The takeaway: Current data for three separate COVID-19 vaccines suggests that the vaccine prevents COVID-19 or lessens the disease severity. No data from top three COVID-19 vaccine candidates indicates that the vaccine makes the disease worse. Phase III clinical trials to conclusively prove the effect of the vaccine will be completed before administration of the vaccine to the general public.

The claim: The vaccine may make COVID-19 much, much worse in many people.

The evidence: A number of protein sequences encoded by SARS-CoV-2 genome are similar to human proteins (1). This similarity led to the hypothesis that a SARS-CoV-2 vaccine could result in more severe disease when exposure occurs after vaccination (1). For previous SARS and MERS, this severe reaction was observed during the animal studies and therefore the vaccines were not pursued. The hypothesis was proposed before SARS-CoV-2 animal study vaccines were published as stated in (1).

Three vaccines are currently in phase III clinical development in the USA, funded by Operation Warp Speed (2). Vaccine approval process involves four stages (3). Phase I is a small scale study in healthy people to make sure the vaccine does no harm. Phase II is a study with more people to test whether the vaccine does what it is supposed to do. Phase III study occurs in 300-3000 people to ensure that the vaccine works as intended in a larger group of people. Phase IV is post-approval monitoring of the vaccine for an adverse events that may happen after the drug is approved. Human study in Phase I clinical trials only occurs after the vaccine has been proven safe in animals first.

Moderna’s mRNA-1273 prevented COVID-19 disease in monkeys (4). Control monkeys' lungs showed signs of pneumonia from COVID-19. Lungs in vaccinated monkeys were normal after exposure to COVID. The virus was not detectable in the monkey's nose after two days for animals vaccinated with 100 ug dose. Phase I clinical trial data from humans is published and included older adults (5).

University of Oxford and AstraZeneca’s AZD1222 (ChAdOx1 nCoV-19) prevented pneumonia in monkeys and did not cause disease enhancement (6). AZD1222 reduced the number of SARS-CoV-2 (virus) in the lung of the monkeys but did not stop the virus from leaving the nose of the monkeys. Early results from the phase I/II clinical trials demonstrate the safety of the vaccine (7). Further research is ongoing to establish safety and efficacy. This includes phase III clinical trials with more participants and one year monitoring of Phase II participants.

Pfizer and BioNTech's BNT162 is several different vaccine candidates which were tested simultaneously to determine the vaccine with the best protection and least number of reactions such as pain at the injection site, fever, etc (8). In phase I/II clinical trails, the reactions to BNT162b1 were mild to moderate and did not last long (9). Animal studies are presented as a pre-print (10). From the pre-print, it is unclear whether the vaccine prevented lung damage because both vaccine and control group had no lung damage. In other rhesus macaque COVID infections with no vaccine, lung damage was observed (4, 6). BNT162b2 COVID vaccine resulted in no detectable COVID virus after the first day of challenge in monkeys (10).

Sources:

1) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7142689/

2) https://www.raps.org/news-and-articles/news-articles/2020/3/covid-19-vaccine-tracker

3) https://www.fda.gov/patients/drug-development-process/step-3-clinical-research

4) https://pubmed.ncbi.nlm.nih.gov/32722908/

5) https://www.nejm.org/doi/full/10.1056/NEJMoa2028436?query=featured_home

6) https://pubmed.ncbi.nlm.nih.gov/32731258/

7) https://pubmed.ncbi.nlm.nih.gov/32702298/

8) https://www.nejm.org/doi/full/10.1056/NEJMoa2027906?query=featured_home#

9) https://pubmed.ncbi.nlm.nih.gov/32785213/

10) https://www.biorxiv.org/content/10.1101/2020.09.08.280818v1.full.pdf

Take away:

If correct numerators and denominators are used, COVID-19 is at least 10 times as deadly as seasonal influenza.

The claim:

The Infection Fatality Ratio for COVID-19 is “approximately 2.5 times higher than the estimated IFR for seasonal influenza.”

The evidence:

Blackburn et al. report an infection fatality ratio among community-living adults of 0.26% (1). If institutionalized adults had been included the ratio would be higher, likely approximating the 0.6% mortality rate among exposed individuals readily calculated by combining official death tolls, the known 30% undercount (2), and a definitive CDC study that found 10 times as many people have been exposed to the novel coronavirus than are reported as cases (3). Among the elderly, Blackburn et al. calculate COVID-19 is 2.5 times deadlier than seasonal flu. This is clearly an underestimate:

1) Blackburn et al. use CDC estimates of case-fatality rates calculated on the basis of all Americans, including the institutionalized, not limited to much healthier community-dwellers.

2) The seasonal influenza case fatality rates reported by the CDC, including the often cited 0.1% overall, are for symptomatic cases. Their denominators are estimated by using the reported number of influenza hospitalizations to guess the burden of clinical illness (4). But antibody studies show that 65%-85% of people infected with influenza never develop symptoms (5). The 0.6% mortality rate calculated here for SARS-CoV-2-exposed individuals is 6 times higher than the 0.1% usually cited for seasonal influenza. Given the overestimation of commonly accepted influenza mortality rates due to failure to take asymptomatic infections into account, SARS-CoV-2 can be seen to be not 2.5 times, or even 6 times, but at least 10 times as lethal as seasonal flu.

Sources:

1 http://www.acpjournals.org/doi/10.7326/M20-5352

2 https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2767980

3 https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2768834

4 https://www.cdc.gov/flu/about/burden/how-cdc-estimates.htm

5 https://pubmed.ncbi.nlm.nih.gov/26133025/

Take away: COVID-19 death rate is worse than seasonal influenza death rate.

The claim: Coronavirus mortality was over estimated as 10X worse than seasonal influenza to congress due to misclassifying influenza infection fatality rate as a case fatality rate.

The evidence: Comparing infection fatality ratio (IFR) and case fatality ratio (CFR) is an apples to oranges comparison (1). Case fatality ratios present higher death percentages than infection fatality ratios. At the same time, it is important to understand that COVID-19 and seasonal influenza CFR and IFR numbers are rough approximations of reality and the potential for errors exist in all calculations.

The seasonal IFR rate of influenza was overstated in this article. The claim that seasonal influenza IFR and COVID-19 IFR are the same is based on seasonal influenza IFR of 0.1%. Per the WHO report, seasonal influenza “is usually well below 0.1%” (2). This statement was translated into “0.1% or lower” and then “the WHO also reported that 0.1% is the IFR of seasonal influenza, not the CFR of seasonal influenza as reported in the NEJM editorial” (3).

The article is questioning whether COVID-19 is worse than seasonal influenza due to confusion with IFR and CFR. The article overstated influenza IFR to arrive at the conclusion that COVID-19 and seasonal influenza death rates are the same.

Comparison of influenza and COVID-19 deaths:

Influenza CFR = 0.1-0.2%

(Based on CDC data # deaths / # symptomatic cases, 4).

COVID-19 CFR = 2.8%

(In the USA as of 10/6/2020. Includes asymptomatic cases and may therefore be an underestimate of true CFR, 5-6)

It is also important to note that COVID-19 disease is ongoing with the potential for some of the 7,461,206 cases to die from COVID-19 later. Only 2,935,142 cases in the US are reported as recovered as of 10/6/2020.

Even with the inclusion of asymptomatic cases in the death rate calculation for COVID-19, deaths/cases is at least 10X higher than the deaths/cases calculation of symptomatic influenza based on CDC data.

Sources:

1 https://pubmed.ncbi.nlm.nih.gov/32234121/

2 https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200306-sitrep-46-covid-19.pdf?sfvrsn%3d96b04adf_4

3 https://www.cambridge.org/core/journals/disaster-medicine-and-public-health-preparedness/article/public-health-lessons-learned-from-biases-in-coronavirus-mortality-overestimation/7ACD87D8FD2237285EB667BB28DCC6E9/core-reader

4 https://www.cdc.gov/flu/about/burden/index.html#:~:text=While%20the%20impact%20of%20flu,61%2C000%20deaths%20annually%20since%202010

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

6 https://coronavirus.jhu.edu/map.html

Take away: COVID-19 has a higher case fatality rate than seasonal flu but a lower case fatality rate than SARS and MERS.

The claim: Dr. Anthony Fauci is lying when he states COVID-19 is ten times worse than the seasonal flu.

The evidence: From 2010 to 2018, 0.1-0.2% of seasonal flu cases resulted in death (1). To date, the number of coronavirus deaths in the United States is 206,615 deaths per 7,216,828 cases (2, accessed 9/30/2020) which is a death rate of 2.9%. Therefore, the death rate of coronavirus is higher than the death rate of the seasonal flu. Similarities and differences between COVID-19 and seasonal flu are explained by John Hopkins Medicine and CDC (3-4).

COVID-19 is related to SARS, MERS, and "common cold" coronaviruses. The fatality rate of SARS (9.5%) and MERS (34.4%) is higher than COVID-19 (2.3%) (5).

Sources:

1 https://www.cdc.gov/flu/about/burden/index.html#:~:text=While%20the%20impact%20of%20flu,61%2C000%20deaths%20annually%20since%202010.

2 https://coronavirus.jhu.edu/map.html

3 https://www.hopkinsmedicine.org/health/conditions-and-diseases/coronavirus/coronavirus-disease-2019-vs-the-flu

4 https://www.cdc.gov/flu/symptoms/flu-vs-covid19.htm#:~:text=Because%20some%20of%20the%20symptoms,differences%20between%20the%20two.

5 https://pubmed.ncbi.nlm.nih.gov/32234451/

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

Take away: Though chloroquine and hydroxychloroquine showed some effects against SARS-CoV-2 in vitro for vero cells, the FDA removed emergency use authorization for COVID-19 patients due to increased heart problems. No in vitro effect was seen when using human lung cells instead of monkey cells. Many clinical trials are ongoing.

The claim: Hydroxychloroquine is a relatively cheap and available medication with positive results in patients who have taken the drug.

The evidence: Chloroquine and hydroxychloroquine inhibited infection of vero E6 cells (African green monkey kidney cell line) by SARS-CoV-2 (1, 2). These drugs did not inhibit SARS-CoV-2 infection in Calu-3 cells (human lung cell line, 3). Several clinical trials have reported positive outcomes with the use of hydroxychloroquine/chloroquine (4, 5). Current evidence is reviewed in (6). Known side effects including cardiovascular, neuropsychiatric, and gastrointestinal exist based on use of hydroxychloroquine and chloroquine in treating malaria and autoimmune conditions (7). These side effects may more severely affect COVID-19 patients due to the average age and comorbidities often present in severe COVID-19 cases and similarity to COVID-19 symptoms. A randomized, double blind placebo-controlled trial did not observe a significant difference between treatment and control groups when hydroxychloroquine was used prophylactically (8). Increased cardiovascular mortality, chest pain/angina, and heart failure occurred when hydroxychloroquine was combined with azithromycin (9). The FDA removed emergency use authorization in June (10). Many clinical trials are currently ongoing (11).

Disclaimer: This content is not intended as a substitute for professional medical advice. Always seek the advice of a qualified health provider with any questions regarding a medical condition.

Sources:

1 https://www.nature.com/articles/s41422-020-0282-0

2 https://academic.oup.com/cid/article/71/15/732/5801998

3 https://www.nature.com/articles/s41586-020-2575-3

4 https://www.jstage.jst.go.jp/article/bst/14/1/14_2020.01047/_pdf/-char/en

5 https://www.sciencedirect.com/science/article/pii/S0924857920300996?via%3Dihub

6 https://pmj.bmj.com/content/96/1139/550.long

7 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7228887/

8 https://pubmed.ncbi.nlm.nih.gov/32492293/

9 https://www.medrxiv.org/content/medrxiv/early/2020/05/31/2020.04.08.20054551.full.pdf

10 https://www.fda.gov/drugs/drug-safety-and-availability/fda-cautions-against-use-hydroxychloroquine-or-chloroquine-covid-19-outside-hospital-setting-or

11 https://clinicaltrials.gov/ct2/results?cond=Covid19&term=hydroxychloroquine&cntry=&state=&city=&dist=

Take away: The claim here is ultimately a value judgement, but the data used to support the claim is subject to examination. Overall the source for the presented data was not identifiable and the overall argument requires greater context to evaluate.

The claim: Students are harmed more by not being in school/sports than by COVID-19.

The evidence: The claim is supported by a single data table. Unfortunately, the source for the table presented was not found. It is well documented that the infection fatality rate in age groups 15-24 is lower than for older age groups, however the actual infection fatality rate for this age group is still not entirely clear. As one example, the CDC website has two data sets to estimate COVID-19 deaths. One is provisional COVID-19 deaths with 242 deaths reported in the age group 15-24 in the entire United States. The second dataset estimates COVID-19 deaths based on the increase over the number of expected deaths based on historical data. The second dataset does not present data for 15-24 age group, only "under 25 years." The lower range of the second dataset is ~10,000 deaths in people under 25 years in Florida above the expected deaths for the year. These deaths may be attributed to COVID-19.

Regardless, no data is presented which measures how students are harmed by not being in school. Additional data needs to be presented for the claim to be validated including measurable metrics by which students are harmed. These are not presented in the Twitter post.

Sources: https://www.cdc.gov/nchs/nvss/vsrr/covid19/excess_deaths.htm#dashboard https://data.cdc.gov/NCHS/Provisional-COVID-19-Death-Counts-by-Sex-Age-and-S/9bhg-hcku/data https://experience.arcgis.com/experience/96dd742462124fa0b38ddedb9b25e429 https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/older-adults.html#:~:text=As%20you%20get%20older%2C%20your,than%20people%20in%20their%2050s.

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.

Sources:

(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/

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.

The takeaway: Reported numbers of asymptomatic individuals are discordant but generally are less than 20% of reported cases.

The claim: Half of people infected with COVID-19 are asymptomatic.

The evidence: 17.9% of the Diamond Princess ship were asymptomatic (1). Only 48 out of 473 total cases were from asymptomatic individuals in Iceland (2). The initial analysis of China's asymptomatic cases was 1% (3). A research article summarizing data from China and Italy lists China's asymptomatic cases as 80.9% and Italy's asymptomatic cases as 8.5% (4). It appears that mild symptoms and asymptomatic cases were combined in reference 4 for China's data as mild symptom numbers were N/A.

Therefore, there is no consensus on the number of asymptomatic individuals. Additional clarity is needed in the data before conclusions can be made based on the number of asymptomatic individuals.

Sources:

1 https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.10.2000180

2 https://www.government.is/news/article/2020/03/15/Large-scale-testing-of-general-population-in-Iceland-underway/

3 https://jamanetwork.com/journals/jama/fullarticle/2762130

4 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7278221/

Take away: The COVID-19 virus (SARS-CoV2) did not exist in 2012, however a related virus was isolated from bats in 2013.

The claim: The same virus that is causing the COVID-19 pandemic existed in miners in 2012.

The evidence:RaTG13, a virus that was isolated from bats by the Wuhan Institute of Virology in 2013 is the closest known relative to SARS-CoV2, the virus that causes COVID-19 (Ge 2016, Zhou 2020). This bat virus is not the same virus as SARS-CoV2, but is closely related (96% identical DNA). The virus was isolated from bats, not humans. However, it was isolated from a cave near where workers the previous year became sick and some died, and may be linked to the illnesses. The SARS-CoV2 virus shows a number of key adaptations that likely makes it much more infectious in humans than the related bat virus (Wrobel, 2020).

Source:

Ge XY, Wang N, Zhang W, Hu B, Li B, Zhang YZ, Zhou JH, Luo CM, Yang XL, Wu LJ, Wang B. Coexistence of multiple coronaviruses in several bat colonies in an abandoned mineshaft. Virologica Sinica. 2016 Feb 1;31(1):31-40.

Zhou P, Yang XL, Wang XG, Hu B, Zhang L, Zhang W, Si HR, Zhu Y, Li B, Huang CL, Chen HD. A pneumonia outbreak associated with a new coronavirus of probable bat origin. nature. 2020 Mar;579(7798):270-3.

Wrobel AG, Benton DJ, Xu P, Roustan C, Martin SR, Rosenthal PB, Skehel JJ, Gamblin SJ. SARS-CoV-2 and bat RaTG13 spike glycoprotein structures inform on virus evolution and furin-cleavage effects. Nature Structural & Molecular Biology. 2020 Aug;27(8):763-7.

Take away: In their 2019 report the WHO actually recommended for, not against, the use of masks in severe influenza epidemics or pandemics, contrasting the statement made in this tweet. Further, recent evidence overwhelmingly supports the benefit of masks for preventing the spread of SARS-CoV2, the virus that causes COVID-19.

The claim: Overall the claim here appears to be that masks are ineffective against the spread of SARS-CoV2, the virus that causes the clinical syndrome known as COVID-19. The evidence used in support of this claim is that “the WHO found ‘moderate’ evidence AGAINST using masks” in their 2019 report on the use of non-pharmaceutical interventions for mitigating influenza pandemics.

The evidence: This overall claim is poorly supported by data and the evidence used to support this claim is incorrectly characterized by the claimant. Narrowly, the claim that the WHO recommended against mask use is patently false. In their report, the WHO reviewed 10 separate studies and did conclude that there was scant evidence that masks significantly decreased spread of the flu. However, they found no evidence that masks increased spread, and based on mechanistic plausibility (i.e. masks are barriers that prevent droplets from passing between people) and the low risk/high reward, they made a conditional recommendation for mask use in severe influenza epidemics or pandemics.

While influenza does not behave exactly like the SARS-CoV2 virus, the similarities in mode of transmission make it reasonably likely that masks would also have protective effects against the spread of this virus is well. The best evidence is hard data, and that too increasingly points to the benefit of masks for slowing down or preventing the transmission of SARS-CoV2. A recent summary of that data is available here.

Take away: Real-world evidence from countries like New Zealand, that already have very low disease incidence, suggests that the concerns for false positives raised in this article are overhyped.

The claim: "When virus levels in the population are very low, the chances of a test accurately detecting Covid-19 could be even less than 50 per cent..."

The evidence: The author explains theoretical scenarios where, when rates of true COVID infections are low, the rate of true positives (test positive and have COVID) may be equal to or less than false positives (test positive but do not have COVID). The background here is that no test is perfect and every screening test used in medicine has some percentage of false negatives and false positives. Several anecdotes are cited in support, however real world data from countries that already have very low disease incidence, suggests that the concerns or false positives raised in this article are unfounded. New Zealand, for example, has tested an average of 2127 people per day from July 1-22, with an average of 1.2 positive cases identified per day—an average % positive of only 0.07%. In order for the authors assumptions to hold, all of the positive tests reported there would have to be false positives—highly unlikely as New Zealand still has symptomatic patients. Therefore, real-world evidence from standard PCR based COVID testing in low incidence populations suggests that the concern for high rates of false positives raised in this article is overhyped.

Source: https://www.health.govt.nz/our-work/diseases-and-conditions/covid-19-novel-coronavirus/covid-19-current-situation/covid-19-current-cases

This is an oversimplification that dismisses research findings on particularly vulnerable activities. For instance, superspreading events have been linked to certain activities, as detailed here. For instance, a study of 110 case-patients from 11 clusters in Japan, linked all clusters to closed environments, including fitness centers, shared eating environments, and hospitals. The authors found an 18.7x risk of transmission in closed environments than open environments. Therefore, it is inaccurate to say "there are no activities that we can point to as extremely vulnerable."