niman

https://www.health.nd.gov/diseases-conditions/coronavirus/north-dakota-coronavirus-cases

Total Cases 1,208 6 New Currently Hospitalized 2 Hospitalized Under Investigation 11 Total People Recovered 949 Deaths 56 People Tested 64,993 https://www.healthvermont.gov/response/coronavirus-covid-19/current-activity-vermont

COVID-19 Case Trends Maine COVID-19 Reopening Gating Metrics Cumulative COVID-19 Cases by ZIP Code Tables of COVID-19 Testing Data, Hospital Use, and Case Demographics View a Table of All Reported COVID-19 Tests in Maine

https://dhhr.wv.gov/COVID-19/Pages/default.aspx

New Hampshire 2019 Novel Coronavirus (COVID-19) Summary Report (data updated as of June 29, 2020, 9:00 AM) Number of Persons with COVID-19 1 5,760 Recovered 4,435 (77%) Deaths Attributed to COVID-19 367 (6%) Total Current COVID-19 Cases 958 Persons Who Have Been Hospitalized for COVID-19 565 (10%) Current Hospitalizations 34 Total Persons Tested at Selected Laboratories, Polymerase Chain Reaction (PCR)2 118,298 Total Persons Tested at Selected Laboratories, Antibody Laboratory Tests2 19,051 Persons with Specimens Submitted to NH PHL 31,868 Persons with Test Pending at NH PHL3 58 Persons Being Monitored in NH (approximate point in time) 3,550 1 Includes specimens positive at any laboratory and those confirmed by CDC confirmatory testing.2 Includes specimens tested at the NH Public Health Laboratories (PHL), LabCorp, Quest, Dartmouth-Hitchcock Medical Center, and those sent to CDC prior to NH PHL testing capacity.3 Includes specimens received and awaiting testing at NH PHL. Does not include tests pending at commercial laboratories. Active Cases Dashboard | Active Cases Map Cumulative Cases Dashboard | Cumulative Cases Map Governor's COVID-19 Equity Response Team https://www.nh.gov/covid19/

1,227 Douglas 231 Sarpy 228 Lancaster 45 Dodge 40 Dakota 35 Scotts Bluff 27 Madison 26 Hall 19 Morrill 18 Saunders 15 Platte 14 Adams 14 Colfax 13 Saline 13 Dawson 12 Thurston 9 Cedar 8 Knox 8 Custer 8 Otoe 8 Dixon 8 Buffalo 8 Cheyenne 7 Washington 6 Cass 5 Phelps 5 Sioux 4 Lincoln 4 Sheridan 4 Gage 4 York 4 Hamilton 3 Fillmore 3 Clay 2 Burt 2 Merrick 2 Cuming 2 Keith 2 Holt 2 Pierce 2 Valley 1 Harlan 1 Furnas 1 Howard 1 Dawes 1 Wayne 1 Kimball 1 Kearney 1 Rock 1 Seward https://nebraska.maps.arcgis.com/apps/opsdashboard/index.html#/4213f719a45647bc873ffb58783ffef3

Science This coronavirus mutation has taken over the world. Scientists are trying to understand why. Health-care workers from University of South Florida Health administer coronavirus testing June 25 at a community center in Tampa. (Octavio Jones/Getty Images) By Sarah Kaplan and Joel Achenbach June 29 at 9:00 AM When the first coronavirus cases in Chicago appeared in January, they bore the same genetic signatures as a germ that emerged in China weeks before. But as Egon Ozer, an infectious-disease specialist at the Northwestern University Feinberg School of Medicine, examined the genetic structure of virus samples from local patients, he noticed something different. A change in the virus was appearing again and again. This mutation, associated with outbreaks in Europe and New York, eventually took over the city. By May, it was found in 95 percent of all the genomes Ozer sequenced. At a glance, the mutation seemed trivial. About 1,300 amino acids serve as building blocks for a protein on the surface of the virus. In the mutant virus, the genetic instructions for just one of those amino acids — number 614 — switched in the new variant from a “D” (shorthand for aspartic acid) to a “G” (short for glycine). But the location was significant, because the switch occurred in the part of the genome that codes for the all-important “spike protein” — the protruding structure that gives the coronavirus its crownlike profile and allows it to enter human cells the way a burglar picks a lock. [Stay safe and informed with our free Coronavirus Updates newsletter] And its ubiquity is undeniable. Of the approximately 50,000 genomes of the new virus that researchers worldwide have uploaded to a shared database, about 70 percent carry the mutation, officially designated D614G but known more familiarly to scientists as “G.” The tiny mutation found in the dominant coronavirus variant Like all coronaviruses, SARS-CoV-2 has a series of characteristic spikes surrounding its core. These spikes are what allow the virus to attach to human cells. SPIKE Spike SARS-CoV-2 Amino acid 614 A mutation affecting the virus's spike protein changed amino acid 614 from “D” (aspartic acid) to “G” (glycine). Research suggests that this small change — which affects three identical amino acid chains — might make the spike protein more effective, enhancing the virus’s infectiousness. Source: GISAID, Post reporting AARON STECKELBERG/THE WASHINGTON POST “G” hasn’t just dominated the outbreak in Chicago — it has taken over the world. Now scientists are racing to figure out what it means. At least four laboratory experiments suggest that the mutation makes the virus more infectious, although none of that work has been peer-reviewed. Another unpublished study led by scientists at Los Alamos National Laboratory asserts that patients with the G variant actually have more virus in their bodies, making them more likely to spread it to others. The mutation doesn’t appear to make people sicker, but a growing number of scientists worry that it has made the virus more contagious. “The epidemiological study and our data together really explain why the [G variant’s] spread in Europe and the U.S. was really fast,” said Hyeryun Choe, a virologist at Scripps Research and a lead author of an unpublished study on the G variant’s enhanced infectiousness in laboratory cell cultures. “This is not just accidental.” But there may be other explanations for the G variant’s dominance: biases in where genetic data are being collected, quirks of timing that gave the mutated virus an early foothold in susceptible populations. “The bottom line is, we haven’t seen anything definitive yet,” said Jeremy Luban, a virologist at the University of Massachusetts Medical School. The scramble to unravel this mutation mystery embodies the challenges of science during the coronavirus pandemic. With millions of people infected and thousands dying every day around the world, researchers must strike a high-stakes balance between getting information out quickly and making sure that it’s right. Spike protein mutation takes over A mutation in the spike protein of the SARS-CoV-2 virus changes just one amino acid in a chain of about 1,300, but it might make a difference in how the virus attacks human cells. The mutation (called D614G), which first appeared in January, is found in what has become the dominant variant of the coronavirus. New weekly samples in Nextrain’s global subsample 100% Proportion of samples with the D614G mutation 50% Proportion of samples without the D614G mutation 0% January 2020 March May June Data includes 3,006 samples acquired June 24. JOE FOX/THE WASHINGTON POST Source: Nextstrain, GISAID A better lock pick SARS-CoV-2, the novel coronavirus that causes the disease covid-19, can be thought of as an extremely destructive burglar. Unable to live or reproduce on its own, it breaks into human cells and co-opts their biological machinery to make thousands of copies of itself. That leaves a trail of damaged tissue and triggers an immune system response that for some people can be disastrous. [How the virus kills] This replication process is messy. Even though it has a “proofreading” mechanism for copying its genome, the coronavirus frequently makes mistakes, or mutations. The vast majority of mutations have no effect on the behavior of the virus. But since the virus’s genome was first sequenced in January, scientists have been on the lookout for changes that are meaningful. And few genetic mutations could be more significant than ones that affect the spike protein — the virus’s most powerful tool. This protein attaches to a receptor on respiratory cells called ACE2, which opens the cell and lets the virus slip inside. The more effective the spike protein, the more easily the virus can break into the bodies of its hosts. Even when the original variant of the virus emerged in Wuhan, China, it was obvious that the spike protein on SARS-CoV-2 was already quite effective. SARS-CoV-2 copies SARS-CoV-2 ACE2 RNA SARS-CoV-2 uses its spike to bind to the ACE2 receptor, allowing access into the cell. The virus’s RNA is released into the cell. The cell reads the RNA and makes proteins. The proteins are assembled into new copies of the virus, which then go on to infect more cells. AARON STECKELBERG/THE WASHINGTON POST But it could have been even better, said Choe, who has studied spike proteins and the way they bind to the ACE2 receptor since the severe acute respiratory syndrome outbreak in 2003. The spike protein for SARS-CoV-2 has two parts that don’t always hold together well. In the version of the virus that arose in China, Choe said, the outer part — which the virus needs to attach to a human receptor — frequently broke off. Equipped with this faulty lock pick, the virus had a harder time invading host cells. “I think this mutation happened to compensate,” Choe said. Studying both versions of the gene using a proxy virus in a petri dish of human cells, Choe and her colleagues found that viruses with the G variant had more spike proteins, and the outer parts of those proteins were less likely to break off. This made the virus approximately 10 times more infectious in the lab experiment. The mutation does not seem to lead to worse outcomes in patients. Nor did it alter the virus’s response to antibodies from patients who had the D variant, Choe said, suggesting that vaccines being developed based on the original version of the virus will be effective against the new strain. Choe has uploaded a manuscript describing this study to the website BioRxiv, where scientists can post “preprint” research that has not yet been peer reviewed. She has also submitted the paper to an academic journal, which has not yet published it. The distinctive infectiousness of the G strain is so strong that scientists have been drawn to the mutation even when they weren’t looking for it. Neville Sanjana, a geneticist at the New York Genome Center and New York University, was trying to figure out which genes enable SARS-CoV-2 to infiltrate human cells. But in experiments based on a gene sequence taken from an early case of the virus in Wuhan, he struggled to get that form of the virus to infect cells. Then the team switched to a model virus based on the G variant. “We were shocked,” Sanjana said. “Voilà! It was just this huge increase in viral transduction.” They repeated the experiment in many types of cells, and every time the variant was many times more infectious. Their findings, published as a preprint on BioRxiv, generally matched what Choe and other laboratory scientists were seeing. But the New York team offers a different explanation as to why the variant is so infectious. Whereas Choe’s study proposes that the mutation made the spike protein more stable, Sanjana said experiments in the past two weeks, not yet made public, suggest that the improvement is actually in the infection process. He hypothesized that the G variant is more efficient at beginning the process of invading the human cell and taking over its reproductive machinery. Luban, who has also been experimenting with the D614G mutation, has been drawn to a third possibility: His experiments suggest that the mutation allows the spike protein to change shape as it attaches to the ACE2 receptor, improving its ability to fuse to the host cell. Different approaches to making their model virus might explain these discrepancies, Luban said. “But it’s quite clear that something is going on.” Unanswered questions Although these experiments are compelling, they’re not conclusive, said Kristian Andersen, a Scripps virologist not involved in any of the studies. The scientists need to figure out why they’ve identified different mechanisms for the same effect. All the studies still have to pass peer review, and they have to be reproduced using the real version of the virus. Even then, Andersen said, it will be too soon to say that the G variant transmits faster among people. Cell culture experiments have been wrong before, noted Anderson Brito, a computational biologist at Yale University. Early experiments with hydroxychloroquine, a malaria drug, hinted that it was effective at fighting the coronavirus in a petri dish. The drug was touted by President Trump, and the Food and Drug Administration authorized it for emergency use in hospitalized covid-19 patients. But that authorization was withdrawn this month after evidence showed that the drug was “unlikely to be effective” against the virus and posed potential safety risks. So far, the biggest study of transmission has come from Bette Korber, a computational biologist at Los Alamos National Laboratory who helped build one of the world’s biggest viral genome databases for tracking HIV. In late April, she and colleagues at Duke University and the University of Sheffield in Britain released a draft of their work arguing that the mutation boosts transmission of the virus. Analyzing sequences from more than two dozen regions across the world, they found that most places where the original virus was dominant before March were eventually taken over by the mutated version. This switch was especially apparent in the United States: Ninety-six percent of early sequences here belonged to the D variant, but by the end of March, almost 70 percent of sequences carried the G amino acid instead. The British researchers also found evidence that people with the G variant had more viral particles in their bodies. Although this higher viral load didn’t seem to make people sicker, it might explain the G variant’s rapid spread, the scientists wrote. People with more virus to shed are more likely to infect others. The Los Alamos draft drew intense scrutiny when it was released in the spring, and many researchers remain skeptical of its conclusions. “There are so many biases in the data set here that you can’t control for and you might not know exist,” Andersen said. In a time when as many as 90 percent of U.S. infections are still undetected and countries with limited public health infrastructure are struggling to keep up with surging cases, a shortage of data means “we can’t answer all the questions we want to answer.” Pardis Sabeti, a computational biologist at Harvard University and the Broad Institute, noted that the vast majority of sequenced genomes come from Europe, where the G variant first emerged, and the United States, where infections thought to have been introduced by travelers from Europe spread undetected for weeks before the country shut down. This could at least partly explain why it appears so dominant. The mutation’s success might also be a “founder effect,” she said. Arriving in a place like Northern Italy — where the vast majority of sequenced infections are caused by the G variant — it found easy purchase in an older and largely unprepared population, which then unwittingly spread it far and wide. Scientists may be able to rule out these alternative explanations with more rigorous statistical analyses or a controlled experiment in an animal population. And as studies on the D614G mutation accumulate, researchers are starting to be convinced of its significance. “I think that slowly we’re beginning to come to a consensus,” said Judd Hultquist, a virologist at Northwestern University. Solving the mystery of the D614G mutation won’t make much of a difference in the short term, Andersen said. “We were unable to deal with D,” he said. “If G transmits even better, we’re going to be unable to deal with that one.” But it’s still essential to understand how the genome influences the behavior of the virus, scientists say. Identifying emerging mutations allows researchers to track their spread. Knowing what genes affect how the virus transmits enables public health officials to tailor their efforts to contain it. Once therapeutics and vaccines are distributed on a large scale, having a baseline understanding of the genome will help pinpoint when drug resistance starts to evolve. “Understanding how transmissions are happening won’t be a magic bullet, but it will help us respond better,” Sabeti said. “This is a race against time.” Correction: An initial version of this article gave an incorrect affiliation for Jeremy Luban. He is a virologist at the University of Massachusetts Medical School.

A change in the virus was appearing again and again. This mutation, associated with outbreaks in Europe and New York, eventually took over the city. By May, it was found in 95 percent of all the genomes Ozer sequenced. https://www.washingtonpost.com/science/2020/06/29/coronavirus-mutation-science/?arc404=true&pwapi_token=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJjb29raWVuYW1lIjoid3BfY3J0aWQiLCJpc3MiOiJDYXJ0YSIsImNvb2tpZXZhbHVlIjoiNWU2MWFkODc5YmJjMGYyMTk0YTlkOGUxIiwidGFnIjoid3BfdG9feW91cl9oZWFsdGgiLCJ1cmwiOiJodHRwczovL3d3dy53YXNoaW5ndG9ucG9zdC5jb20vc2NpZW5jZS8yMDIwLzA2LzI5L2Nvcm9uYXZpcnVzLW11dGF0aW9uLXNjaWVuY2UvP2FyYzQwND10cnVlJnV0bV9jYW1wYWlnbj13cF90b195b3VyX2hlYWx0aCZ1dG1fbWVkaXVtPWVtYWlsJnV0bV9zb3VyY2U9bmV3c2xldHRlciZ3cGlzcmM9bmxfdHloJndwbWs9MSJ9.LxWSH3LSuSDsGlY4uJ0RwFdcQvolKI9maSPsU_Hryek&utm_campaign=wp_to_your_health&utm_medium=email&utm_source=newsletter&wpisrc=nl_tyh&wpmk=1

https://doh.sd.gov/news/Coronavirus.aspx

https://coronavirus.idaho.gov/

Arkansas (28 Cases) Ashley (70 Cases) Baxter (20 Cases) Benton (2,679 Cases) Boone (22 Cases) Bradley (51 Cases) Calhoun (0 Cases) Carroll (169 Cases) Chicot (129 Cases) Clark (56 Cases) Clay (54 Cases) Cleburne (85 Cases) Cleveland (25 Cases) Columbia (84 Cases) Conway (48 Cases) Craighead (447 Cases) Crawford (155 Cases) Crittenden (737 Cases) Cross (85 Cases) Dallas (10 Cases) Desha (45 Cases) Drew (64 Cases) Faulkner (453 Cases) Franklin (18 Cases) Fulton (12 Cases) Garland (240 Cases) Grant (40 Cases) Greene (114 Cases) Hempstead (50 Cases) Hot Spring (428 Cases) Howard (86 Cases) Independence (55 Cases) Izard (20 Cases) Jackson (27 Cases) Jefferson (693 Cases) Johnson (211 Cases) Lafayette (16 Cases) Lawrence (99 Cases) Lee (641 Cases) Lincoln (1,038 Cases) Little River (38 Cases) Logan (45 Cases) Lonoke (134 Cases) Madison (190 Cases) Marion (4 Cases) Miller (114 Cases) Missing County Info (731 Cases) Mississippi (161 Cases) Monroe (12 Cases) Montgomery (4 Cases) Nevada (101 Cases) Newton (5 Cases) Ouachita (20 Cases) Perry (29 Cases) Phillips (140 Cases) Pike (8 Cases) Poinsett (57 Cases) Polk (74 Cases) Pope (416 Cases) Prairie (20 Cases) Pulaski (1,916 Cases) Randolph (47 Cases) Saline (261 Cases) Scott (11 Cases) Searcy (7 Cases) Sebastian (457 Cases) Sevier (693 Cases) Sharp (53 Cases) St. Francis (811 Cases) Stone (14 Cases) Union (247 Cases) Van Buren (34 Cases) Washington (3,417 Cases) White (85 Cases) Woodruff (6 Cases) Yell (591 Cases) https://experience.arcgis.com/experience/c2ef4a4fcbe5458fbf2e48a21e4fece9

Data current as of 6/29/2020, 12:01 a.m. Updated Monday - Friday.* Total cases 8,4851 Total deaths 204 Positive tests 8,121 Negative tests 226,648 Total tested 234,769 * For daily counts of cases, deaths and negative tests on weekends, please see our weekend press releases available here. 1Includes cases confirmed by diagnostic testing and presumptive cases. Presumptive cases are those without a positive diagnostic test who present COVID-19-like symptoms and had close contact with a confirmed case. https://govstatus.egov.com/OR-OHA-COVID-19

https://coronavirus.health.ok.gov/

Positive 11,982 Total Tests * 337,720 Click Here to View Positive Cases By County COVID-Related Deaths in NM 493 *Numbers are cumulative persons tested through 6/29/2020, 5:30:26 PM. Test results are from the state Scientific Laboratory Division of the New Mexico Department of Health, TriCore Reference Laboratories, LabCorp, Mayo Clinic Laboratories, Quest Diagnostics, and BioReference Laboratories. https://cv.nmhealth.org/

https://www.coronavirus.kdheks.gov/160/COVID-19-in-Kansas

Aitkin County: 13 Anoka County: 2,076 Becker County: 54 Beltrami County: 27 Benton County: 208 Big Stone County: 14 Blue Earth County: 377 Brown County: 27 Carlton County: 81 Carver County: 338 Cass County: 12 Chippewa County: 74 Chisago County: 93 Clay County: 560 Clearwater County: 7 Cook County: 1 Cottonwood County: 132 Crow Wing County: 102 Dakota County: 2,157 Dodge County: 79 Douglas County: 58 Faribault County: 53 Fillmore County: 25 Freeborn County: 282 Goodhue County: 117 Grant County: 6 Hennepin County: 11,656 Houston County: 23 Hubbard County: 5 Isanti County: 61 Itasca County: 64 Jackson County: 54 Kanabec County: 14 Kandiyohi County: 565 Kittson County: 2 Koochiching County: 12 Lac Qui Parle County: 4 Lake County: 6 Le Sueur County: 79 Lincoln County: 9 Lyon County: 295 Mahnomen County: 7 Marshall County: 12 Martin County: 155 McLeod County: 81 Meeker County: 57 Mille Lacs County: 32 Morrison County: 58 Mower County: 911 Murray County: 50 Nicollet County: 136 Nobles County: 1,651 Norman County: 19 Olmsted County: 1,027 Otter Tail County: 91 Pennington County: 50 Pine County: 100 Pipestone County: 20 Polk County: 79 Pope County: 10 Ramsey County: 4,629 Red Lake County: 3 Redwood County: 16 Renville County: 24 Rice County: 804 Rock County: 30 Roseau County: 6 Scott County: 641 Sherburne County: 291 Sibley County: 46 St. Louis County: 158 Stearns County: 2,228 Steele County: 215 Stevens County: 3 Swift County: 20 Todd County: 397 Traverse County: 5 Wabasha County: 30 Wadena County: 15 Waseca County: 57 Washington County: 995 Watonwan County: 200 Wilkin County: 20 Winona County: 113 Wright County: 438 Yellow Medicine County: 27 https://mndps.maps.arcgis.com/apps/opsdashboard/index.html#/f28f84968c1148129932c3bebb1d3a1a

Total Cases 11,376119.0 per 10,000 people Confirmed Cases 10,306 Probable Cases 1,070 Positive Cases in Comparable Locations New Castle County 5,03187.0 per 10,000 people Kent County 1,67295.0 per 10,000 people Sussex County 4,637240.0 per 10,000 people Unknown 36 https://coronavirus.delaware.gov/

June 28, 2020 Total Overall Tested: 95,360* Total Number of DC Residents Tested: 74,575* Total Positives: 10,292 Total Lives Lost: 551 Cleared From Isolation: 1,200 *All Data are preliminary and are subject to change based on additional reporting Download copy of DC COVID-19 data Other Data Public Safety Agency Data Human Services Agency Data Hospital Status Data https://coronavirus.dc.gov/page/coronavirus-data

Total Tested394,773PCR: 360,929- Serology: 33,844 Total Positive15,347Probable: 512 - Lab Confirmed: 14,835 Deaths560Probable: 3 - Lab Confirmed: 557 Recovered3,939 https://govstatus.egov.com/kycovid19

https://coronavirus.iowa.gov/pages/case-counts

https://coronavirus.utah.gov/case-counts/

https://app.powerbigov.us/view?r=eyJrIjoiMjA2ZThiOWUtM2FlNS00MGY5LWFmYjUtNmQwNTQ3Nzg5N2I2IiwidCI6ImU0YTM0MGU2LWI4OWUtNGU2OC04ZWFhLTE1NDRkMjcwMzk4MCJ9

Abbeville Rate (per 100k): 432.18 Cases--Confirmed positives: 106, Probable: 0 Deaths--Confirmed: 0, Probable: 0 Aiken Rate (per 100k): 209.51 Cases--Confirmed positives: 358, Probable: 0 Deaths--Confirmed: 9, Probable: 0 Allendale Rate (per 100k): 552.49 Cases--Confirmed positives: 48, Probable: 0 Deaths--Confirmed: 3, Probable: 0 Anderson Rate (per 100k): 285.35 Cases--Confirmed positives: 578, Probable: 0 Deaths--Confirmed: 11, Probable: 0 Bamberg Rate (per 100k): 732.26 Cases--Confirmed positives: 103, Probable: 0 Deaths--Confirmed: 1, Probable: 0 Barnwell Rate (per 100k): 369.02 Cases--Confirmed positives: 77, Probable: 0 Deaths--Confirmed: 1, Probable: 0 Beaufort Rate (per 100k): 597.54 Cases--Confirmed positives: 1,148, Probable: 4 Deaths--Confirmed: 18, Probable: 0 Berkeley Rate (per 100k): 441.85 Cases--Confirmed positives: 1,007, Probable: 0 Deaths--Confirmed: 21, Probable: 0 Calhoun Rate (per 100k): 570.33 Cases--Confirmed positives: 83, Probable: 0 Deaths--Confirmed: 1, Probable: 0 Charleston Rate (per 100k): 887.93 Cases--Confirmed positives: 3,653, Probable: 1 Deaths--Confirmed: 23, Probable: 0 Cherokee Rate (per 100k): 253.05 Cases--Confirmed positives: 145, Probable: 0 Deaths--Confirmed: 7, Probable: 0 Chester Rate (per 100k): 533.43 Cases--Confirmed positives: 172, Probable: 1 Deaths--Confirmed: 1, Probable: 0 Chesterfield Rate (per 100k): 795.18 Cases--Confirmed positives: 363, Probable: 0 Deaths--Confirmed: 14, Probable: 0 Clarendon Rate (per 100k): 1,212.03 Cases--Confirmed positives: 409, Probable: 0 Deaths--Confirmed: 42, Probable: 0 Colleton Rate (per 100k): 857.29 Cases--Confirmed positives: 323, Probable: 1 Deaths--Confirmed: 21, Probable: 0 Darlington Rate (per 100k): 649.97 Cases--Confirmed positives: 433, Probable: 0 Deaths--Confirmed: 14, Probable: 0 Dillon Rate (per 100k): 977.72 Cases--Confirmed positives: 298, Probable: 0 Deaths--Confirmed: 8, Probable: 0 Dorchester Rate (per 100k): 385.11 Cases--Confirmed positives: 627, Probable: 0 Deaths--Confirmed: 3, Probable: 0 Edgefield Rate (per 100k): 278.8 Cases--Confirmed positives: 76, Probable: 0 Deaths--Confirmed: 2, Probable: 0 Fairfield Rate (per 100k): 1,288.76 Cases--Confirmed positives: 288, Probable: 0 Deaths--Confirmed: 21, Probable: 0 Florence Rate (per 100k): 838.08 Cases--Confirmed positives: 1,159, Probable: 0 Deaths--Confirmed: 49, Probable: 0 Georgetown Rate (per 100k): 701.98 Cases--Confirmed positives: 440, Probable: 0 Deaths--Confirmed: 3, Probable: 0 Greenville Rate (per 100k): 933.45 Cases--Confirmed positives: 4,887, Probable: 8 Deaths--Confirmed: 80, Probable: 0 Greenwood Rate (per 100k): 658.09 Cases--Confirmed positives: 466, Probable: 1 Deaths--Confirmed: 6, Probable: 1 Hampton Rate (per 100k): 353.76 Cases--Confirmed positives: 68, Probable: 0 Deaths--Confirmed: 2, Probable: 0 Horry Rate (per 100k): 889.63 Cases--Confirmed positives: 3,150, Probable: 2 Deaths--Confirmed: 44, Probable: 0 Jasper Rate (per 100k): 405.68 Cases--Confirmed positives: 122, Probable: 0 Deaths--Confirmed: 3, Probable: 0 Kershaw Rate (per 100k): 960.17 Cases--Confirmed positives: 639, Probable: 1 Deaths--Confirmed: 15, Probable: 0 Lancaster Rate (per 100k): 445.86 Cases--Confirmed positives: 437, Probable: 10 Deaths--Confirmed: 11, Probable: 1 Laurens Rate (per 100k): 623.77 Cases--Confirmed positives: 421, Probable: 1 Deaths--Confirmed: 5, Probable: 0 Lee Rate (per 100k): 1,693.61 Cases--Confirmed positives: 285, Probable: 0 Deaths--Confirmed: 22, Probable: 0 Lexington Rate (per 100k): 655.73 Cases--Confirmed positives: 1,959, Probable: 12 Deaths--Confirmed: 48, Probable: 0 McCormick Rate (per 100k): 158.51 Cases--Confirmed positives: 15, Probable: 0 Deaths--Confirmed: 1, Probable: 0 Marion Rate (per 100k): 629.55 Cases--Confirmed positives: 193, Probable: 0 Deaths--Confirmed: 7, Probable: 0 Marlboro Rate (per 100k): 1,152.46 Cases--Confirmed positives: 301, Probable: 0 Deaths--Confirmed: 4, Probable: 0 Newberry Rate (per 100k): 639.96 Cases--Confirmed positives: 246, Probable: 0 Deaths--Confirmed: 4, Probable: 0 Oconee Rate (per 100k): 316.8 Cases--Confirmed positives: 252, Probable: 0 Deaths--Confirmed: 1, Probable: 0 Orangeburg Rate (per 100k): 805.34 Cases--Confirmed positives: 694, Probable: 0 Deaths--Confirmed: 10, Probable: 0 Pickens Rate (per 100k): 634.44 Cases--Confirmed positives: 805, Probable: 4 Deaths--Confirmed: 4, Probable: 0 Richland Rate (per 100k): 797.34 Cases--Confirmed positives: 3,315, Probable: 48 Deaths--Confirmed: 82, Probable: 0 Saluda Rate (per 100k): 1,006.2 Cases--Confirmed positives: 206, Probable: 0 Deaths--Confirmed: 1, Probable: 0 Spartanburg Rate (per 100k): 482.51 Cases--Confirmed positives: 1,543, Probable: 1 Deaths--Confirmed: 45, Probable: 0 Sumter Rate (per 100k): 955.76 Cases--Confirmed positives: 1,020, Probable: 2 Deaths--Confirmed: 22, Probable: 1 Union Rate (per 100k): 307.51 Cases--Confirmed positives: 84, Probable: 0 Deaths--Confirmed: 0, Probable: 0 Williamsburg Rate (per 100k): 1,280.95 Cases--Confirmed positives: 389, Probable: 0 Deaths--Confirmed: 15, Probable: 0 York Rate (per 100k): 411.06 Cases--Confirmed positives: 1,155, Probable: 1 Deaths--Confirmed: 12, Probable: 0 https://www.scdhec.gov/infectious-diseases/viruses/coronavirus-disease-2019-covid-19/sc-testing-data-projections-covid-19

Summary of COVID-19 cases by age group Case counts Case percentages Age Group (Years) Cases as of 6/29/2020 Ever hospitalized as of 6/29/2020 Any Intensive Care as of 6/29/2020 Deaths as of 6/29/2020 <10 933 26 2 0 10-19 2,138 51 3 0 20-29 6,254 196 26 8 30-39 4,948 278 37 8 40-49 4,376 413 91 23 50-59 3,962 570 132 59 60-69 2,659 702 206 133 70-79 1,416 600 147 200 80-89 912 416 75 193 90+ 460 155 28 153 Total 28,058 3,407 747 777 Percent of COVID-19 cases by hospitalization status Hospitalization status Number of confirmed cases as of 6/29/2020 Percent of confirmed cases as of 6/29/2020 Ever hospitalized 3,407 12% Never hospitalized 16,331 58% Unknown 8,320 30% Total 28,058 100% Percent of COVID-19 cases living in group housing Long-term care facilities include skilled nursing facilities (nursing homes) and assisted living facilities (community-based residential facilities and residential care apartment complexes). Group housing facilities include correctional facilities, homeless shelters, dormitories, and group homes. The data on group housing is unknown at this time for a portion of cases because these data have only been systematically collected since April 8, 2020. However, any COVID cases who were part of an outbreak investigation in a long-term care or other group housing facility prior to April 8 are classified under the appropriate group setting category (and are not included in the unknown category). Percent of COVID-19 cases by recovery status The number of patients recovered from COVID-19 is defined as the number of confirmed cases who are currently alive based on Wisconsin state vital records system data and had one or more of the following: Documentation of resolved symptoms Documentation of release from public health isolation 30 days since symptom onset or diagnosis* Active cases include COVID-19 cases who were diagnosed in the last 30 days, are not known to have died, and do not yet meet the definition of having recovered. *Our data indicate that the vast majority of reported cases who recovered did so within 30 days. Rarely, more than 30 days were required to recover. As a result, a very small number of cases who are still recovering might be included in the 'Recovered' category. Percent of COVID-19 cases who are health care workers Data on COVID-19 cases who are health care workers represents a broad range of occupations in the health care field, including nurses, physicians, surgeons, physician assistants, health care support staff, emergency medical technicians and paramedics, dentists and other dental health workers, and pharmacists. Last Revised: June 23, 2020 https://www.dhs.wisconsin.gov/covid-19/cases.htm

Cases and Deaths by County https://msdh.ms.gov/msdhsite/_static/14,0,420.html#caseTable Totals of all reported cases since March 11, including those in long-term care (LTC) facilities. County Total Cases Total Deaths Total LTC Facility Cases Total LTC Facility Deaths Adams 253 18 44 10 Alcorn 53 1 1 0 Amite 80 2 12 2 Attala 347 23 89 19 Benton 27 0 1 0 Bolivar 254 13 28 4 Calhoun 114 4 23 4 Carroll 149 11 45 9 Chickasaw 246 18 36 11 Choctaw 71 4 0 0 Claiborne 220 10 43 8 Clarke 196 24 19 9 Clay 237 8 0 0 Coahoma 180 6 0 0 Copiah 558 13 29 3 Covington 269 5 1 0 Desoto 1293 15 18 4 Forrest 786 42 95 29 Franklin 35 2 3 1 George 72 3 1 0 Greene 88 7 34 5 Grenada 355 5 21 2 Hancock 120 13 8 4 Harrison 688 7 21 2 Hinds 2048 38 124 14 Holmes 514 40 98 20 Humphreys 115 9 18 6 Issaquena 6 1 0 0 Itawamba 119 8 34 7 Jackson 493 16 43 5 Jasper 239 6 0 0 Jefferson 83 3 1 0 Jefferson Davis 102 4 3 1 Jones 1033 49 144 32 Kemper 172 13 37 9 Lafayette 330 4 42 1 Lamar 393 7 3 2 Lauderdale 872 78 201 51 Lawrence 149 1 0 0 Leake 533 19 3 0 Lee 460 15 61 9 Leflore 455 48 168 33 Lincoln 430 33 111 25 Lowndes 438 11 19 6 Madison 1181 32 124 16 Marion 233 11 15 2 Marshall 195 3 4 0 Monroe 342 29 94 24 Montgomery 112 2 0 0 Neshoba 938 68 78 25 Newton 329 9 4 0 Noxubee 242 8 15 3 Oktibbeha 477 24 111 18 Panola 246 6 2 1 Pearl River 237 32 47 12 Perry 59 4 0 0 Pike 347 11 24 6 Pontotoc 203 3 3 1 Prentiss 94 3 24 3 Quitman 65 0 0 0 Rankin 798 12 27 0 Scott 733 15 13 2 Sharkey 24 0 0 0 Simpson 209 3 2 0 Smith 203 11 52 8 Stone 50 1 0 0 Sunflower 247 6 0 0 Tallahatchie 93 3 2 1 Tate 242 6 17 4 Tippah 119 11 0 0 Tishomingo 61 1 2 0 Tunica 88 3 12 2 Union 166 9 20 8 Walthall 168 3 0 0 Warren 409 17 39 9 Washington 427 9 8 1 Wayne 509 10 17 1 Webster 122 10 52 9 Wilkinson 89 9 5 2 Winston 230 5 25 2 Yalobusha 151 7 35 7 Yazoo 454 6 19 2 Total 26,567 1,059 2,574 516 The numbers in this table are provisional. County case numbers and deaths may change as investigation finds new or additional information about residence. Case Classifications Mississippi investigates and reports both probable and confirmed cases and deaths according to the CSTE case definition. Confirmed Probable Total Cases 26,400 167 26,567 Deaths 1,042 17 1,059