleigh, NC MSA 1.93
1.16
1.16
North Carolina 0% 39580
Sampson County, NC 1.42
0.99
1.17
North Carolina 57% CN3716300000000
Sanford, NC μSA 1.74
1.41
1.41
North Carolina 36% 41820
Virginia Beach–Norfolk–Newport News, VA–NC MSA 1.60
0.98
0.98
North Carolina,Virginia 100% 47260
Winston–Salem, NC MSA 2.03
1.20
1.20
North Carolina 0% 49180
Akron, OH MSA 1.85
1.08
1.08
Ohio 14% 10420
Canton–Massillon, OH MSA 1.20
0.73
0.89
Ohio 100% 15940
Cleveland–Elyria, OH MSA 2.83
1.02
1.02
Ohio 71% 17460
Columbus, OH MSA 2.68
0.95
0.95
Ohio 100% 18140
Dayton, OH MSA 2.07
0.99
0.99
Ohio 100% 19380
Marion, OH μSA 3.45
0.82
0.90
Ohio 100% 32020
New Philadelphia–Dover, OH μSA 1.67
0.82
0.82
Ohio 100% 35420
Salem, OH μSA 1.47
1.31
1.47
Ohio 0% 41400
Toledo, OH MSA 1.99
0.95
0.95
Ohio 100% 45780
Wheeling, WV–OH MSA 1.25
0.93
0.93
Ohio,West Virginia 100% 48540
Youngstown–Warren–Boardman, OH–PA MSA 1.80
0.95
0.95
Ohio,Pennsylvania 100% 49660
Guymon, OK μSA 1.95
0.57
0.82
Oklahoma 100% 25100
Oklahoma City, OK MSA 2.24
0.88
0.89
Oklahoma 100% 36420
Tulsa, OK MSA 2.49
1.02
1.02
Oklahoma 43% 46140
Portland–Vancouver–Hillsboro, OR–WA MSA 1.79
1.28
1.28
Oregon,Washington 29% 38900
Salem, OR MSA 2.02
0.94
0.94
Oregon 100% 41420
Bloomsburg–Berwick, PA MSA 1.77
1.15
1.15
Pennsylvania 71% 14100
Chambersburg–Waynesboro, PA MSA 1.55
0.61
0.82
Pennsylvania 100% 16540
East Stroudsburg, PA MSA 1.75
0.88
0.88
Pennsylvania 100% 20700
Harrisburg–Carlisle, PA MSA 1.49
1.02
1.02
Pennsylvania 79% 25420
Huntingdon, PA μSA 1.90
0.65
0.65
Pennsylvania 100% 26500
Lancaster, PA MSA 2.45
0.93
0.97
Pennsylvania 100% 29540
Lebanon, PA MSA 2.22
0.97
1.02
Pennsylvania 57% 30140
Pittsburgh, PA MSA 2.70
0.96
0.96
Pennsylvania 100% 38300
Pottsville, PA μSA 1.92
0.90
0.91
Pennsylvania 100% 39060
Reading, PA MSA 2.20
0.87
0.87
Pennsylvania 100% 39740
Scranton—Wilkes–Barre—Hazleton, PA MSA 2.75
0.76
0.81
Pennsylvania 100% 42540
York–Hanover, PA MSA 2.09
1.03
1.03
Pennsylvania 57% 49620
New Shoreham Town, RI 1.80
0.79
0.94
Rhode Island 100% 50500
Charleston–North Charleston, SC MSA 2.45
1.15
1.16
South Carolina 0% 16700
Columbia, SC MSA 1.57
1.18
1.18
South Carolina 64% 17900
Florence, SC MSA 1.49
1.11
1.11
South Carolina 71% 22500
Greenville–Anderson–Mauldin, SC MSA 2.99
1.27
1.27
South Carolina 36% 24860
Hilton Head Island–Bluffton–Beaufort, SC MSA 1.89
1.19
1.34
South Carolina 0% 25940
Spartanburg, SC MSA 1.73
1.36
1.36
South Carolina 0% 43900
Huron, SD μSA 2.19
1.43
2.19
South Dakota 0% 26700
Rapid City, SD MSA 1.77
1.12
1.77
South Dakota 0% 39660
Sioux Falls, SD MSA 2.38
0.62
0.62
South Dakota 100% 43620
Bledsoe County, TN 3.34
0.99
0.99
Tennessee 100% CN4700700000000
Cookeville, TN μSA 1.51
1.12
1.48
Tennessee 0% 18260
Dayton, TN μSA 2.69
0.54
2.51
Tennessee 50% 19420
Hardeman County, TN 2.30
0.89
0.89
Tennessee 100% CN4706900000000
Knoxville, TN MSA 2.05
1.02
1.19
Tennessee 0% 28940
Lake County, TN 2.44
0.51
0.54
Tennessee 100% CN4709500000000
Nashville–Davidson—Murfreesboro—Franklin, TN MSA 2.13
1.08
1.08
Tennessee 14% 34980
Abilene, TX MSA 2.05
1.87
2.05
Texas 0% 10180
Amarillo, TX MSA 1.61
0.52
0.71
Texas 100% 11100
Austin–Round Rock, TX MSA 2.71
1.02
1.06
Texas 14% 12420
Beaumont–Port Arthur, TX MSA 1.87
0.96
1.00
Texas 93% 13140
Brownsville–Harlingen, TX MSA 1.85
1.01
1.01
Texas 86% 15180
College Station–Bryan, TX MSA 1.21
0.84
1.21
Texas 43% 17780
Dallas–Fort Worth–Arlington, TX MSA 3.01
1.05
1.05
Texas 71% 19100
Dumas, TX μSA 2.03
1.66
1.66
Texas 50% 20300
El Paso, TX MSA 1.75
0.94
1.13
Texas 36% 21340
Houston–The Woodlands–Sugar Land, TX MSA 2.18
1.03
1.04
Texas 50% 26420
Huntsville, TX μSA 1.99
1.91
1.99
Texas 0% 26660
Killeen–Temple, TX MSA 1.49
1.49
1.49
Texas 21% 28660
Laredo, TX MSA 1.69
1.04
1.04
Texas 86% 29700
Lubbock, TX MSA 2.22
1.10
1.15
Texas 36% 31180
McAllen–Edinburg–Mission, TX MSA 1.74
1.21
1.23
Texas 0% 32580
Mount Pleasant, TX μSA 2.08
1.24
1.92
Texas 0% 34420
San Antonio–New Braunfels, TX MSA 3.12
1.12
1.13
Texas 50% 41700
Sherman–Denison, TX MSA 1.52
0.56
1.44
Texas 64% 43300
Provo–Orem, UT MSA 2.42
1.19
1.19
Utah 14% 39340
Salt Lake City, UT MSA 2.03
1.24
1.24
Utah 0% 41620
Summit Park, UT μSA 2.01
0.99
1.13
Utah 57% 44920
Burlington–South Burlington, VT MNECTA 2.55
0.86
0.95
Vermont 100% 72400
Accomack County, VA 2.27
1.22
1.22
Virginia 57% CN5100100000000
Charlottesville, VA MSA 1.33
1.05
1.05
Virginia 79% 16820
Harrisonburg, VA MSA 1.94
0.81
0.90
Virginia 100% 25500
Richmond, VA MSA 2.21
1.12
1.12
Virginia 0% 40060
Shenandoah County, VA 1.50
1.08
1.08
Virginia 50% CN5117100000000
Winchester, VA–WV MSA 1.33
1.31
1.33
Virginia,West Virginia 0% 49020
Bellingham, WA MSA 2.01
1.03
1.17
Washington 0% 13380
Kennewick–Richland, WA MSA 3.11
1.18
1.24
Washington 0% 28420
Seattle–Tacoma–Bellevue, WA MSA 2.07
0.96
1.01
Washington 71% 42660
Spokane–Spokane Valley, WA MSA 2.45
1.38
1.89
Washington 0% 44060
Yakima, WA MSA 2.40
1.12
1.29
Washington 0% 49420
Elkins, WV μSA 2.38
1.81
2.38
West Virginia 0% 21180
Green Bay, WI MSA 2.12
0.73
0.73
Wisconsin 100% 24580
Janesville–Beloit, WI MSA 1.65
0.82
1.05
Wisconsin 71% 27500
Madison, WI MSA 2.34
1.27
1.27
Wisconsin 0% 31540
Milwaukee–Waukesha–West Allis, WI MSA 4.42
1.12
1.12
Wisconsin 0% 33340
Racine, WI MSA 1.39
1.03
1.03
Wisconsin 57% 39540

Introduction

To keep the number of COVID-19 cases manageable, Re should be kept below 1. This model aims to provide retrospective estimates of Re, as well as forecast different scenarios in which social distancing is reduced by various percentages. For the purposes of these scenarios, the reopening date is currently set to planned reopening dates at the State level if known. Otherwise, a date 1 week after the run date of the model is used.

In the U.S., based on recommendations from the White House, beginning a partial reopening should only be undertaken when the number of documented cases has a downward trajectory over a 14-day period. This is an equivalent to Re being under 1 for the same period.

An export of Re data over time can be found in the footer.

What are R0 and Re?

An R0 or Re is defined as the average number people infected by each patient who contracts COVID-19. For example, a value of 1 means on average 1 person is infected by each person who contracts COVID-19.

R0 is the basic reproduction number at the beginning of the epidemic and prior to social distancing. Re is the effective reproduction number that takes into account social distancing measures and partial herd immunity from antibody immunity and is the best measure of the current rate of spread of the epidemic.

How is the Cerner Reopening Risk Index (CRRI) defined?

The CRRI is defined as

  • 1 (low risk) when Re is less than 0.9

  • 2 (medium risk) when Re is between 0.9 and 1.1

  • 3 (high risk) when Re is between 1.1 and 1.3

  • 4 (very high risk) when Re is greater than 1.3

Geographical areas

A core based statistical area (CBSA) is a U.S. geographic area defined by the Office of Management and Budget (OMB) that consists of one or more counties (or equivalents) anchored by an urban center of at least 10,000 people plus adjacent counties that are socioeconomically tied to the urban center by commuting. A CBSA is either a metropolitan statistical area (MSA) or a micropolitan statistical area (μSA), determined by whether the area has a population of greater than or less than 50,000, respectively. Core-based statistical areas wree introduced in 2003 to replace the previous concept of "metropolitan areas".

A combined statistical area (CSA) is a United States Office of Management and Budget (OMB) term for a combination of adjacent CBSAs in the United States and Puerto Rico that can demonstrate economic or social linkage.

A labor market area is an economically integrated region in which residents can find jobs within a reasonable commuting distance or can change their employment without changing their place of residence.

A New England city and town area (NECTA) is a geographic and statistical entity defined by the U.S. federal government for use in the six-state New England region. We use MNECTA to refer to a metropolitan NECTA and μNECTA to refer to a micropolitan NECTA.

Data sources

Only publicly available data sources were used to build the Cerner model. The public data sources include:

Methodology

We first use local regression on the cumulative case counts to derive a smoothed version of the daily case counts. Then, we apply a local exponential regression on these smoothed daily case counts in order to infer the logarithmic growth rate. The logarithmic growth rate \(k(t)\) can be thought of as the approximate day-to-day percentage increase in daily case count.

In order to account for increases in testing , we also compute the logarithmic growth rate of testing \(k_{\mathrm{test}}\), and model the true case growth rate as \[ k_{\mathrm{true}}(t) = k(t) - \phi \min(k_{\mathrm{test}}(t), \,0.1) \] where \(\phi\) is a parameter currently set to 0.8. Note that because the median time from first symptoms to death is 14 days, we shift the deaths growth rate backwards by 14 days.

Note that because testing data is not available for every country, we do not do a testing adjustment if the data is not available, and for CSA and CBSA, we assume that the state trends in testing apply.

We then use the SEIR model to calculate an estimate for Re over time and apply a moving average to smooth out short time-scale variations. To parametrize this SEIR model, based on the literature, we assume that the latent period is 3 days, and the serial interval is 3.96 days.

We compute an Re value from the mean of the (testing-adjusted) case growth rate and the (time-shifted) deaths growth rate. Because of the time shifting, we do not use the deaths growth rate for the last 14 days before the model is run, and we linearly transition from using the mixture of cases and death growth rates to only deaths growth rate for the period between 14 and 21 days before hte model is run.

We assume that Re remains what it is today until social distancing is relaxed. The planned date of any partial reopening is used, if known, and is otherwise assumed to be May 15. Currently we have collated this data for U.S. states but not for other geographical groupings. After that date, we assume that Re will eventually increase by various percentages toward the initial R0. The model assumes that Re will reach its eventual value within 10 days of the reopening date.

The SEIR model is used to predict the case counts. We estimate the actual case counts as \(7(\mathrm{positivity} / 0.12)^{0.5} (\textrm{positive cases}) \). The number 0.12 is the current average test positivity rate across all US states. An exponent of 0 would assume that the undercount ratio is constant, while an exponent of 1 would assume that testing is done via a true random sample. We therefore choose an exponent of 0.5 to represent an intermediate strategy. There is evidence supporting a nonlinear relationship between the test positivity rate and the number of cases.

Exact estimates differ.

This analysis is built for each CSA or CBSA with at least 100 cumulative cases and a daily case count of 10 for at least three days.

Discussion

All interventions, including but not limited to social distancing policies, wearing masks in public, contact tracing, and expanded testing can reduce Re and any plan for reopening should make use of all available strategies. Monitoring the state of Re is of paramount importance in making continuing decisions about social distancing policies and allocation of resources.

Finally, it's worth mentioning effective mask-wearing, contact tracing, and mass testing are some of the most effective ways to deal with the COVID-19 epidemic. A good example is South Korea's success in reducing Re with such policies. Masks can reduce transmission by over 75%.

Caveats

  • Estimations of Re can have high variance for regions with few total cases documented, or a relatively short history of having 10 or more cases.

  • Case counts can be unreliable, and the amount of unreliability may vary by jurisdiction. Many cases of COVID-19 go undetected because the patient is either asymptomatic or mildly symptomatic and doesn't undergo testing. We chose to primarily use case counts instead of deaths because of the inherent time lag of deaths data, which can be a hindrance for public health planning and for hospital surge planning.

  • Data may be incomplete for the most recent 3 days or so included in the model due to reporting delays.

  • Forecasted future daily case counts are based on current reporting practices, and estimate the number of cases that will be reported, not the actual total.

  • Because many of the factors going into Re are inherently behavioral and policy-based rather than epidemiological, we assume a constant Re between the time the model is run and the assumed relaxation date.

  • Some states include antibody tests in their testing counts, which can give an inaccurate picture of the level of testing. There are also various reports of numbers being reported inaccurately by some states, including reporting COVID-19 deaths as pneumonia deaths.

  • Results are not guaranteed.

Changelog

  • 5/29/2020

    • Adjusted summary plot labels for clarity
    • Added reopening dates to historical state plot
  • 5/28/2020

    • Updated UI and adjusted scenarios
  • 5/26/2020

    • Error modeling improved
  • 5/17/2020

    • \(\phi\) changed to 0.8
    • Minimum test growth changed to -0.05
  • 5/16/2020

    • Texas test count may be inflated due to inclusion of antibody tests in reporting
  • 5/14/2020

    • Scenarios for 75% and 100% now dotted (less plausible scenarios)
  • 5/13/2020

    • Testing data for countries is added, graph displays improved for clarity
  • 5/12/2020

    • Growth rates now account for increases in testing, so Re estimates and CRRIs will on average be lower
  • 5/10/2020

    • Changed methodology for parametrizing epidemiological forecasts
    • Make charts open summary plot on click
  • 5/9/2020

    • Added per capita case counts
    • Bugfixes
    • Removal of some mass reporting of prison cases to avoid skewing data away from community spread