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July 02, 2020

Are Children Really Recovering 99.9584% of the Time From COVID-19?

By H. Ealy, M. McEvoy, M. Sava, S. Gupta, D. Chong, E. Braham, C. Fieberg, D. White, P. Anderson

Abstract

The purpose of this statistical research paper is to provide the reader with a complete understanding of the issues surrounding the data being made available to all Americans at the US State & Territory Health Department (USSTHD) level. The strengths, the limitations, and the potential for measurement error were examined in the USSTHD data. The authors of this paper believe the data we examined can be utilized to develop targeted strategic approaches to both protect our most vulnerable citizens (50+ years of age with at least 1 major comorbidity) without injuring the emotional, psychological, and social development of our least vulnerable citizens (0 to 19 years of age).

As early as March 9th 2020, and certainly by March 18th, the Centers for Disease Control & Prevention (CDC) were aware that children, teens, and young adults in the 0 to 19 age range would not be at high-risk for significant negative medical outcomes, including fatality, from the SARS-CoV-2 (COVID-19) Coronavirus infection.

This knowledge was based upon data acquired by the World Health Organization (WHO) from the Chinese CDC, the South Korean Ministry of Health & Welfare and the ISS Italian National Health Institute.1,2,3,4

According to a CNBC.com report on March 9th by Kopecki, Higgins-Dunn & Miller, “Of the 70,000 cases WHO scientists looked at, only about 2% were in people younger than 19. The odds of developing COVID-19 increase with age, starting at age 60. It’s especially lethal for people over 80.

Now, some 152 days since the first lab confirmed case in the US on January 21st, the number of cases, hospitalizations, and fatalities are comparable to or lower than what was initially anticipated for Americans age 0 to 19.

We have collected data daily since March 4th (through June 21st for the purposes of this paper) for cases, lab testing, hospitalizations, fatalities, recoveries, & projections. Our data sources are each USSTHDs, CDC, WHO, IHME, and COVID Tracking Project for both nationwide and individual states & territories. We cross reference all data sources for accuracy wherever able to do so. When confronted by a discrepancy in the data we defer to data at the USSTHD level in alignment with guidelines to do so by the CDC.5

Each Sunday we perform a very detailed, point by point visual verification process at each USSTHD to ensure data accuracy as well as update age and comorbidity demographic data where available.

For data collected for age demographics, we can confirm the following independently verifiable trends.

 

Table 1 – Summary Of Low-Risk & High-Risk Demographic Data & % Of Total

 

The data indicates that, while our children are testing positive for COVID-19 greater than initially anticipated, our children are also not requiring hospitalization with any concerning level of frequency. And when we further examine the data for reassurance against our greatest concern, ‘If my child contracts COVID-19, will they die?’ we are indeed reassured that the statistics confirm that a child positive for COVID-19 between the ages 0 to 19 demographic are likely to recover from COVID-19 in the 99.9577 percentile. Probability of Recovery for all age demographics, which will be discussed in more detail later, has been rising each week since we began performing this calculation on April 5th.

Potential Mental & Emotional Impacts

COVID-19 policies have impacted the psychological, emotional and sociological health of children and adolescents around the globe. According to Amy Learmonth, PhD, a developmental psychologist in the U.S.: “Social development has important impacts at all ages, but for the purposes of social distancing, the kids who are likely to suffer the most are in late childhood and adolescence.” 6

A survey conducted by Young Minds UK involving 745 participants found that 67% of parents and caregivers were “concerned about the long-term impact of coronavirus on their child’s mental health”. For children who had received recent mental health support, 77% of parents surveyed were concerned about the impact of their child’s mental health longterm.7

In a survey including over 2,000 participants up to 25 years of age who have a preexisting mental illness, 83% felt that the COVID-19 pandemic had made their condition worse, and 26% stated they are unable to access mental health support.8,9

In light of the existing data available for children and adolescents in the U.S., the authors of this paper endorse a thorough review of public health policies and procedures put into effect as a result of concerns due to COVID-19. We endorse the development of revised policies and procedures that include more balanced guidelines for also protecting the mental, emotional and sociological health of our children.

Case Data

 

Based upon the compiled data we are able to conclude that the Age 0 to 19 demographic makes up 6.4% of total COVID-19 cases, while the Age 20 to 49 demographic makes up 47.8% and the age 50+ demographic makes up 45.8% of total cases. (See Table 2 for a complete look at the Data By State)

While anyone, at any age, can contract the SARS-CoV-2 viral infection, the Age 0 to 19 demographic has been the least impacted to date.

Table 2 – Age Demographic Data For Cases By State

 

Hospitalization Data

 

Based upon the compiled data we are able conclude that the Age 0 to 19 demographic makes up only 1.1% of total COVID-19 hospitalizations, while the Age 20 to 49 demographic makes up 19.1% and the age 50+ demographic makes up 79.8% of total hospitalization. (See Table 3 for a complete look at the Data By State)

While anyone, at any age, can be hospitalized with a COVID-19 infection, the Age 0 to 19 demographic has been the least impacted to date.

Table 3 – Age Demographic Data For Hospitalizations By State

 

Fatality Data

 

Based upon the compiled data we are able to conclude that the Age 0 to 19 demographic makes up only 0.049% of total COVID-19 fatalities, while the Age 20 to 49 demographic makes up 4.3% and the age 50+ demographic makes up 95.6% of total fatalities. (See Table 4 for a complete look at the Data By State)

While anyone, at any age, can pass away due to a COVID-19 infection, the Age 0 to 19 demographic has been the least impacted to date.

Table 4 – Age Demographic Data For Fatalities By State

 

Hospitalization vs Fatality Data

 

Based upon the compiled data we are able to conclude that % Of Hospitalization To Fatality for the Age 0 to 19 demographic is 1.52%, which further defines this age demographic as Low-Risk. The % Hospitalization To Fatality for the Age 20 to 49 demographic increases to 9.60%. However, the % Hospitalization To Fatality for the Age 50+ demographic is 51.84%, which clearly identifies this age demographic, yet again, as High-Risk.

In our professional opinion, this significant disparity in % Of Hospitalization To Fatality from the Low-Risk demographic to the High-Risk demographic does warrant additional concern for the High-Risk demographic and draw into question the lack of the clinical nutrition as a primary intervention in COVID-19 confirmed patients; a topic we will discuss in future peer-reviewed research studies. (See Table 5 for a complete look at how this graph was developed)

Table 5 – Age Demographic Data For Fatalities By State

 

While this is a preliminary, surface-level look at the likelihood of recovery vs fatality for hospitalizations, we do feel this raises 4 very important questions for further investigation: (1) During COVID-19 family members have been unable to be present in the hospital rooms of their hospitalized family member. Does the absence of a family member in these situations have any effect upon mortality?; (2) Are patients with a pre-existing comorbidities more likely to be admitted to a hospital and therefore at greater risk for negative outcomes?; (3) What percentage of the current fatalities are due to medical error as opposed to COVID-related sequalae?; (4) To what extent can optimizing serologic nutritional status as a primary medical intervention, improve patient outcomes across all demographics?

 Probability of Recovery Data Analysis

As of June 21st, if we calculate Probability of Fatality as Total Fatalities divided by Total Cases by Demographic Age Range we come to the following results.

Age 0 to 19 Demographic

Total Fatalities – 55

Total Cases – 132,096

Probability of Fatality = 0.0416%

From this we can calculate the Probability of Recovery as 100% minus the Probability of Fatality.

Probability of Recovery = 99.9584%

Understanding the data limitations that 51 US State Health Departments are reporting case demographics while only 45 US State Health Departments are reporting fatality demographics, we can remove the total cases where we do not have corresponding fatality demographics for improved accuracy.

Removing Total Cases for the Age 0 to 19 Demographic for Alaska (79), Hawai’i (52), Maine (203), Montana (76), New Mexico (1,366), & West Virginia (232) corrects the Total Cases by 2,008 cases.

Adjusted Total Cases = 132,096 – 2008 = 130,088

Adjusted Probability of Fatality = 0.0423%

Thus, the Adjusted Probability of Recovery is 99.9577%, a difference of 0.0007%, for children, preteens, and teenagers in the Age 0 to 19 demographic. This data supports the conclusion that the Age 0 to 19 demographic is at significantly low risk.

By comparison we have calculated the Probability of Fatality in the Age 50+ demographic to be:

Age 50+ Demographic

Total Fatalities – 107,314

Total Cases – 945,856

Probability of Fatality = 11.3457%

Probability of Recovery = 88.6543%

When we adjust the Total Cases for the 50+ demographic we find the following.

Adjusted Total Cases = 945,856 – 7,329 = 938,527

Adjusted Probability of Fatality = 11.4343%

Thus, the Adjusted Probability of Recovery is 88.5657%, a difference of 0.0886%, for adults in the Age 50+ demographic. This data supports the conclusion that the Age 50+ demographic is at significantly higher risk when compared to the Age 0 to 19 demographic.

 Conclusion

This statistical research paper provides objective, data-driven results that can be used for crucial policy development for American children, preteens, and teenagers in the Age 0 to 19 demographic. Children, Preteens & Teens have a Probability of Recovery of 99.9577% from COVID-19 infections.

This data suggests that extreme measures for ensuring the safety of the Age 0 to 19 demographic may potentially do more harm than good. Policy decisions for governance and education should always be weighed against the very real possibility of severe emotional stress, psychological strain, and stunted social development, created by preventing young American citizens from attending school in person, or imposing severe protective measures to limit physical contact while in attendance in school.

We, the authors of this paper, believe that any policy decisions regarding schooling for Americans in the Age 0 to 19 demographic should include considerations for the impacts they create relative to Anxiety, Depression, and the potential of Suicidal Ideation created by prolonged isolation and despair regarding their perception of current world events and their uncertain future.

Mahalo. 

Data Limitations

There are 4 primary data limitations for readers to be aware of concerning the collection and reporting of data for each individual US State & Territory Health Department (USSTHD): (1) The Inclusion of ‘Probable’, ‘Suspected’, Unconfirmed Case, Hospitalization, and Fatality Data; (2) Incomplete Age Demographic Data; (3) Significant Lack of Comorbidity Data; & (4) The Potential For Confusion & Inaccuracy Due To The Lack Of Universal Reporting Guidelines.

 

1. The Inclusion of ‘Probable’, ‘Suspected’, Unconfirmed Data

On March 24th, the National Vital Statistics System (NVSS) issued COVID-19 Alert No. 2 which answered the question ‘Should COVID-19 be reported on the death certificate only with a confirmed test?’10

“COVID-19 should be reported on the death certificate for all decedents where the disease caused or is assumed to have caused or contributed to death. Certifiers should include as much detail as possible based on their knowledge of the case, medical records, laboratory testing, etc. If the decedent had other chronic conditions such as COPD or asthma that may have also contributed, these conditions can be reported in Part II.

On April 14th, the CDC adopted guidelines from the Council of State and Territorial Epidemiologists (CSTE) to further clarify the minimal standards necessary for a case, hospitalization, and/or fatality to qualify as ‘Probable’ for inclusion into reported totals at the local, state, and national levels.11

“A. Narrative: Description of criteria to determine how a case should be classified. A1. Clinical Criteria At least two of the following symptoms: fever (measured or subjective), chills, rigors, myalgia, headache, sore throat, new olfactory and taste disorder(s) OR At least one of the following symptoms: cough, shortness of breath, or difficulty breathing OR Severe respiratory illness with at least one of the following: Clinical or radiographic evidence of pneumonia, or Acute respiratory distress syndrome (ARDS) AND No alternative more likely diagnosis.”

A5. Case Classifications Confirmed: Meets confirmatory laboratory evidence. Probable: Meets clinical criteria AND epidemiologic evidence with no confirmatory laboratory testing performed for COVID-19. Meets presumptive laboratory evidence AND either clinical criteria OR epidemiologic evidence. Meets vital records criteria with no confirmatory laboratory testing performed for COVID19.

Therefore, a patient could present with a cough from unknown origin and, without confirmatory testing widely available today, be counted as a COVID-19 ‘Probable’ case, hospitalization or fatality. This minimal standard of proof remains in place despite over 28,133,272 lab tests being performed through June 21st.

Interestingly, this decision set the stage for every USSTHD to include ‘Probable’ cases, hospitalizations, and fatalities in their reported totals without providing any guidelines for reporting lab negative Molecular RT-PCR, Serologic Viral Load, or Serologic Antibody testing. This letter from the CSTE also gave no guidance for how to report recovery data which would be essential to monitoring the progress of this crisis.

2. Incomplete Age Demographic Data

As of June 21st, only 22 of 56 USSTHDs are providing complete age demographic data for cases, hospitalizations, and fatalities. These USSTHDs include:

  • Arizona, Colorado, Florida, Georgia, Kansas, Massachusetts, Minnesota, Mississippi, Nebraska, New Hampshire, New Jersey, New York (When Coupled With The NY City Health Department), North Dakota, Ohio, Oregon, Rhode Island, South Dakota, Utah, Virginia, Washington (State), & Wisconsin
  • (Please See REFERENCES For A Listing Of All USSTHDs)

Despite thorough investigation, we were unable to locate age demographics for hospitalizations from any of the US State Health Departments not listed previously, as of June 21st.

Additionally, the we are unable to locate age demographic data for fatalities from the following US State Health Departments:

  • Alaska, Hawai’i, Maine, Montana, New Mexico, West Virginia
  • With the exception of New Mexico (134 Confirmed Fatalities), we acknowledge that the remainder of these states currently have low total fatalities that may not warrant detailed age demographics for this metric.

Currently the CDC is reporting age demographics for cases, but not hospitalizations or fatalities on their most visited, ‘Cases in the US’ webpage.12

3. Significant Lack of Comorbidity Data

As of June 21st, only 6 of 56 USSTHDs are providing comorbidity data for fatalities in a manner that can be statistically analyzed to draw important, potentially life-saving conclusions from. These USSTHDs include:

  • New York, Massachusetts, Pennsylvania, Georgia, Utah & Oklahoma

In the May 12th Surveillance Report, the Oregon Health Authority decided to stop reporting comorbidity data (underlying conditions) with the following rationale.14

“With this edition of the weekly report, OHA has chosen not to produce a table of “underlying conditions” among people who have died. Routinely available information about COVID-19 cases lacks sufficient detail to offer useful information about the specific conditions that make up the broad categories included in the table. Consequently, this has led to confusion and unwarranted apprehension about which groups might be at greater risk of dying from COVID-19 infection.

Yet, even with these limitations we were able to analyze the data available to determine that out of 42,147 fatalities 38,299 (90.9%) had at least 1 major comorbidity, 3,166 (7.5%) had no known comorbidity, and 682 (1.6%) any comorbidity was unknown.

This data is statistically significant and provides a much more accurate interpretation of the current clinical situation all healthcare providers should be aware of to better assist them in creating positive patient outcomes.

It is important to note the top 3 comorbidities thus far are Hypertension (High Blood Pressure), Diabetes, and Hyperlipidemia (Elevated Cholesterol Levels).

Currently, the CDC is not reporting comorbidity demographics on their most visited, ‘Cases in the US’ webpage. 12

4. Lack of Universal Reporting Guidelines

Minimal Universal Reporting Guidelines, from the WHO or CDC, for all USSTHDs do not currently exist.

One of the many challenges in collecting data from each USSTHD in order to accurately assess the clinical situation at the state and national level is the lack of clear Universal Reporting Guidelines as a common standard and baseline for data collection and reporting.

For example, the age range breakdown varies from health department to health department. Some health departments will report age demographic data using a traditional decade scale like New York (0 to 9, 10 to 19, 20 to 29, 30 to 39, 40 to 49, 50 to 59, 60 to 69, 70 to 79, 80 to 89, 90+), while others will report age demographic data using a varied targeted scale like Arizona (Less than 20, 20 to 44, 45 to 54, 55 to 64, 65+).

Obviously, this makes the job of data organization and nationwide compilation & analysis exceedingly difficult for complete accuracy thus introducing the potential for measurement error. As a result, in order to organize this data for review we have had to make some educated, yet subjective, decisions as to which category certain data falls into for age related demographics when compiling data for nationwide analysis.

As a result, we have organized the available age demographic data into the following measurement scale (Age 0 to 19, Age 20 to 49, Age 50+) based upon the variance in reporting style. A special category for fatalities 70 years of age or older was also created. Please note that this category will include some data for fatalities 65 years or older for certain health departments using a scale 65+ measurement scale.

Minimal Universal Reporting Guidelines for separating data based upon Probable vs Lab Confirmed, Male vs Female, Cultural Demographics, Common Decade Scale For Age Related Demographics, & Comorbidity would allow for comparison and compilation from each state, while also allowing each health department to collect additional data at their unique discretion.

Supplemental Comorbidity Data

 

Based upon the compiled data we are able to conclude that comorbidity (‘pre-existing conditions’) plays a large role in fatalities, particularly in the Age 50+, high-risk, demographic that has been so adversely impacted during this crisis.

Unfortunately, as of June 21st, only 6 of 51 Health Departments were publishing this data in a meaningful way that can be analyzed. New York, Pennsylvania, Massachusetts, Georgia, Utah, & Oklahoma are the state health departments reporting this data.

There are several other states publishing comorbidity data related to cases rather than fatalities or publishing data in a manner that cannot be analyzed with any statistical degree of confidence. (See Table 6 for a complete look at how this graph was developed)

The New York State Department of Health is the only US health department providing Age & Comorbidity data combined. For example, of the 24,725 Total Fatalities in New York, 4 were in the 0 to 9 Age Range and 11 were in the 10 to 19 Age Range. Of these 15 total fatalities, 5 comorbidities were reported.

 

Clearly, comorbidity plays a very significant role in fatalities and warrants further investigation.

Table 6 – Comorbidity Data

 

Funding & Conflict of Interest Statement

This statistical research paper has been developed, composed and published without any funding and thanks in part to a strictly, 100% volunteer community effort made by a diverse array of qualified professionals who care deeply about children and the health of every American. The authors of this paper confirm no conflicts of interest, financial, political or otherwise.

References

  1. Kopecki, Higgins-Dunn, Miller; CDC tells people over 60 or who have chronic illnesses like diabetes to stock up on goods and buckle down for a lengthy stay at home; CNBC, March 9, 2020, https://www.cnbc.com/2020/03/09/many-americans-will-be-exposed-to-coronavirus-through-2021-cdc-says.html
  2. Marzo et al; Report on the characteristics of patients who died positive for COVID-19 in Italy: based on data updated to 17 March 2020; https://www.epicentro.iss.it/coronavirus/bollettino/Report-COVID-2019_17_marzo-v2.pdf
  3. Thomala; Fatality rate of novel coronavirus COVID-19 in China as of February 11, 2020, by age group; Statista, April 2, 2020; https://www.statista.com/statistics/1099662/china-wuhan-coronavirus-covid-19-fatality-rate-by-age-group/
  4. Special Expert Group for Control of the Epidemic of Novel Coronavirus Pneumonia of the Chinese Preventive Medicine Association; Chin J Epidemiol, 2020,41:Epub ahead of print. DOI: 10.3760/cma.j.issn.0254-6450.2020.02.002
  5. CDC: Coronavirus Disease 2019 (COVID-19) https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html
  6. Campbell, Leah: How COVID-19 Could Affect Kids’ Long-Term Social Development; healthline; April 3, 2020: https://www.healthline.com/health-news/social-distancing-effects-on-social-development
  7. Impact of COVID-19 on children and Young People’s Mental Health: results of survey with parents and carers. Young Minds; May 14, 2020: https://youngminds.org.uk/media/3774/youngminds-survey-with-parents-and-carers-summary-of-results.pdf
  8. Lee, Joyce; Mental health effects of school closures during COVID-19; The Lancet: April 17, 2020; https://www.thelancet.com/pdfs/journals/lanchi/PIIS2352-4642(20)30109-7.pdf
  9. Thomas, Emma; Coronavirus: Impact on Young People with Mental Health Needs; Young Minds; March 24, 2020: https://youngminds.org.uk/media/3708/coronavirus-report_march2020.pdf
  10. Schwartz; New ICD code introduced for COVID-19 deaths; National Vital Statistics System; March 24, 2020; https://www.cdc.gov/nchs/data/nvss/coronavirus/Alert-2-New-ICD-code-introduced-for-COVID-19-deaths.pdf
  11. Council of State & Territorial Epidemiologists; Standardized surveillance case definition and national notification for 2019 novel coronavirus disease (COVID-19); Interim-20-ID-01; https://cdn.ymaws.com/www.cste.org/resource/resmgr/2020ps/Interim-20-ID-01_COVID-19.pdf
  12. CDC: Coronavirus Disease 2019 (COVID-19) https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html
  13. https://www.oregon.gov/oha/ERD/Pages/covid-19-news.aspx?wp6965=f:{c:38877,o:{t:2,o:[%22Weekly+Report%22]}}

State & Territory Health Departments

  1. Alaska Department of Health & Social Services Coronavirus Response: https://coronavirus-response-alaska-dhss.hub.arcgis.com/
  2. Alabama’s COVID-19 Data and Surveillance Dashboard: https://alpublichealth.maps.arcgis.com/apps/opsdashboard/index.html#/6d2771faa9da4a2786a509d82c8cf0f7
  3. https://www.healthy.arkansas.gov/programs-services/topics/novel-coronavirus
  4. Arkansas Department of Health: https://azdhs.gov/preparedness/epidemiology-disease-control/infectious-disease-epidemiology/covid-19/dashboards/index.php
  5. California COVID-19 Dashboard: https://public.tableau.com/views/COVID-19PublicDashboard/Covid-19Hospitals?:embed=y&:display_count=no&:showVizHome=no
  6. Colorado Department of Public Health & Environment, Case Data: https://covid19.colorado.gov/data/case-data
  7. Connecticut COVID-19 Response: https://portal.ct.gov/Coronavirus
  8. Government of the District of Columbia, Coronavirus Data: https://coronavirus.dc.gov/page/coronavirus-data
  9. State of Delaware COVID-19 Data Dashboard: https://myhealthycommunity.dhss.delaware.gov/locations/state
  10. Florida COVID-19 Response: https://floridahealthcovid19.gov/
  11. Georgia Department of Public Health: https://dph.georgia.gov/covid-19-daily-status-report
  12. State of Hawaii Department of Health, Disease Outbreak Division: https://health.hawaii.gov/coronavirusdisease2019/
  13. Iowa Department of Public Health https://idph.iowa.gov/Emerging-Health-Issues/Novel-Coronavirus
  14. Idaho Department of Public Health Dashboard: https://public.tableau.com/profile/idaho.division.of.public.health#!/vizhome/DPHIdahoCOVID-19Dashboard_V2/Story1
  15. Illinois Department of Public Health COVID-19 Statistics: http://www.dph.illinois.gov/covid19/covid19-statistics
  16. Indiana COVID-19 Dashboard: https://www.coronavirus.in.gov/
  17. Kansas Department of Health & Environment, COVID-19 Cases in Kansas: https://www.coronavirus.kdheks.gov/160/COVID-19-in-Kansas
  18. Kentucky Cabinet for Health & Family Services: https://govstatus.egov.com/kycovid19
  19. Louisiana Department of Health: http://ldh.la.gov/Coronavirus/
  20. Massachusetts Department of Public Health COVID-19 Dashboard -Dashboard of Public Health Indicators: https://www.mass.gov/info-details/covid-19-response-reporting
  21. Maryland Department of Health: https://coronavirus.maryland.gov/
  22. Maine Center for Disease Control & Prevention: https://www.maine.gov/dhhs/mecdc/infectious-disease/epi/airborne/coronavirus/index.shtml
  23. Michigan Coronavirus Data: https://www.michigan.gov/coronavirus/0,9753,7-406-98163_98173—,00.html
  24. Minnesota Department of Health: https://www.health.state.mn.us/diseases/coronavirus/situation.html
  25. Missouri COVID-19 Dashboard: http://mophep.maps.arcgis.com/apps/MapSeries/index.html?appid=8e01a5d8d8bd4b4f85add006f9e14a9d
  26. Mississippi State Department of Health: https://msdh.ms.gov/msdhsite/_static/14,0,420.html#caseTable
  27. MONTANA RESPONSE: COVID-19 – Coronavirus – Global, National, and State Information Resources: https://montana.maps.arcgis.com/apps/MapSeries/index.html?appid=7c34f3412536439491adcc2103421d4b
  28. North Carolina NCDHHS COVID-19 Response: https://covid19.ncdhhs.gov/https://www.health.nd.gov/diseases-conditions/coronavirus/north-dakota-coronavirus-cases
  29. Coronavirus COVID-19 Nebraska Cases by the Nebraska Department of Health and Human Services (DHHS): https://nebraska.maps.arcgis.com/apps/opsdashboard/index.html#/4213f719a45647bc873ffb58783ffef3
  30. New Hampshire Department of Health & Human Services: https://www.nh.gov/covid19/
  31. New Jersey COVID-19 information Hub: https://covid19.nj.gov/#live-updates
  32. https://cv.nmhealth.org/
  33. State of Nevada Department of Health & Human Services, Office of Analytics: https://app.powerbigov.us/view?r=eyJrIjoiMjA2ZThiOWUtM2FlNS00MGY5LWFmYjUtNmQwNTQ3Nzg5N2I2IiwidCI6ImU0YTM0MGU2LWI4OWUtNGU2OC04ZWFhLTE1NDRkMjcwMzk4MCJ9
  34. New York Department of Health, NYSDOH COVID-19 Tracker: https://covid19tracker.health.ny.gov/views/NYS-COVID19-Tracker/NYSDOHCOVID-19Tracker-Map?%3Aembed=yes&%3Atoolbar=no&%3Atabs=n
  35. New York City Coronavirus Data: https://github.com/nychealth/coronavirus-data
  36. https://www1.nyc.gov/site/doh/covid/covid-19-data.page
  37. Ohio Department of Health: https://coronavirus.ohio.gov/wps/portal/gov/covid-19/home
  38. Oklahoma State Department of Health: https://coronavirus.health.ok.gov/
  39. Oregon Health Authority: https://govstatus.egov.com/OR-OHA-COVID-19
  40. COVID-19 Data for Pennsylvania: https://www.health.pa.gov/topics/disease/coronavirus/Pages/Cases.aspx
  41. Puerto Rico Health Statistics: https://estadisticas.pr/en/covid-19
  42. Rhode Island COVID-19 Response Data: https://ri-department-of-health-covid-19-data-rihealth.hub.arcgis.com/
  43. South Carolina Testing Data & Projections (COVID-19): https://scdhec.gov/infectious-diseases/viruses/coronavirus-disease-2019-covid-19/sc-testing-data-projections-covid-19
  44. South Dakota Department of Health: https://doh.sd.gov/news/Coronavirus.aspx
  45. Tennessee Department of Health: https://www.tn.gov/health/cedep/ncov.html
  46. Texas Health & Human Services: https://txdshs.maps.arcgis.com/apps/opsdashboard/index.html#/ed483ecd702b4298ab01e8b9cafc8b83
  47. Utah Department of Health: COVID-19 Surveillance: https://coronavirus-dashboard.utah.gov/
  48. Virginia Department of Health: https://public.tableau.com/views/VirginiaCOVID-19Dashboard/VirginiaCOVID-19Dashboard?:embed=yes&:display_count=yes&:showVizHome=no&:toolbar=no
  49. S Virgin Islands Department of Health: https://doh.vi.gov/
  50. Vermont Current Activity Dashboard: https://www.healthvermont.gov/response/coronavirus-covid-19/current-activity-vermont
  51. Washington State Department of Health: https://www.doh.wa.gov/Emergencies/Coronavirus
  52. Wisconsin Department of Health Services: https://www.dhs.wisconsin.gov/covid-19/data.htm
  53. West Virginia Health & Human Resources: https://dhhr.wv.gov/COVID-19/Pages/default.aspx
  54. Wyoming Department of Health: https://health.wyo.gov/publichealth/infectious-disease-epidemiology-unit/disease/novel-coronavirus/covid-19-map-and-statistics/

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