The takeaway: >99% of people age 0-69 infected with SARS-CoV-2 survive COVID based on the CDC's current best estimate of infection fatality ratio. A subset of those infected will suffer from continued symptoms even though they did not die from COVID.

The claim: Greater than 99% of people age 0-69 fully recover from COVID-19.

The evidence: This numbers align with the CDC's current best estimate of the infection fatality ratio (1). Infection fatality ratio is the number of people that die from a disease divided by the number of people who get the disease. These numbers do not account for people with symptoms such as lung damage, chronic fatigue, and mental illness which may follow a COVID infection (2, 3).

In a study of 143 hospitalized patients from Italy after an average of 60.3 days, only 12.6% were symptom free (4). Per Mayo Clinic guidelines, long term effects can occur in those with mild symptoms but most often occur in severe cases (5). Mental health problems were diagnosed 14-90 days after COVID in 18.1% of COVID patients studied (3).

A more accurate estimate of the number of people that fully recover may be obtained if the number of people who recovered without hospitalization is used. The numbers presented are the CDC's current best estimate of the number of people that survive COVID not the number of people that fully recover.

Sources:

1) https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html

2) https://www.nature.com/articles/d41586-020-02598-6

3) https://www.thelancet.com/journals/lanpsy/article/PIIS2215-0366(20)30462-4/fulltext

4) https://jamanetwork.com/journals/jama/fullarticle/2768351/

5) https://www.mayoclinic.org/diseases-conditions/coronavirus/in-depth/coronavirus-long-term-effects/art-20490351

The takeaway: While minimal protection occurs when a mask is worn in a place where many others are not wearing a mask, community masking is associated with a reduction in COVID cases.

The claim: In a community with modest infection rates, some social distancing, and most people not wearing masks, wearing a surgical mask did not reduce the SARS-CoV-2 infection rate by more than 50%.

The evidence: This study showed that wearing a mask in a community where most people did not wear a mask, did not reduce the risk of getting infected by 50%. Fewer COVID infections were reported in the mask group than in the unmasked group. This study agrees with a meta analysis which showed that masks resulted in a decrease in infections but did not prevent all infections (1) According to the CDC, seven studies have shown community level benefit when masking recommendations were made (2).

When most in the community are not wearing masks, social distancing, and washing hands, wearing a mask alone provides minimal protection to the mask wearer. Community wide masking is associated with a reduction in COVID cases (2).

Sources:

1) https://pubmed.ncbi.nlm.nih.gov/29140516/

2) https://www.cdc.gov/coronavirus/2019-ncov/more/masking-science-sars-cov2.html

Take away: Lower COVID-19 spread occurred after stay-at home orders were issued. Room for debate exists on how restrictive lockdowns should be.

The claim: Mandatory stay-at home orders are "useless" in helping lower the spread of SARS-CoV-2.

The evidence: Two publications showed that lower COVID-19 spread occurred after stay-at home orders were issued (1, 2). Hospitalizations were lower than predicted exponential growth rates after implementation of stay-at home orders (3). Some caveats to consider include that it is impossible to tease apart the effects of the stay-at home orders from other measure implemented simultaneously with stay-at home orders such as increased hygiene measures, social distancing guidelines, and school closures. It is also impossible to conclusively state that the effect is from the stay-at home order and not the natural progression of the disease.

The comparison between Illinois with stay-at home orders and Iowa without stay-at home orders resulted in an estimated 217 additional COVID-19 cases in Iowa over the course of a month (2). This small number raises the question, "are stay-at home orders worth it?" It is important to remember that comparison of Iowa and Illinois is the comparison of two social distancing strategies. Stay-at home orders close everything and then write the exceptions that can remain open. Iowa took the approach of leaving everything open except what the government choose to close (4). Some businesses in Iowa were still closed and many federal guidelines were still followed. A negative control showing disease progression without any mitigation measures does not exist in published literature.

Sources:

1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7246016/

2 https://pubmed.ncbi.nlm.nih.gov/32413112/

3 https://www.desmoinesregister.com/story/news/2020/04/07/iowa-equivalent-stay-at-home-order-coronavirus-kim-reynolds/2961810001/

4 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7254451/

The takeaway: Even though mutations happen in all virus, vaccines still work. Current evidence about SARS-CoV-2 indicates that an effective COVID-19 vaccine can be obtained, and that it should be able to provide immunity against the virus.

The claim: A better analogy for COVID-19 is the mumps. For more than 45 years, we’ve had a very effective vaccine for measles, mumps, and rubella (which are also RNA viruses).

The evidence: We are all imperfect and we all make mistakes. For a virus, a mistake means the introduction of a mutation in its sequence, and RNA viruses (like the flu, mumps, measles virus, and SARS-CoV-2) have the highest error rates in nature. Mutations are indispensable for viral survival and evolution; this property is believed to benefit the viral population, allowing it to adapt and respond to different complex environments encountered during spread between hosts, within organs and tissues, and in response to the pressure of the host immune response [1]. How fast a virus is changing can be estimated by measuring its mutation rate, and then they can be classified as changing fast – high mutation rate – like HIV or Influenza, or as stable, like measles or mumps virus. SARS-CoV-2 has a mutation rate three times slower than the flu virus [2], but it's still changing faster than the mumps virus (the mutation rate of influenza is more than 10 times higher than mumps) [3]. Of course, how fast a virus can change has implications in the efficacy of treatments and vaccines, but it's not the only determinant. Even though mutations happen in all viruses, vaccines still work. A great example is the measles virus, as the antigenic composition of the vaccine (the molecules that “wake up” the immune system) used to prevent it has remained efficient since it was developed, in the 1960s, and confers protection against the 24 circulating genotypes [4]. The same is true for the mumps virus, with a vaccine that has been efficient for many decades [5]. Sequencing data suggest that coronaviruses change more slowly than most other RNA viruses, probably because of a viral ‘proofreading’ activity that corrects all the copying mistakes [6]. Taken together, all this evidence indicates that an effective COVID-19 vaccine can be obtained, and that it should be able to provide lasting immunity against the virus.

Sources:<br> 1

2 SARS-CoV-2 mutation rate: 1.26 x 10-3 substitutions/site/year

3 Influenza (flu-virus) mutation rate: 3.68 x 10-3 substitutions/site/year. Mumps mutation rate: 2.98 × 10−4 substitutions/site/year

4

5

6

Take away: Though the number of COVID deaths prevented and the exact number of years lost due directly to decreases in mental health from lockdowns is at best a rough estimate, several facts are known. Lockdowns decrease mental health, and a decrease in mental health shortens lives too.

The claim: Anxiety from reactions to COVID-19 will destroy at least seven times more years of life than can be saved by lockdowns.

The evidence: This article references many studies detailing the anxiety surrounding COVID-19 (1-4). These studies indicate that many people have increased stress due to COVID. Nature Public Health Emergency Collection reports that the mental health cost of widespread lockdowns may negate the lives saved by this policy (5). This article lists many articles which describe the effect of stay-at-home orders on mental health. Additionally, the effect of poor mental health on physical outcomes is well-defined. Poor mental health shortens lives. Other factors with COVID such as negative media coverage and dealing with job loss and death are also described as negatively affecting mental health. It is unclear how much of the negative mental health outcomes is directly related to lockdowns and what is contributed to the disease, job loss, future uncertainty, and continuous media coverage.

Several supporting facts used in this article are now outdated or could use clarification. Many assumptions are detailed in this article to estimate the number of years lost due to mental harm caused by lockdowns. One example is the authors used a survey of 1,266 patients to estimate the number of people in the United States who have suffered mental harm from lockdowns. These estimates are challenging to conclusively verify. The authors did choose the conservative estimate for each of their numbers. One example of an outdated number is the predicted number of deaths was 114,228 by August 4th. The actual number of deaths per Johns Hopkins was 157,500 (6).

Based on the facts, anxiety and mental disorders can be deadly. Lockdowns result in an increase in poor mental health. The exact number of years lost due to poor mental health directly resulting from lockdowns is less clear. Poor mental health may also result from constant media coverage, loss of loved ones and fear of the future.

The sources:

1) https://www.psychiatry.org/newsroom/news-releases/new-poll-covid-19-impacting-mental-well-being-americans-feeling-anxious-especially-for-loved-ones-older-adults-are-less-anxious

2) https://www.kff.org/health-reform/report/kff-health-tracking-poll-early-april-2020/

3) https://www.bsgco.com/post/coronavirus-and-americans-mental-health-insights-from-bsg-s-pulse-of-america-poll

4) https://www.kff.org/report-section/kff-health-tracking-poll-late-april-2020-economic-and-mental-health-impacts-of-coronavirus/

5) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7431738/#

6) https://coronavirus.jhu.edu/us-map

The takeaway: Though COVID-19 cases are at a record high, the number of deaths from COVID-19 has not followed the steep rise in cases. An increase in the number of deaths may be reported later as deaths lag cases by several weeks.

The claim: On every measure - new infections, hospitalizations, and deaths - the U.S. is headed in the wrong direction.

The evidence: New COVID infections in the US are the highest they have ever been with a 7-day moving average of 104,417 cases/day (1). The number of deaths in the US is similar to the number of deaths in August, lower than the number of deaths in the spring and higher than the number of deaths in the summer (2). A slight increase was seen in the number of deaths for the first two weeks in October followed by a slight decline which may change as more data is added (3). The number of emergency department visits for coronavirus like symptoms is on an upward trajectory nationwide (4). The CDC states "At least one indicator used to monitor COVID-19 activity is increasing in each of the ten HHS regions, and many regions are reporting increases in multiple indicators" (3).

Though COVID-19 cases are at a record high, the number of deaths from COVID-19 has not followed the steep rise in cases. An increase in the number of deaths may be reported later as deaths lag cases by several weeks.

Sources:

1) https://covid.cdc.gov/covid-data-tracker/#trends_dailytrendscases

2) https://covid.cdc.gov/covid-data-tracker/#trends_dailytrendsdeaths

3) https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html

4) https://covid.cdc.gov/covid-data-tracker/#ed-visits

The takeaway: Dimeric lipopeptide fusion inhibitor prevented SARS-CoV-2 infection in all six ferrets tested. Much more work is needed before this could be used in humans.

The claim: Treatment of ferrets with a dimeric lipopeptide fusion inhibitor completely prevents SARS-CoV-2 infection in ferrets.

The evidence: Per Figure 3, SARS-CoV-2 was detected in all three animals inoculated with the virus, all six animals treated with a placebo, and none of the animals treated with the dimeric lipopeptide fusion inhibitor (1). Animals treated with dimeric lipopeptide fusion inhibitor did not mount an immune response to SARS-CoV-2 while an immune response was seen in inoculated animals and placebo treated animals (Figure 4).

More research is needed before this treatment can be used in humans. This preliminary study showed that in a small sample of animals which do not typically show COVID symptoms, SARS-CoV-2 infection was blocked by the dimeric lipopeptide fusion inhibitor. This paper describes the first step in a long journey. Before a new treatment is approved for use in humans, Phase I, II and III clinical trials must be completed (2) which includes showing that a treatment does no harm to healthy humans and proving that it works in humans. This work also needs peer-review in a published journal which may occur with time.

Sources:

1) https://www.biorxiv.org/content/10.1101/2020.11.04.361154v1.full.pdf

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

The takeaway: The debt to GDP ratio after coronavirus relief spending is higher than it has ever been.

The claim: The coronavirus pandemic is expected to take the U.S. national debt to levels not seen since World War II.

The evidence: A number of COVID-19 spending acts and executive orders include: Coronavirus Preparedness and Response Supplemental Appropriations Act, 2020, Families First Coronavirus Response Act (FFCRA), CARES Act, Paycheck Protection Program and Health Care Enhancement Act, and President Trump's Executive Actions (1). Prior to these bills and executive actions, the fiscal year 2020 federal deficit was predicted to be $3.1 trillion (1). The total cost of the coronavirus relief measures is $2,607,000,000,000 (1). The debt to GDP ratio in 2020 at the end of quarter 2 is 136% (2). The debt had previously peaked in 1946 after WWII at 118% debt to GDP ratio (2).

Sources:

1) https://www.forbes.com/sites/robertberger/2020/10/18/5-big-numbers-reveal-the-unsettling-scope-of-stimulus-spending/?sh=26ae8057142b

2) https://www.thebalance.com/national-debt-by-year-compared-to-gdp-and-major-events-3306287

Take away: Even though mini-lungs (and mini-organs) are extremely valuable tools for scientist to study disease and prospective therapeutics, results obtained with these models are hardly generalizable and normally need to be validated in animal models and clinical studies.

The claim: Based on our model we can tackle many unanswered key questions, such as understanding genetic susceptibility to SARS-CoV-2, assessing relative infectivity of viral mutants, and revealing the damage processes of the virus in human alveolar cells. Most importantly, it provides the opportunity to develop and screen potential therapeutic agents against SARS-CoV-2 infection.

The evidence: Regardless of their name, mini-organs are hardly real miniature organs, these clumps of cells resemble organs in many ways, but they lack certain features that allow real organs to function and grow. For now, mini-organs don’t develop beyond tiny and simplistic models of organs, and remain hard to produce in the large, consistent batches needed for drug screening and other efforts. But, in spite of their limitations, they still are a giant step up from 2D cultures of cells that scientists have long grown in the lab. In particular, studies of SARS-CoV-2 in mini-organs have limitations because they do not reflect the crosstalk between organs and systems that happens in the body. Here for example, the mini-organs do not produce the full cellular spectrum present in the adult alveoli. Also, the mini-lungs in this study cannot mimic an interaction with the immune system, which likely influences how the disease develops. Some groups are beginning to test existing drugs against SARS-CoV-2 in mini-organs in a small scale, but we will only know at the end of this process what the predictive value of these systems are for testing drug efficacy.

Source: https://www.cell.com/cell-stem-cell/fulltext/S1934-5909(20)30498-7 https://www.nature.com/articles/ncb3312 https://www.biorxiv.org/content/10.1101/2020.06.10.144816v1 https://www.biorxiv.org/content/10.1101/2020.05.25.115600v2

The takeaway: During the peak of infections during large outbreaks, COVID-19 deaths in age group 25-44 is higher than drug overdose deaths.

The claim: COVID-19 has likely become the leading cause of death in age group 25-44.

The evidence: This article compares COVID-19 deaths to opioid deaths during 2018. When the hardest hit areas are combined and areas not hit are excluded, the number of COVID-19 deaths is five deaths more than the opioid deaths during the same period in 2018. Unintentional injuries are the leading cause of death in the age group 25-44 (1-2). In 2018, opioid overdose resulted in 24,253 deaths in the age group of 25-44 in the United States (3). Transportation fatal injuries for the age group 25-44 in 2018 was 12,904 (4). In 2020, deaths from all causes for age group 25-44 is 124,736 with 5,911 directly attributable to COVID-19 (5, accessed 10/28/2020).

COVID-19 was briefly the leading cause of death in the hardest hit areas during the peak of the epidemic for age group 25-44 if unintentional injuries is broken into subcategories.

Sources: 1 https://www.cdc.gov/injury/wisqars/animated-leading-causes.html

2 https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_06-508.pdf

3 https://www.cdc.gov/mmwr/volumes/69/wr/mm6911a4.ht m

4 https://webappa.cdc.gov/sasweb/ncipc/mortrate.html

5 https://www.cdc.gov/nchs/nvss/vsrr/covid_weekly/index.htm

The takeaway: An increase in COVID-19 infections occurred nationwide in the time following protests. Due to simultaneous occurrence of non-uniform lifting of stay-at home orders, Memorial Day, and Black Lives Matter protests, it is not possible to conclusively determine the exact cause of the nationwide COVID-19 case spike after June 9, 2020.

The claim: Black Lives Matter protests did not seem to trigger a spike of COVID-19 infections.

The evidence: This statement is based on an article written in IZA Institute of Labor Economics discussion paper series (1). The article, titled “Black Lives Matter Protests, Social Distancing, and COVID-19” states that overall, stay-at home orders were better followed during protests based on cell phone data. Yet it still shows a steady increase in COVID-19 cases (Figure 6, 1). Additionally, The data from this report stops after June 9th while riots continued and COVID-19 cases across the country spiked (2, 3). As other factors such as Memorial Day weekend, and opening of economies occurred in a non-uniform fashion during the same time as protests, it is not possible to determine the exact cause of the nationwide spike in COVID-19 cases.

The abstract of the IZA report was updated August 2020 to read: "We conclude that predictions of population-level spikes in COVID-19 cases from Black Lives Matter protests were too narrowly conceived because of failure to account for non-participants’ behavioral responses to large gatherings." (4). The non-participant response was explained by this statement in the abstract: "Event-study analyses provide strong evidence that net stay-at-home behavior increased following protest onset." To put this in plain language: non-protestors stayed home more during protests which resulted in a steady increase in COVID-19 instead of a spike. The effect of mask wearing by protestors was not mentioned in the report.

Only anecdotal evidence and one small study (20 participants) were found showing protestors wearing masks (5-9). No scientific publications with the direct effect of the masks on the spread of COVID-19 during protests were found.

Valentine et al examined eight cities with tens of thousands of protestors (1, 10). Cities were chosen which had economies open at least 30 days prior to the protests to control for an expected spike when economies open. They found that six out of eight cities examined had significant abnormal positive growth of COVID-19 infection rate following the Black Lives Matter protests (10). All cities studied had abnormal positive infection rate growth.

Protests resulted in abnormal positive infection growth rates in all eight cities with stay at home orders lifted for at least 30 days prior to protests (10). A spike in COVID-19 cases nationwide happened after June 9th (3). Due to simultaneous occurrence of non-uniform lifting of stay-at home orders, Memorial Day, and Black Lives Matter protests, it is not possible to conclusively determine the exact cause of the nationwide COVID-19 case spike after June 9, 2020.

Sources:

1 http://ftp.iza.org/dp13388.pdf

2 https://www.theguardian.com/us-news/2020/jun/07/george-floyd-protests-enter-third-week

3 https://covid.cdc.gov/covid-data-tracker/#trends_dailytrendscases

4 https://www.nber.org/papers/w27408

5 https://www.npr.org/sections/coronavirus-live-updates/2020/06/24/883017035/what-contact-tracing-may-tell-about-cluster-spread-of-the-coronavirus

6 https://www.vox.com/2020/6/26/21300636/coronavirus-pandemic-black-lives-matter-protests

7 https://news.northeastern.edu/2020/08/11/racial-justice-protests-were-not-a-major-cause-of-covid-19-infection-surges-new-national-study-finds/

8 https://www.geekwire.com/2020/testing-shows-no-big-spike-covid-19-infections-due-protests-wear-mask/

9 https://assets.researchsquare.com/files/rs-68862/v1/79db6827-52c3-4e94-afa0-679d15a89049.pdf

10 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7454741/

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

Sources:

(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/

Take away: This model excludes the possibility of vaccination. As many vaccines are in stage three clinical trials, the conclusion that more people will die from closing schools, etc. will most likely not be realized.

The claim: School closures and isolation of younger people will increase total number of deaths from second and subsequent waves of COVID-19 when restrictions are lifted.

The evidence: This model predicts more deaths from the combination of place closures such as schools, case isolations, household quarantine, and social distancing of over 70s than for the combination of case isolation, household quarantine, and social distancing for over 70s. The majority of the deaths for the combination of place closures, case isolations, household quarantine, and social distancing of over 70s occur once the restrictions are lifted. This model excludes the possibility of a vaccine reducing the size of the second wave.

At least ten companies have a COVID-19 vaccine in the final stage (Phase III) of clinical trials (1). Therefore a model which excludes vaccination will most likely not be accurate to reality once a vaccine is widely administered.

Source:

1 https://www.who.int/publications/m/item/draft-landscape-of-covid-19-candidate-vaccines

Take away: More research is needed before the conclusion can be reached that T-cells from common cold coronaviruses are protective against SARS-CoV-2.

The claim: A significant part of the population may be immune to SARS-CoV-2 due to cross-reactivity to T-cells from HCo infections (“common cold viruses”).

The evidence: T- cell cross reactivity between common cold coronaviruses and SARS-CoV-2 occurred in 20-50% of people not exposed to SARS-CoV-2 (1-4). This cross-reactivity from T-cells led to the speculative hypothesis that cross-reactivity explains why children and young adults are not affected as badly as older adults (1). Additional research is needed to conclude that the presence of cross-reactive T-cells leads to less severe COVID-19 disease and does not result in the cytokine storm which is harmful instead of helpful in recovery from COVID-19 (2, 4). Significant cross-reactivity between SARS-CoV-2 and HCo antibodies was not observed with 1064 serum samples when tested with ELISA (5). A small study found some cross-reactivity between SARS-CoV-2 and HCo using rapid immunochromatographic antibody test which tests the ability of antibodies to react with SARS-CoV-2 (6).

The immune system uses multiple components to rid the body of an infection. Innate immunity includes inflammation, fever, and cells which non-specifically destroy infectious/toxic particles (7). The adaptive immune system includes cells which adapt to the specific pathogen it is attacking (8). The adaptive immune system includes B cells and T cells. B cells produce antibodies. Antibodies bind and neutralize toxins/infectious particles. T cells kill infected human cells which present antigens, infectious particle identifiers, which specific T cells recognize. A summary of the immune system's interaction with SARS-CoV-2 is written (9). Additional discussion can be found (10, 11).

In conclusion, T-cell cross reactivity was shown to occur (1-4). More research is needed to conclusively determine whether the presence of HCo cross-reactive T-cells leads to prevention or less severe infection by SARS-CoV-2.

Sources:

1 https://www.nature.com/articles/s41577-020-0389-z

2 https://www.cell.com/cell/fulltext/S0092-8674(20)30610-3?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0092867420306103%3Fshowall%3Dtrue

3 https://pubmed.ncbi.nlm.nih.gov/32766111/

4 https://www.nature.com/articles/s41586-020-2550-z

5 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7417941/

6 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7381928/

7 https://www.ncbi.nlm.nih.gov/books/NBK26846/

8 https://www.ncbi.nlm.nih.gov/books/NBK21070/

9 https://www.cell.com/trends/pharmacological-sciences/pdf/S0165-6147(20)30130-9.pdf?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0165614720301309%3Fshowall%3Dtrue

10 https://blogs.sciencemag.org/pipeline/archives/2020/07/07/more-on-t-cells-antibody-levels-and-our-ignorance

11 https://twitter.com/EricTopol/status/1278400526716211200?s=20

Take Away: Most germicidal ultraviolet (UV) lamps emit a wavelength of around 254 nm. While these are very effective means of sterilization, they are also damaging to human skin and eyes and therefore are used in unoccupied spaces. However, a recent study has shown that a safer form of UV light at a wavelength of 222 nm is effective in killing SARS-CoV-2 virus in vitro.

The Claim: Researchers offer first proof that Ultraviolet C light with a 222 nm wavelength — which is safer to use around humans — effectively kills the SARS-CoV-2 virus.

The Evidence: The authors reference the safety of 222 nm UV light, and there are many studies to support this claim. 222 nm UV light has been shown to not cause DNA damage or skin lesions even at higher doses and for longer exposure times than used here (1, 2).

In the study referenced, researchers at Hiroshima University exposed SARS-CoV-2 virus to low dosage 222 nm UV light and subsequently measured the amount of viable virus (3). They found that exposure of 0.1 mW/cm^2 for 30 seconds reduced the amount of viable virus by 99.7%. However, as the authors note, this study was performed using virus plated on a dish in a hood, and translation of these results to a public setting is unclear. For instance, in a hospital, there are many different types of surfaces and direct/consistent exposure to the UV light might not be feasible. While this study is promising, additional studies need to be done before promoting this as a safe and effective means of killing SARS-CoV-2 in an occupied environment.

Sources: 1) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6059456/

2) https://onlinelibrary.wiley.com/doi/abs/10.1111/php.13269

3) https://www.sciencedirect.com/science/article/pii/S0196655320308099#:~:text=Results,after%20a%205%2Dmin%20irradiation.

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

Take away: Waning antibodies don’t necessarily mean that immunity will also decrease, because other components of the immune system retain “memory” for an infection and can combat invaders even after antibody counts have gone down.

The claim: “If you were infected with the novel coronavirus, a new study suggests that your immunity to the virus could decline within months.”

The evidence: This study [1], along with others [2], does indeed show evidence for declining neutralizing antibodies within a few months after infection; however, antibody counts alone are not enough to predict whether a patient will have durable immunity to a virus. Neutralizing antibodies are generated by B cells, a type of immune cell that patrols the body looking for their molecular targets. Some B cells carry “memory,” a quality that allows them to respond quickly when they see a virus or pathogen that they have encountered before, which allows them to pump out large quantities of antibody rapidly to fight the infection [3]. It’s actually normal in many viral infections for antibody levels within the blood to wane over time; the real concern is whether there are enough memory B cells to generate new antibodies at a moment’s notice.

In addition to B cells, a second type of immune cell known as a “T cell” is critical for predicting durable immunity. Like B cells, some T cells carry “memory” and can patrol the body for years looking for their targets. Some T cells play a role in helping B cells produce antibodies quickly, and other T cells can actually target the infection directly [4]. Studies have now shown that T cell responses can persist after SARS-CoV-2 infection, and some patients even have T cells that can react to SARS-CoV-2 due to “cross-reactivity,” likely from preexisting immunity from common cold viruses that share some characteristics of SARS-CoV-2. While this cross-reactivity does not guarantee immunity, the presence of robust B and T cell responses is important, and could be more predictive than presence of antibodies alone.

This article, written by a two well-known immunologists and COVID-19 experts at Yale University, provides a nice summary of the data that puts these claims in context [6].

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: COVID-19 infections result in antibodies in almost all cases. These antibodies probably give immunity to future infection for at least some time, although how long is still not known.

The claim: The presence of antibodies to SARS-CoV2 does not guarantee future immunity from future COVID-19 infection.

The evidence: COVID-19 has not been present in the human population long enough to know how long immunity will last. There is some evidence to suggest that having COVID-19 typically leads to antibodies will provide at least some immunity to future infections. The vast majority (>90%) of serious (1-3) and mild (4,5) COVID-19 infections do result in the production of antibodies and it has been found that neutralizing antibodies provide immunity to reinfection in monkeys (6). We do not know how long immunity lasts. The best evidence is from the related coronavirus infections SARS and MERS. SARS and MERS infections result in antibodies that last for at least 1-3 years (7-9).

Source:

1. https://academic.oup.com/cid/advance-article/doi/10.1093/cid/ciaa344/5812996
2. https://erj.ersjournals.com/content/early/2020/05/13/13993003.00763-2020.abstract
3. https://www.nature.com/articles/s41591-020-0897-1)
4. https://www.sciencedirect.com/science/article/pii/S2352396420302905
5. https://www.medrxiv.org/content/10.1101/2020.07.11.20151324v1
6. https://www.biorxiv.org/content/10.1101/2020.03.13.990226v2.abstract
7. https://www.jimmunol.org/content/jimmunol/181/8/5490.full.pdf
8. https://wwwnc.cdc.gov/eid/article/13/10/07-0576_article,
9. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5512479/

Take away: Dr. Van Kerkhove appeared to refer to only “asymptomatic” individuals and not “presymptomatic” individuals in her statement. Clarification from the WHO, and public availability of the data leading to the claim, is needed for proper interpretation. At the current time, existing published data indicates that a significant amount of SARS-CoV-2 infections are due to individuals who did not have symptoms when they spread the virus.

The claim: According to the WHO, asymptomatic spread of coronavirus is ‘very rare’.

The evidence: This statement is attributed to WHO official Dr. Maria Van Kerkhove during a recent news conference. It deserves greater clarification from the WHO, but Dr. Van Kerkhove appears to make the distinction between “asymptomatic” and “pre-symptomatic” individuals during her comments. This distinction is essential for proper interpretation of her statement. “Asymptomatic” refers to persons who test positive, but who never display symptoms throughout the course of their SARS-CoV-2 infection. In contrast, “presymptomatic” individuals are those with confirmed infection, who do not currently display symptoms, but later go on to develop COVID-19 related symptoms (fever, cough, loss of taste/smell, etc).

Importantly, the distinction between asymptomatic and presymptomatic can only be made retrospectively. From a clinical standpoint, if someone currently has no symptoms, but tests positive, there is no way of knowing at that time if they are “asymptomatic” or “presymptomatic”. Preliminary data estimates that around 20% of SARS-CoV-2 infections are truly “asymptomatic”.

If “asymptomatic” individuals were rarely involved in transmission of the virus, this would be an important finding, but from a practical standpoint if “presymptomatic” individuals still spread the virus (as the data indicates), then the rationale for preventative measures still stands. Early studies [1] [2] have estimated that up to 40-60% of virus spread occurs when people don’t have symptoms. Preventative measures such as social distancing and universal mask wearing have been implemented to prevent the spread of virus from individuals not currently demonstrating symptoms.

This explanation is garbled and misstated. Genetic material is stored in DNA in the nucleus of the cell. Messenger RNA (mRNA) molecules carry the information stored within the DNA to the rest of the cell. Both DNA and RNA are a type of molecule called a "nucleic acid." Once outside the nucleus, the information in the messenger RNA can then be read, or "translated," to create proteins, such as the spike protein used by SARS-CoV-2. These proteins in turn carry out a wide variety of tasks that allow cells to function. This process is known as the "Central Dogma of Molecular Biology".