5 Matching Annotations
  1. Oct 2020
    1. A scientific review of the science behind lockdown concludes the policy was a MISTAKE & will have caused MORE deaths from Covid-19

      Take Away: The new scientific paper confirms earlier modeling work and should not be interpreted as a detailed prediction for future deaths due to the ongoing pandemic.

      The Claim: "A scientific review of the science behind lockdown concludes the policy was a MISTAKE & will have caused MORE deaths from Covid-19"

      The Evidence: The scientific process involves replication and confirmation of experiments and studies. A new paper replicates and expands on an early modeling study of the COVID-19 pandemic in England (1). Their findings support the earlier results. However, there are limitations to the replication paper, which does not accurately reflect the current state of the pandemic response and does not make detailed predictions for a second wave of infections and deaths.

      A recent expert response to the paper further explains (2):

      "It needs to be stressed that all the simulations assume that interventions are only in place for 3 months (18th April – 18th July) and then completely relaxed. This gives rise to a strange set of scenarios where a second wave is allowed to progress in an uncontrolled manner."

      “It is this that leads to the counter-intuitive headline finding “that school closures would result in more overall covid-19 deaths than no school closures” – actually what the authors find is that a short period of intense lock-down (including the closure of schools) leads to a large second wave if it is allowed to run with no controls. To be fair the authors do highlight this in the paper, but it is not in the reported press release." -Prof Matt Keeling, Professor of Populations and Disease, University of Warwick


      (1) https://www.bmj.com/content/371/bmj.m3588

      (2) https://www.sciencemediacentre.org/expert-reaction-to-reanalysis-of-model-used-for-imperial-report-9-and-impact-of-school-closures/

  2. Sep 2020
    1. The lowest value for false positive rate was 0.8%. Allow me to explain the impact of a false positive rate of 0.8% on Pillar 2. We return to our 10,000 people who’ve volunteered to get tested, and the expected ten with virus (0.1% prevalence or 1:1000) have been identified by the PCR test. But now we’ve to calculate how many false positives are to accompanying them. The shocking answer is 80. 80 is 0.8% of 10,000. That’s how many false positives you’d get every time you were to use a Pillar 2 test on a group of that size.

      Take Away: The exact frequency of false positive test results for COVID-19 is unknown. Real world data on COVID-19 testing suggests that rigorous testing regimes likely produce fewer than 1 in 10,000 (<0.01%) false positives, orders of magnitude below the frequency proposed here.

      The Claim: The reported numbers for new COVID-19 cases are overblown due to a false positive rate of 0.8%

      The Evidence: In this opinion article, the author correctly conveys the concern that for large testing strategies, case rates could become inflated if there is (a) a high false positive rate for the test and (b) there is a very low prevalence of the virus within the population. The false positive rate proposed by the author is 0.8%, based on the "lowest value" for similar tests given by a briefing to the UK's Scientific Advisory Group for Emergencies (1).

      In fact, the briefing states that, based on another analysis, among false positive rates for 43 external quality assessments, the interquartile range for false positive rate was 0.8-4.0%. The actual lowest value for false positive rate from this study was 0% (2).

      An upper limit for false positive rate can also be estimated from the number of tests conducted per confirmed COVID-19 case. In countries with low infection rates that have conducted widespread testing, such as Vietnam and New Zealand, at multiple periods throughout the pandemic they have achieved over 10,000 tests per positive case (3). Even if every single positive was false, the false positive rate would be below 0.01%.

      The prevalence of the virus within a population being tested can affect the positive predictive value of a test, which is the likelihood that a positive result is due to a true infection. The author here assumes the current prevalence of COVID-19 in the UK is 1 in 1,000 and the expected rate of positive results is 0.1%. Data from the University of Oxford and the Global Change Data Lab show that the current (Sept. 22, 2020) share of daily COVID-19 tests that are positive in the UK is around 1.7% (4). Therefore, based on real world data, the probability that a patient is positive for the test and does have the disease is 99.4%.

      Sources: (1) https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/895843/S0519_Impact_of_false_positives_and_negatives.pdf

      (2) https://www.medrxiv.org/content/10.1101/2020.04.26.20080911v3.full.pdf+html

      (3) https://ourworldindata.org/coronavirus-data-explorer?yScale=log&zoomToSelection=true&country=USA~DEU~IND~ITA~AUS~VNM~FIN~NZL~GBR&region=World&testsPerCaseMetric=true&interval=smoothed&aligned=true&smoothing=7&pickerMetric=location&pickerSort=asc

      (4) https://ourworldindata.org/coronavirus-data-explorer?zoomToSelection=true&country=USA~DEU~IND~ITA~AUS~VNM~FIN~NZL~GBR&region=World&positiveTestRate=true&interval=smoothed&aligned=true&smoothing=7&pickerMetric=location&pickerSort=asc

    1. He added that while it would not be possible to check every test to see whether there was active virus, the likelihood of false positive results could be reduced if scientists could work out where the cut-off point should be.

      Take Away: This is an incorrect usage of the term "false positive." A positive PCR test result from a recovered infection is a valid and true positive.

      Claim: PCR tests for SARS-CoV-2 give false positive results when there is no active virus.

      Evidence: The diagnostic PCR tests currently in widespread use are designed to detect the presence of the SARS-CoV-2 viral RNA in a clinical sample. The RNA is only a part of the complete virus and is not infectious on its own. Research has shown that viral RNA can be detected in some samples up to 12 weeks after onset of symptoms (1). In other words, this is like testing if an oven is warmer than the room temperature - it could be hot even after it has been turned off.

      By definition, in the context of SARS-CoV-2 PCR tests, a "false positive" means that a test result is deemed positive when in reality there was no viral RNA in the sample. If a person is recovering from an infection, gets tested, and then is given a positive test result, that is a true positive regardless of whether they are infectious or not.

      Sources: 1) https://www.cdc.gov/coronavirus/2019-ncov/hcp/duration-isolation.html

    1. Your Coronavirus Test Is Positive. Maybe It Shouldn’t Be.

      Take Away: Diagnostic tests are most useful when they are both sensitive and rapid. The sensitivity of SARS-CoV-2 PCR tests is not the issue, but rather the time it takes to get a result. Additionally, the "90%" statistic is likely misleading due to the data source and not generalisable to all testing results.

      The Claim: The usual PCR diagnostic tests may be too sensitive and too slow, with up to 90% of positive cases due to trace amounts of virus.

      The Evidence: Polymerase Chain Reaction (PCR)-based tests, which are currently in the most widespread use for detection of SARS-CoV-2 RNA, involves a molecular process that amplifies target DNA sequences in repeated temperature-dependent cycles. The amount of target DNA is measured after each cycle and the number of the cycle when the target can be reliably detected is often referred to as the cycle threshold (Ct). The Ct value is proportional to the amount of starting DNA in the sample and can be used to estimate the viral load of a patient. In some ways this is like a teacher making photocopies of a chapter from a textbook until they have enough for all their students.

      However, Ct values are relative measurements and need to be directly compared to controls for every sample - a Ct value taken alone can be meaningless. For instance, consider an infected patient who is tested twice: the first time they are gently swabbed and the sample is relatively dilute, the second time they are vigorously swabbed and the sample is relatively concentrated. The resulting Ct values could be drastically different. Therefore, Ct values need to be considered carefully in the proper context for making medical or policy decisions. The FDA also recommends that a PCR result alone should not be used to determine infection status.

      Positive results are indicative of the presence of SARS-CoV-2 RNA; clinical correlation with patient history and other diagnostic information is necessary to determine patient infection status. (1)

      Current PCR test results are generally given as a binary positive/negative based on a cutoff value for Ct. The cutoff needs to be determined based on the performance of each individually developed SARS-CoV-2 test, of which there are currently over 160 that have been granted emergency use authorization by the FDA (2). Based on unpublished data from the CDC, setting a stringent Ct cutoff of 30 could return negative results in patients who are both infected and potentially infectious (3 Fig 5). Furthermore, a 30 cycle cutoff would return invalid results for samples which are too diluted. Based on the same CDC data, up to 30% of potentially infectious patients would get invalid results and need to be re-swabbed, thereby extending the time between getting infected and getting a positive result.

      The period of time when RNA from SARS-CoV-2 can be detected (and a positive PCR test result returned) may extend up to 12 weeks after recovery, with Ct values trending higher over time (3,4). According to The New York Times article, they looked at Ct values from people who tested positive in Massachusetts in July and found 85-90% of results had Ct values greater than 30. The epidemiology of COVID-19 is highly time and region dependent. Massachusetts had a peak in COVID-19 hospitalizations on April 21 (5), which is 9-12 weeks prior to the testing data analyzed by The NY Times. Therefore, the detection of a large proportion of people with lingering viral RNA is not surprising. These results are likely not universal and can not be applied to other regions, especially where community spread is still significant.


      (1) https://www.fda.gov/media/135900/download

      (2) https://www.fda.gov/medical-devices/coronavirus-disease-2019-covid-19-emergency-use-authorizations-medical-devices/vitro-diagnostics-euas

      (3) https://www.cdc.gov/coronavirus/2019-ncov/hcp/duration-isolation.html

      (4) Li N, Wang X, Lv T. Prolonged SARS-CoV-2 RNA Shedding: Not a Rare Phenomenon. J Med Virol 2020 Apr 29. doi: 10.1002/jmv.25952.

      (5) https://www.bostonherald.com/2020/05/22/massachusetts-finally-seeing-downward-coronavirus-trends/

    1. If we are now going to hold our nation hostage because of this obsession over PCR (polymerase chain reaction) swab tests, we should at the very least make certain they’re accurate. What happens when we have expedited and chaotic test results driving an epidemic curve rather than actual symptoms? You get what happened to Ohio Governor Mike DeWine last Thursday. He tested positive for the virus after experiencing absolutely no symptoms. But because he is such a VIP, he got a second, more accurate test that showed he was in fact negative for SARS-CoV-2. The same thing happened to Detroit Lions quarterback Matthew Stafford, who tested negative after receiving a false positive and was therefore allowed out of coronavirus prison.

      Take away: Current polymerase chain reaction (PCR) testing technology is very sensitive and specific. Even for rapidly developed new tests for the novel coronavirus, SARS-CoV-2, available clinical data indicates they are highly accurate.

      The claim: SARS-CoV-2 testing is unreliable and plagued by false positive results.

      The evidence: Any diagnostic test has some degree of error that is typically very low for FDA approved products. For SARS-CoV-2 tests, which detect the presence of the virus that causes COVID-19, although rare, it is possible to get a positive result when you may not have been exposed or infected by the virus. In other words, a false positive. So how frequently do false positives occur?

      There is no universal false positive rate for SARS-CoV-2 test results because there are dozens of different tests that have been developed and deployed, each with their own error rate. As of August 26, 2020, there are 146 commercial diagnostic tests that have received emergency use authorization from the FDA. Data from clinical performance testing submitted to the FDA indicates that PCR tests are highly accurate. For example, the specific PCR test mentioned by the author, Quest Diagnostics SARS-CoV-2 rRT-PCR, obtained 100% correct results in clinical evaluation studies (n = 60), and 100% true negative results in a random population of samples from before the pandemic (n = 72).

      Additional considerations: In addition to PCR technology-based tests, which detect the viral RNA genome and require lab processing, there are antigen tests, which use antibodies to detect viral proteins and can be rapidly performed in point-of-care settings. Antigen tests are much easier to perform than PCR tests, but they can be less sensitive. For example, the LumiraDx SARS-CoV-2 Ag Test, when compared to PCR, has an overall agreement of 96.9%.

      The author provides two anecdotes of high-profile personnel who obtained false positive test results. For the Ohio Governor, his initial positive was from an antigen test, not a PCR test. The NFL quarterback is part of a unique population that is presumed to be largely SARS-CoV-2 negative but is being tested frequently and repeatedly. This scenario increases the probability that a positive test result may be false. However, the NFL in early August said it has conducted over 75,000 tests, so unless there are many additional cases of false positives, this suggests that their testing methodology is over 99.99% accurate.