Critically Examining COVID Data

Interpreting “COVID Cases,” “COVID Hospitalizations,” and “COVID Deaths”

An Exercise in Critical Thinking and Healthy Dialogue

On November 10, 2020 CNN reported the following [1]:

“After reporting 100,000 new coronavirus infections seven days in a row, the US has now surpassed a total of more than 10 million cases since the start of the pandemic — far more than any other country. And that number will likely keep rapidly climbing, one expert told CNN.

‘We are watching cases increase substantially in this country far beyond, I think, what most people ever thought could happen,’ Michael Osterholm, director of the Center for Infectious Disease Research and Policy at the University of Minnesota, told CNN’s Anderson Cooper on Monday. Osterholm has been named a member of President-elect Joe Biden’s Transition Covid-19 Advisory Board.

‘It will not surprise me if in the next weeks we see over 200,000 new cases a day,’ he added.

The country’s seven-day average of new daily cases was 119,238 on Monday [11/9] — more than three times higher than it was around mid-September, when it was at a post-summer-surge low.

But it’s not just the rising number of infections that is alarming. On Monday, the US had more than 59,200 people hospitalized nationwide, according to the COVID Tracking Project.

That’s the country’s highest total number since July 25, and not far from the nation’s pandemic peak of 59,940 set on April 15.

And as more people are infected and more are hospitalized, more American deaths will likely be recorded daily. Last week saw five days in a row with more than 1,000 Covid-19 deaths — the first time that’s happened since August.

More than 238,000 people have died since the start of the pandemic in the US, according to Johns Hopkins University. Another 110,000 or more deaths are projected in the next two months, according to the University of Washington’s Institute for Health Metrics and Evaluation.”

CRITICAL EXAMINATION OF DATA:

COVID-19 can be life-threatening and life-taking, especially in the elderly and frail. A tragic number of people have died from COVID. The above CNN report further heightens fears about COVID-19.

In the tradition of rigorous science and careful, disciplined practice of medicine, it is imperative to critically examine such data— to determine the scientific quality of the data and whether they understate, accurately state, or overstate the threat we face. It is not scientific to accept data at face value, without thorough scrutiny. Included in this critical examination is the need to pay attention to language and insist on careful, accurate use of language.

This article is intended to facilitate healthy, respectful, evidence-based dialogue about COVID-related data. The purpose of the article is to emphasize what questions need to be asked about the quality of the data being collected and reported. The goal of the article is to raise the necessary questions, not necessarily answer them. However, it is hoped that these questions will be answered by those who are leading our nation’s response to this epidemic (e.g., the USA Task Force on COVID, the CDC, Johns Hopkins University Bloomberg School of Public Health, WHO, the University of Washington Institute for Health Metrics and Evaluation, and the Gates Foundation).

To determine the quality and meaning of reported data, such as that reported by CNN on 11/10/20, it is wise to ask the following questions:

• What definition of a “new case” is being used in this reportage? What criteria must be fulfilled for an event to be declared a “new case”?
• Do these “119,238 new coronavirus infections” occurring per day represent people who have been confirmed to be actively and definitely infected with the COVID-19 virus? Or do these 119,238 new coronavirus “cases,” more accurately, represent simply the number of “positive COVID tests” that have been recorded? This distinction is important, because a “positive COVID test” may or may not mean that the tested person has truly been infected with the COVID-19 virus [2-34]; and, even in the case of a true positive, may or may not mean that the person is capable, at the time, of infecting others [3, 8, 24-26].
• Is a “positive COVID test,” alone, sufficient to be counted as a “new case?” That is, can asymptomatic people who happen to have a “positive COVID test” be counted as “new cases?” Or to be counted as a new case, must a person be both “COVID test positive” and have COVID symptoms? If so, what minimum number and types of symptoms must be present (and when, chronologically) for a person to be considered to have “COVID symptoms?”
• Can a person be counted as a “new case,” if they are not COVID-test positive, or they have not been tested, but they have some COVID-like symptoms (which ones and when?) with or without at least some degree of exposure to a person with definite COVID? Could the exposure be to a person with less definite COVID?
• What criteria must be fulfilled to declare a person’s COVID test to be positive? Does a “positive COVID test” need to be a PCR test, or could it be one of the less reliable rapid antigen tests?
• For PCR tests used, what cycle threshold (Ct) “cutoff” number is being used for declaration of “definite positivity?” For a detailed explanation of Ct values, see APPENDIX: Understanding Ct values and estimating viral load. Also, see companion article on Ct values and REFERENCES [2-34]
• For PCR tests that are positive only at a Ct of 37 or 40, or higher, are we sure that that positivity is truly and always due to definite COVID infection, especially in asymptomatic or minimally and non-specifically symptomatic people who were tested as part of a screening (surveillance) process?
• What is the false positivity rate of PCR tests? Is the false positivity rate higher when the test is used in a surveillance setting, as opposed to a hospital/ICU setting? Is the false positivity rate higher in people whose test is only weakly positive (Ct of 37 or 40, or 45), than in people whose test is strongly positive?
• Of the recent/current COVID test positive “new cases” what was the breakdown regarding the degree of test positivity—what percentage were positive only at a Ct of 37 or higher? What percentage were positive only at a Ct of 35-37? What percentage were positive only at a Ct between 30-34? What percentage were positive at a Ct less than 30; less than 24; less than 20; less than 15; less than 10? Do these Ct statistics matter? Does it matter whether the test was strongly positive or only very weakly positive?
• Of the “new cases,” what is the breakdown regarding degree of symptoms—what percentage were asymptomatic, mildly symptomatic, moderately symptomatic, severely symptomatic, and specifically symptomatic of COVID?
• When a “positive” COVID test result is reported to the State Health Department and then to the CDC and the Johns Hopkins Bloomberg School of Public Health database, who has filled out a data record form on that person and how frequently and accurately does it contain all the above-mentioned clinical information, including details about symptoms and the Ct at which their test was positive? Or is there often little or no clinical information provided, other than the fact that the person “had a positive COVID test result?”
• The World Health Organization (WHO) and many government health ministries have encouraged diagnosis of SARS-CoV-2 infection (COVID) on the basis of a single positive PCR result, even in asymptomatic persons without any history of exposure. [7] For example, WHO has defined a “confirmed case” as a person with a positive test result, “irrespective of clinical signs and symptoms.” [35] Is that the policy that is being applied to the “new case” counts being currently reported in the USA?
• In the USA, the Council of State and Territorial Epidemiologists (CSTE) has developed a more appropriate and nuanced “COVID-19 Interim Case Definition,” which was approved by the CDC in August 2020. [36] To what extent are the criteria recommended by the CSTE being strictly applied in the current collection, determination, and reportage of “new COVID cases” in the USA? Of the 119,238 daily “new cases” reported on 11/10/20, what percentage of them fulfilled strict CSTE criteria for presence of “confirmatory” vs “presumptive” vs only “supportive” evidence for designation as a “new case.” If a person had a “positive COVID test,” but other details needed to make an accurate CSTE designation were missing, was such a person, nevertheless, entered as a “new case,” or were such people designated as “possible new case, but available information is insufficient” and not included in the case count, or included as a separately reported “possible case”?
• Bear in mind that even the more demanding “case definition” recommended by the CSTE does not include any mention of the extent to which a COVID test is positive. According to the CSTE recommendation, a person whose PCR test for COVID is positive only at a Ct of 40, or even 45 (very weak positivity) would be considered to have “confirmatory laboratory evidence” of COVID. Does that represent a scientifically sound decision?
• Likewise, the case definitions recommended by the CDC and WHO do not include any mention of the extent of COVID test positivity (e.g., Ct values). Is this wise?
• To what extent have efforts been made to ensure that “new cases” are not counted more than once? For example, if a previously known “COVID positive” person has had several positive follow-up re-tests (to document possible ongoing infectivity) is each positive re-test included in the “new case” count? How is such duplicate counting prevented?
• If 119,238 “new cases” were noted on a particular day in the USA (on 11/9/20, e.g.), how many people were tested on that same day? It turns out that approximately 1.5 million tests were performed daily during the week before 11/10/20. What was the breakdown, regarding percentage of those 119,238 that were positive only at a Ct greater than 35? What percentage were positive at a Ct of 30 or less?
• How many false positives would be expected, if 1.5 million asymptomatic or minimally and non-specifically symptomatic people were tested, as part of a mass screening/surveillance campaign? Since we currently do not know what the false positivity rate is in the mass surveillance setting, we do not know how many of the 1.5 million would be expected to have a false positive result.
• During a given week, to what extent is a portion of a “rising daily new case count” simply a reflection of a rising number of people being tested?
• Regarding the reported number of “new COVID hospitalizations,” were all these patients hospitalized primarily because of COVID illness, or were some (what percentage?) simply patients who were hospitalized for other reasons and happened to have a positive COVID test (how strongly or weakly positive?) when routinely screened at the time of admission?
• Likewise, regarding new “COVID deaths,” what criteria were strictly applied in each case to accurately determine whether a patient truly died of COVID, as opposed to dying from some other cause, but having a “positive COVID test” (again, how strongly or weakly positive?) or even just COVID exposure? How complete and accurate has information on these “COVID death” certificates been? On a death certificate, does the mere mention of a positive COVID test, or exposure to someone with COVID positivity, mean that that death is automatically included in the national “COVID death” count? To what extent have those who complete death certificates been told that they must list “COVID test positivity” and “COVID exposure” on a death certificate (if such positivity and exposure have occurred), even if COVID was not considered the cause of death or even a minor contributing factor? In past years, have “influenza test positivity” or “exposure to influenza” been listed on death certificates, if influenza was not considered one of the main causes of death, or even a minor contributing cause? What have the rules been, regarding this issue, and how uniformly have rules been applied.
• Regarding people who definitely died of COVID, what treatment did they receive? Did they receive prompt, timely, appropriately aggressive immunosuppression, if they had evidence of hyperinflammation/cytokine storm? What percentage of true COVID deaths could have been prevented with application of a different, more aggressive treatment approach? [37,38] (See companion article on Treatment of Severe COVID Illness.)
• In short, do the above CNN-reported data represent solid, quality data that were collected and interpreted in a scientifically sound fashion and accurately reflect reality—and might even understate the threat we face?
• Or have the COVID data collected and reported (at least in the USA) been of much lower scientific quality than has been assumed.

Additional questions, specifically about COVID PCR testing:

• Is it possible that, when currently available COVID lab tests are used in the setting of surveillance (screening), a high percentage of the positive results could be due to presence of just trace amounts of inert, non-viable, non-infectious viral material left over from past COVID infection that had caused mild or no symptoms? In other words, rather than indicating an explosive outbreak of threatening new cases, is it possible that the vast majority of the 119,238 “new cases” represent people who, we are now discovering (after a marked increase in the number of people being tested), have had distant exposure to COVID (that was either asymptomatic or caused only mild-moderate symptoms), but are no longer contagious and currently represent little or no threat to others?
• Is it possible that, when these tests are used in a surveillance/screening situation, the false positivity rate might be higher than has been appreciated to date, at least for some test kits—particularly in people whose test results are only weakly positive? Do we know what percentage of the 119,238 new cases might represent false positives?
• Instead of ignoring Ct values, should we not take them into account, both in the management of individual patients and in epidemiologic studies? Is it not important to ask:
  • What percentage of the 119,238 “new cases” have had a positive COVID PCR test only at a Ct of 37, 40, or 45—as opposed to being positive at a Ct of 30 or less?
  • What might a positive COVID PCR result at a Ct of 37 (or greater) mean and most commonly mean?
  • What is the correlation between the Ct value at which a person’s COVID test is positive and that person’s likely viral load and degree of infectivity (contagiousness)?
  • What Ct cutoff value would be most appropriate for declaring a COVID PCR test to be “definitely positive?” For example, if the Ct cutoff value for positivity were set at 30 or 32, there would be far fewer positive results, and the vast majority of “possible missed COVID cases” (those who are positive at a Ct of 37, 40, or 45) would be people with tiny amounts of inert, non-viable, non-contagious, “dead” viral material, or people with false positive results.
  • In surveillance testing, would it make more sense to focus on detection of those people who are contagious, rather than detection, also, of all people who have a tiny amount of inert, non-viable, non-infectious remnants of COVID virus?
  • Is there need to better standardize and better determine the reliability of the more than 150 COVID tests on the market, none of which has undergone the rigorous process of FDA approval? They have, so far, been granted only temporary “emergency use authorization” (EUA).
  • Should we not prominently discuss Ct values, not only with those individuals who have “tested positive,” but also in our national public discussion and education about COVID?
  • Should we not take Ct values into consideration when data on “new COVID cases,” new COVID hospitalizations,” and “new COVID deaths” have been collected and reported?
• Should we not make certain that those patients who develop life-threatening COVID are being properly understood and properly treated, with treatment being guided, in part, by serial documentation of viral load (which can be estimated via serial Ct values)? See companion article on Treatment of Severe COVID Illness.[38]
• Out of respect for Science and Humanity, do we not have a moral and ethical obligation to make certain that data on COVID are being collected, interpreted, and reported in a careful, disciplined, rigorously scientific fashion?

Without answers to the above questions, it is difficult to know what a “new case” means, what a “rising new case count” means, or what a “rise in COVID hospitalizations” and a “rise in COVID deaths” mean. The purpose of this article is not necessarily to answer all of the above questions, but to emphasize the importance of critically examining data and engaging in healthy dialogue about those data—a process that starts with asking the right questions.

So, what is the true number of daily “new cases” of COVID, “new COVID hospitalizations,” and “new COVID deaths” in the USA?

To review, on 11/10/20 CNN reported that over the preceding week:

• 119,238 “new cases of COVID” were occurring per day (on average)
• “Soon, there will likely be 200,000 new cases occurring per day.”
• 59,000 “new COVID hospitalizations” were occurring per day
• More than 1000 “new COVID deaths” were occurring per day

How accurate are the above numbers? The short answer is that we do not know how accurate they are—because we do not know the extent to which these data represent quality data.

A fundamental principle of rigorous science, medicine, and clinical research is to establish, implement, and enforce use of adequate and accurate uniform criteria for what constitutes a “new case” of COVID, a “new COVID hospitalization,” and a “new COVID death.” Complete and accurate clinical details are needed to accurately make these designations. Inclusion of Ct details would add greatly to interpretation of data. Throughout this COVID pandemic have these fundamental scientific principles been rigorously followed? Or, instead, has clinical data collection been conducted and reported in an undisciplined fashion?

For example, we do not know the extent to which wise clinical criteria were strictly and accurately applied in the collection of the data reported on 11/10/20; or the extent to which lab testing was most wisely used and interpreted. Afterall, the Ct at which the PCR test was positive in the “new cases” was not even taken into consideration and was not likely to have been reported.

It is possible that the CNN-reported data are roughly correct—but, details about the data collection process and the data have not been made clear. Is it possible that a majority, even a vast majority, of the 119,238 “new daily cases” represented people who were asymptomatic (or had mild and non-specific symptoms) and, upon screening, had a “positive COVID PCR test” at a Ct of 37 or 40, or 35 at the lowest? And is it possible that the positive test (at a Ct of 37 or 40) in the vast majority of such people was due either to presence of a tiny, trace amount of inert, non-infectious viral debris left over from previous exposure to COVID (meaning that these people were no longer a threat to infect others), or represented a false positive (i.e., the person did not have COVID and had never had COVID)?

Among many urgencies, there is urgent need to accurately determine the exact percentage of the “119,238 new daily cases” (and of future totals) that were positive only at a Ct of 37 or 40, or 45, and had only been tested as part of a screening process. And it will be essential to determine what percentage of these “high Ct positive tests” represented false positives.

Based on an Italian study [17] and other studies [8,9], it is conceivable that 90% of the 119,238 new cases (107,314) were people who had a positive COVID test at a Ct of 37 or higher; that 30% of these 107,314 people with high CT positive results represented people with true (but non-contagious) positivity; and that 70% of these 107,314 people represented false positive results (based on the Italian data).

If wise clinical and laboratory criteria (including Ct values) have not been strictly, uniformly, and accurately applied in the collection of data regarding “new COVID hospitalization” and “new COVID deaths, then these reported data also need to be interpreted with great caution.

As stated at the outset of this article, it is important to critically examine the reported data, regarding “new COVID cases,” “new COVID hospitalizations,” and “new COVID deaths,” as opposed to unquestioningly and uncritically accepting these data at face value. There is good reason to be concerned that the quality of COVID-related data collected and reported to date (regarding new cases, new hospitalizations and new deaths) has been poor. Accordingly, the reported data need to be interpreted with great caution, and the data need to be re-examined. More scientifically sound, higher quality data, including careful prospective collection and analysis of Ct data, are desperately needed.

Bottom Line:

The quality of data, regarding “new COVID cases,” “COVID hospitalizations,” and “new COVID deaths,” depends, fundamentally, on:

1. The quality, reliability, interpretation, and wise use of the COVID tests upon which these data are based; and
2. Disciplined and uniform use of carefully constructed diagnostic criteria for “COVID case,” “COVID hospitalization,” and “COVID death.”

If most COVID tests are using a Ct cut-off of 37 or 40; if most positive tests (especially in surveillance testing) are positive only at a Ct of 35 or greater; if most people with a positive test at a Ct of 37 or 40 are not contagious; and if the currently unknown incidence of false positive tests (especially in the surveillance setting) is higher than heretofore appreciated; then, the currently reported COVID data may be greatly inaccurate, or at least excessively alarming—particularly “new case” data that have been generated primarily through mass screening of increasing numbers of asymptomatic and minimally (and non-specifically) symptomatic people. In other words, it is possible that a certain (but, not yet known) percentage of the recently reported 119,238 “new COVID cases” represent people who, yes, have had past exposure to COVID (even distant exposure), but are not currently actively infected or contagious; and a currently unknown percentage might represent people with false positive COVID test results.

Similarly, if Ct values and uniform criteria have not been applied to reported data on COVID hospitalizations and COVID deaths, these data need to be viewed with caution. Finally, if treatment of patients with severe COVID illness has been suboptimal, COVID death data will be misleading.

We must make certain that fundamental principles of science, medicine, clinical care, and clinical/epidemiological research are being followed in the collection, interpretation, and reporting of COVID cases, COVID hospitalizations, and COVID deaths.

APPENDIX:

Understanding Ct values and estimating viral load:

Although the COVID-19 PCR test is designed as a qualitative test, aspects of it (namely the Ct value at which the patient’s test is positive) can be used to estimate viral load. Ct = Cycle threshold; Ct = the number of amplification cycles needed before the test detects presence of viral material in a specimen. The Ct value is the inverse of the viral load. The higher the Ct needed to detect the viral material, the lower the viral load in the specimen and the less sick and contagious the person is likely to be. [2-9]

If a test is positive at a Ct of 12 (becomes positive after only 12 amplification cycles), the viral load might be 100,000,000 copies per microliter, or more. [3, 8, 9] If the test is positive at a Ct of 22, the viral load might be approximately 2,500,000 copies/mL. [31, 32] If the test becomes positive only at a Ct of 37, 40, or 45, the result most likely represents either a false positive, or a true positive that is detecting a trace amount (less than 100 copies, possibly even just a few copies) of inert, non-contagious, “dead” SARS-CoV-2 viral debris. [3, 8, 9]

Knowing the Ct value at which a severely ill patient’s COVID-19 test is positive, would be immensely helpful to a physician who would like to know how much of a viral load the patient is carrying and whether it is relatively safe (or not) to administer life-saving immunosuppression, if careful monitoring reveals need for the latter. By using serial Ct values for guidance, the precision and timing of treatment of severe COVID-19 illness could be markedly improved. This, in turn, could reduce morbidity, mortality, need for mechanical ventilation, duration of hospital and ICU stays, and cost of care.

Knowing the Ct value at which a test is positive in a person who is participating in a screening/surveillance effort would also be valuable. For example, if such a person’s test comes back positive, but only at a Ct of 40, and that person is asymptomatic, it is unlikely that the person is contagious (unless they are in the pre-symptomatic phase of illness, in which case the test can be repeated in 1-3 and the person is monitored for development of symptoms), and their result might even represent a false positive. [8]

Unfortunately, to date, the COVID-19 PCR test has been reported only in a binary fashion, as being either positive or negative, with no indication of how strongly or weakly positive. Although the Ct information has always been available for each result, it has not been routinely reported or used for clinical (or epidemiological) purposes.

Another problem is that there has typically been a delay (often of 3-4 days) in receiving results of the COVID-19 PCR test. COVID-19 PCR results can be made available in a short amount of time, if urgently needed. It takes only 45-60 minutes, or less, to perform the test. Testing could be prioritized so that results on inpatients could be received promptly.

REFERENCES:

1. https://www.cnn.com/2020/11/10/health/us-coronavirus-tuesday/index.html
2. Binnicker MJ. 2020. Challenges and controversies to testing for COVID-19. J Clin Microbiol 58: e01695-20. https://doi.org/10 .1128/JCM.01695-20
3. Tom MR, Mina MJ. To Interpret the SARS-CoV-2 Test, Consider the Cycle Threshold Value. Clin Infect Dis. 2020 May 21: ciaa619. Published online 2020 May 21. doi: 10.1093/cid/ciaa619
4. Hosseini A, Pandey R, Osman E, et al. Roadmap to the Bioanalytical Testing of COVID-19: From Sample Collection to Disease Surveillance [published online ahead of print, 2020 Oct 30]. ACS Sens. 2020; acssensors.0c01377. doi:10.1021/acssensors.0c01377
5. Guo JJ, Yu YH, Ma XY, et al. A multiple-center clinical evaluation of a new real-time reverse transcriptase PCR diagnostic kit for SARS-CoV-2. Future Virol. 2020;10.2217/fvl-2020-0299. doi:10.2217/fvl-2020-0299
6. Wang M, Chen D, Wu W, et al. Analytical performance evaluation of five RT-PCR kits for severe acute respiratory syndrome coronavirus 2 [published online ahead of print, 2020 Oct 27]. J Clin Lab Anal. 2020; e23643. doi:10.1002/jcla.23643
7. Yüce M, Filiztekin E, Özkaya KG. COVID-19 diagnosis -A review of current methods [published online ahead of print, 2020 Oct 24]. Biosens Bioelectron. 2020; 172:112752. doi:10.1016/j.bios.2020.112752
8. TWiV 640: Test often, fast turnaround, with Michael Mina. https://youtu.be/kDj4Zyq3yOA
9. Your Coronavirus Test is Positive. Maybe it shouldn’t be. Interview with Michael Mina, MD. Published August 29, 2020; Updated September 17, 2020. https://www.nytimes.com/2020/08/29/health/coronavirus-testing.html
10. Vandenberg O, et al. Considerations for diagnostic COVID-19 tests. Nat Rev Microbiol. 2020 Oct 14: 1–13. doi: 10.1038/s41579-020-00461-z
11. Surkova E, et al. False-positive COVID-19 results: hidden problems and costs. Lancet Resp Med. published online, September 29, 2020. https://doi.org/10.1016/52213-2600(20)30453-7
12. Mayers C, Baker K. Impact of false-positives and false-negatives in the UK’s COVID-19 RT-PCR testing programme. June 3, 2020. https://assets. publishing.service.gov.uk/government/uploads/system/uploads/ attachment_data/file/895843/S0519_Impact_of_false_positives_and_ negatives.pdf (accessed Aug 8, 2020).
13. Skittrall JP, Wilson M, Smielewska AA, et al. Specificity and positive predictive value of SARS-CoV-2 nucleic acid amplification testing in a low-prevalence setting [published online ahead of print, 2020 Oct 14]. Clin Microbiol Infect. 2020; S1198-743X (20)30614-5. doi:10.1016/j.cmi.2020.10.003
14. Hellou MM, et al Nucleic-acid-amplification tests from respiratory samples for the diagnosis of coronavirus infections: systematic review and meta-analysis, Clinical Microbiology and Infection, https://doi.org/10.1016/j.cmi.2020.11.002.
15. Corman VM, Landt O, Kaiser M, et al. Detection of 2019 Novel Coronavirus (2019-NCoV) by Real-Time RT-PCR. Euro Surveill. 2020, 25 (3), 1−8.
16. Corman V B, leicker T, Brunink S, et al. Diagnostic Detection of Wuhan Coronavirus 2019 by Real-Time RT-PCR; World Health Organization, 2020; pp 1−12.
17. Francesca F, et al. Detection of SARS-COV N2 Gene: Very low amounts of viral RNA or false positive? J Clin Virol. 2020 Dec; 133: 104660. Published online 2020 Oct 14. doi: 10.1016/j.jcv.2020.104660.
18. Katz AP, et al. False positive reverse transcriptase polymerase chain reaction screening for SARS-CoV-2 in the setting of urgent head and neck surgery and otolaryngologic emergencies during the pandemic: Clinical implications, Head Neck 42 (7) (2020) 1621–1628, https://doi.org/10.1002/hed.26317
19. Wang Z, et al. External Quality Assessment for Molecular Detection of Severe Acute Respiratory Syndrome Coronavirus 2 in Clinical laboratories, The Journal of Molecular Diagnostics (2020), doi: https://doi.org/10.1016/j.jmoldx.2020.10.008.
20. Cohen AN, et al. Diagnosing SARS-CoV-2 infection: the danger of over-reliance on positive test results; false positive test results impact clinical and policy decisions. medRxiv preprint doi: https://doi.org/10.1101/2020.04.26.20080911
21. Cohen AN, Kessel B, Milgroom MG. Analysis of expected false positive rates in SARS-CoV-2 testing: technical background, limitations and objections. https://doi.org/10.5281/zenodo.4035317.
22. Ruiz-Villalba A, et al. Amplification of nonspecific products in quantitative polymerase chain reactions (qPCR) Biomol Detect Quantif. 2017 Dec; 14: 7–18. Published online 2017 Nov 1. doi: 10.1016/j.bdq.2017.10.001
23. Willman D. Contamination at CDC lab delayed rollout of coronavirus tests. April 18, 2020. https://www.washingtonpost.com/investigations/ contamination-at-cdc-lab-delayed-rollout-of-coronavirustests/2020/04/18/fd7d3824-7139-11ea-aa80-c2470c6b2034_story.html. (accessed Aug 16, 2020).
24. Bullard J, et al. Predicting infectious SARS-CoV-2 from diagnostic samples. Clin Infect Dis. 2020 May 22: ciaa638. Published online 2020 May 22. doi: 10.1093/cid/ciaa638
25. Singanayagam A, Patel M, Charlett A, et al. (2020). Duration of infectiousness and correlation with RT-PCR cycle threshold values in cases of COVID-19, England, January to May 2020. Euro surveillance: bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin, 25(32), 2001483. https://doi.org/10.2807/1560-7917.ES.2020.25.32.2001483
26. Jaafar R, Aherfi S, Wurtz N, et al. Correlation Between 3790 Quantitative Polymerase Chain Reaction–Positives Samples and Positive Cell Cultures, Including 1941 Severe Acute Respiratory Syndrome Coronavirus 2 Isolates, Clinical Infectious Diseases, ciaa1491, https://doi.org/10.1093/cid/ciaa1491
27. https://www.sciencemag.org/news/2020/09/one-number-could-help-reveal-how-infectious-covid-19-patient-should-test-results
28. Magleby R, Westblade LF, Trzebucki A, et al. Impact of Severe Acute Respiratory Syndrome Coronavirus 2 Viral Load on Risk of Intubation and Mortality Among Hospitalized Patients with Coronavirus Disease 2019, Clinical Infectious Diseases; ciaa851, https://doi.org/10.1093/cid/ciaa851
29. He X, Lau EHY, Wu P, et al. Temporal dynamics in viral shedding and transmissibility of COVID-19. Nat Med 2020; 26:672-5. Doi:http://dx.doi.org/10.1038/s41591-020-0869-5.
30. Yagci AK, Sarinoglu RC, Bilgin H, et al. Relationship of the cycle threshold of SARS-C0V-2 polymerase chain reaction and total severity score of computerized tomography in patients with COVID-19. International Journal of Infectious Diseases 101 (2020) 160-165.
31. Bryan A, Fink SL, Gattuso MA, et al., SARS-CoV-2 viral load on admission is associated with 30-day mortality. Open Forum Infect Dis. 2020 Dec; 7(12): ofaa535. Published online 2020 Nov 3. doi: 10.1093/ofid/ofaa535
32. Perchetti GA, Nalla AK, Huang ML, et al. Validation of SARS-CoV-2 detection across multiple specimen types. J Clin Virol. 2020; 128:104438. doi: 10.1016/j.jcv.2020.104438
33. Long QX., Liu BZ., Deng HJ, et al. Antibody responses to SARS-CoV-2 in patients with COVID-19. Nat Med 26, 845–848 (2020). https://doi.org/10.1038/s41591-020-0897-1
34. Rhoads D,  Peaper DR,  She RC, et al.  College of American Pathologists (CAP) Microbiology Committee Perspective: Caution must be used in interpreting the Cycle Threshold (Ct) value. Clin Infect Dis. 2020 Aug 12; ciaa1199. doi: 10.1093/cid/ciaa1199. Online ahead of print.
35. World Health Organization. Global surveillance for COVID-19 caused by human infection with COVID-19 virus. Interim guidance. 20 Mar 2020. https://www.who.int/emergencies/diseases/novelcoronavirus-2019/technical-guidance/laboratory-guidance.
36. https://wwwn.cdc.gov/nndss/conditions/coronavirus-disease-2019-covid-19/case-definition/2020/08/05/ (CSTE Criteria)
37. Rennebohm RM.  Analysis of the COVID-19 epidemic: an additional narrative; an alternative response. Pediatrician (St. Petersburg). 2020;11(3):23-40. https://doi.org/10.17816/PED11323-40
38. Rennebohm RM. Has undertreatment of severe COVID illness been widespread? A pediatric rheumatologist’s perspective. Russia Biomedical Research, 2020, Vol 5, No 3, p. 3-13.

Rob Rennebohm, MD

1/7/21

Email: rmrennebohm@gmail.com