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

Overall scientific credibility: 'low' according to the scientists who analyzed this article.

Find more details in Climate Feedback's analysis

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

This is a general concept, the rest of the article extrapolates the idea to the act of reading. This may be a stretch, since it implies that whatever can be broken into parts will belong to the hermeneutic circle, while this only applies to interpreting (text)

This is the only useful idea in the text. Whatever we read has the context in which it was written and the context in whcih it is being read. Is this a hermeneutic circle as described earlier? Don't think so.

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/

trusktr herman willems • 2 years ago Haha. Maybe React should focus on a template-string syntax and follow standards (and provide options for pre-compiling in Webpack, etc).

Well anywho, there's other projects now like hyperHTML, lit-html, etc, plus some really fast ones: https://www.stefankrause.ne...

React seems a little old now (and the new Hooks API is also resource heavy).

• Share ›  Michael Calkins trusktr • 4 years ago • edited That's a micro optimization. There isn't a big enough difference to matter unless you are building a game or something extraordinarily odd.

• Share › −  trusktr Michael Calkins • 2 years ago True, it matters if you're re-rendering the template at 60fps (f.e. for animations, or for games). If you're just changing views one time (f.e. a URL route change), then 100ms won't hurt at all.

These are pieces which the IndieWeb should endeavor to experiment in and attempt to fix. Though I will admit that pieces of the IndieWeb layers on top of platforms like WordPress can help to mitigate some context collapse and aggregate social interactions better. (eg: reply context and POSSE)

I like their idea about Twitter being an annotation tool and to some extent it is, and a good one at that. However, we still need to address the distribution mechanism and the fact that Tweets like this are often bereft of context and cause context collapse.

Quote tweets and dunking mechanisms would be interesting to study in this context, particularly in a world where people often delete tweets (dunked or not) which means the original context is gone or missing and we're only left with an orphaned annotation.

Other cultural examples of missing context include commentary for live sporting or cultural events like the Super Bowl, World Series, World Cup Soccer, or the Academy Awards. Watchers will comment on something in real time (often even without an identifying or contextualizing hashtag, eg: #Oscars19), supposing an implied context from their audience, but later generations will be at odds to find or re-complete the original context.

But are we also sometimes falsely defining ourselves because of context collapse within these structures?

Isn't context collapse a root cause of a lot of the toxicity of our communications within platforms like Twitter and Facebook?

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

Overall scientific credibility: 'low', according to scientists who analyzed this article.

Find more details in Health Feedback's analysis

Alexandra Freeman on Twitter. (n.d.). Twitter. Retrieved October 8, 2020, from https://twitter.com/alex_freeman/status/1313533304696639489

Take away: While this estimate may be the most accurate at the time there are several reasons (addressed below) why any estimate provided at this time may be imprecise. As more data is accrued, including information on the immunological dynamics of the SARS-CoV2 antibodies, we should expect to see a more accurate estimate.

The claim: For every one confirmed case the CDC estimates that another 10 more people have been infected.

The evidence: This estimate was revealed in a press briefing with CDC Director Robert Redfield on June, 25, 2020. It is important to emphasize that this is an estimate extrapolated from the collective data of numerous seroprevalence surveys (antibody tests) performed in different locations across the U.S. While it is most definitely true that the reported case numbers are lower than the actual, given the prevalence of asymptomatic individuals that do not visit medical centers to be tested, the actual figure may be lower or higher than the estimate presented here due to a variety of factors including:

1) Areas surveyed: Indeed, it is known that the number of cases vary disproportionately across different areas of the U.S. According to the CDC, three types of seroprevalence surveys are commonly performed: large-scale geographic, community-level, and special populations. It is important to note that each survey may or may not be completely representative of the specific area yet alone the U.S. as a whole.

2)Type of antibody testing: The FDA reported on the performance of numerous EUA authorized serology tests. The conclusion is that each test has varying levels of accuracy and confidence intervals. As the estimate provided by Redfield was most likely obtained from data derived from the specific test used at each individual surveillance site, the figure may be further skewed by the accuracy of each test.

3)Origin of blood samples: The type of individuals from which the blood samples tested originated may have a significant effect on the Redfield’s estimate. For example, if certain surveillance sites are exclusively testing samples from sick patients, the estimate may be an overestimate as a population presenting COVID symptoms is more likely to test positive than a healthy-looking population. Therefore, a detailed characterization of the individuals from which the blood was obtained would be needed in order to uphold accuracy.

4)Time of tests: As the advent of antibodies can occur a week or longer post-infection, individuals who have recently been infected may not have detectable levels of antibodies and may come up as false negatives. It is also possible for an individual to simply not produce enough antibodies to be detectable by a given serology test. Furthermore, a recent paper published in medrxiv suggests that certain antibodies have reduced titers within 50 days of symptom onset.

Take away: While this estimate may be the most accurate at the time there are several reasons (addressed below) why any estimate provided at this time may be imprecise. As more data is accrued, including information on the immunological dynamics of the SARS-CoV2 antibodies, we should expect to see a more accurate estimate.

The claim: For every one confirmed case the CDC estimates that another 10 more people have been infected.

The evidence: This estimate was revealed in a press briefing with CDC Director Robert Redfield on June, 25, 2020. It is important to emphasize that this is an estimate extrapolated from the collective data of numerous seroprevalence surveys (antibody tests) performed in different locations across the U.S. While it is most definitely true that the reported case numbers are lower than the actual, given the prevalence of asymptomatic individuals that do not visit medical centers to be tested, the actual figure may be lower or higher than the estimate presented here due to a variety of factors including:

1) Areas surveyed: Indeed, it is known that the number of cases vary disproportionately across different areas of the U.S. According to the CDC, three types of seroprevalence surveys are commonly performed: large-scale geographic, community-level, and special populations (1). It is important to note that each survey may or may not be completely representative of the specific area yet alone the U.S. as a whole.

2)Type of antibody testing: The FDA reported on the performance of numerous EUA authorized serology tests (2). The conclusion is that each test has varying levels of accuracy and confidence intervals. As the estimate provided by Redfield was most likely obtained from data derived from the specific test used at each individual surveillance site, the figure may be further skewed by the accuracy of each test.

3)Origin of blood samples: The type of individuals from which the blood samples tested originated may have a significant effect on the Redfield’s estimate. For example, if certain surveillance sites are exclusively testing samples from sick patients, the estimate may be an overestimate as a population presenting COVID symptoms is more likely to test positive than a healthy-looking population. Therefore, a detailed characterization of the individuals from which the blood was obtained would be needed in order to uphold accuracy.

4)Time of tests: As the advent of antibodies can occur a week or longer post-infection, individuals who have recently been infected may not have detectable levels of antibodies and may come up as false negatives. It is also possible for an individual to simply not produce enough antibodies to be detectable by a given serology test. Furthermore, a recent paper published in medrxiv suggests that certain antibodies have reduced titers within 50 days of symptom onset (3).

To conclude, while this estimate may be the most accurate at the time given the available data, many factors can affect the figure and, in some instances, more information is needed as it is unclear exactly how this number was obtained from the information provided in the press briefing.

Sources: 1) https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/about-serology-surveillance.html

2) https://www.fda.gov/medical-devices/coronavirus-disease-2019-covid-19-emergency-use-authorizations-medical-devices/eua-authorized-serology-test-performance

3) https://www.medrxiv.org/content/10.1101/2020.07.09.20148429v1

The takeaway: >50% of the adult US population has at least one chronic condition. Therefore exclusion of deaths from people with comorbidity will not predict how COVID-19 affects >50% of the adult US population.

The claim: Only 6% of deaths were caused by COVID-19 alone.

The evidence: The CDC website does state that "For 6% of the deaths, COVID-19 was the only cause mentioned." (1) The same web page also states "Data during the period are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more."

Additionally, in the USA 6 out of 10 adults have one chronic disease and 4 out of 10 adults have two or more chronic conditions (2). Based on this data, COVID-19 deaths in people with chronic conditions should not be excluded because >50% of the adult population has at least one chronic condition.

Sources:

1 https://www.cdc.gov/nchs/nvss/vsrr/covid_weekly/index.htm?fbclid=IwAR2-muRM3tB3uBdbTrmKwH1NdaBx6PpZo2kxotNwkUXlnbZXCwSRP2OmqsI

2 https://www.cdc.gov/chronicdisease/resources/infographic/chronic-diseases.htm

This is a particularly important metric for any testing program, because it gives a sense of whether enough testing is being done to accurately capture the true positive rate. For instance, the WHO recommends that to ensure adequate testing, the % positive rate should be at or below 5% for at least 14 consecutive days: https://coronavirus.jhu.edu/testing/testing-positivity

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

worker productivity relation to the WBGT heat stress levels using work intensity and rest relation

foundational research to base heat stress on productivity analysis: reference values for WBGT to work intensity

divided into sub-sections of each variable that is affected by heat exposure

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

Fareri, Dominic S., Joanne Stasiak, and Peter Sokol-Hessner. ‘Choosing for Another: Social Context Changes Dissociable Computational Mechanisms of Risky Decision-Making’, 21 September 2020. https://doi.org/10.31234/osf.io/dr42a.

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: SARS-CoV-2 infection has been clearly linked to heart muscle injury in those with severe COVID-19 illness. However, at present, there is insufficient data to determine the impact of mild or asymptomatic COVID-19 on the hearts of previously healthy individuals.

The Claim: COVID-19 can wreck your heart, even if you haven’t had any symptoms.

The Evidence: Several articles, including this August 31st piece (1), have raised the alarm about dangerous effects of mild or even asymptomatic cases of COVID-19 on the heart of infected individuals.

In support of this argument, there have been numerous reports, some of which are cited in the article above, documenting severe heart inflammation (myocarditis) and injury (e.g. cardiomyopathy and/or heart failure) in patients with COVID-19. However, most of these documented cases were in individuals with severe cases of COVID-19. At present, the evidence for clinically significant heart injury (requiring treatment or special precautions) from mild or asymptomatic COVID-19, is much less clear, especially in those with no prior evidence of heart disease.

One recent study reported that 78% of patients from an unselected cohort (including patients with asymptomatic, mild, and severe cases) had evidence of myocarditis (via MRI or blood testing) following COVID-19 infection (2). This study clearly demonstrated the link between COVID-19 and myocarditis by examining tissue from biopsies of the heart (the gold standard definitive diagnosis of myocarditis) of patients with the most severe cases. The study went on to show that, on average, patients who were treated for COVID-19 at the hospital (presumably more severe cases) and patients who were treated at home (presumably asymptomatic to moderate cases) both had blood test levels or MRI findings suggesting elevated myocarditis compared to non-COVID-19 infected patients with similar health profiles.

A key limitation here is “average”. The study was not designed or powered to look for the rate of myocarditis in only previously healthy patients with mild or asymptomatic COVID-19. This study included asymptomatic patients in the analysis, but without knowing their prior health or comparing their findings to other healthy non-COVID patients, it is not possible to infer the risk of myocarditis to this population. To their credit, the authors of the study discuss this limitation in their conclusions.

Despite this, the study was widely covered as evidence that ”COVID-19 can wreck your heart, even if you haven’t had any symptoms.“ In order to answer that question, we need research looking selectively healthy patients with mild or asymptomatic COVID-19 as outlined above.

Until that research is conducted, we might look at COVID within the same context as a number of other well studied viruses, many of which generally cause mild illness, that have also been shown to lead to heart injury and inflammation (3).

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.scientificamerican.com/article/covid-19-can-wreck-your-heart-even-if-you-havent-had-any-symptoms/
2. https://jamanetwork.com/journals/jamacardiology/fullarticle/2768916
3. https://www.ahajournals.org/doi/full/10.1161/CIRCULATIONAHA.108.766022

Take away: People are infectious for only part of the time they test positive. The tests for COVID-19 were granted emergency status by the FDA so some debate concerning the most ideal number of cycles is to be expected. It is worth noting that the FDA has the disclaimer "Negative results do not preclude 2019-nCoV infection and should not be used as the sole basis for treatment or other patient management decisions. Negative results must be combined with clinical observations, patient history, and epidemiological information (2)."

The claim: Up to 90 percent of people diagnosed with coronavirus may not be carrying enough of it to infect anyone else

The evidence: Per Walsh et al. (1), SARS-CoV-2 virus (COVID-19) is most likely infectious if the number of PCR cycles is <24 and the symptom onset to test is <8 days. RT-PCR detects the RNA, not the infectious virus. Therefore, setting the cycle threshold at 37-40 cycles will most likely result in detecting some samples with virus which is not infectious. As the PCR tests were granted emergency use by the FDA (samples include 2-9), it is not surprising that some debate exists currently about where the cycle threshold should be. Thresholds need to be set and validated for dozens of PCR tests currently in use. If identifying only infectious individuals is the goal, a lower cycle number may be justified. If detection of as many cases as possible to get closer to the most accurate death rate is the goal, setting the cycle threshold at 37-40 makes sense. A lower threshold will result in fewer COVID-19 positive samples being identified. It is worth noting that the emergency use approval granted by the FDA includes the disclaimer that a negative test does not guarantee that a person is not infected with COVID-19. RNA degrades easily. If samples are not kept cold or properly processed, the virus can degrade and result in a false negative result.

Source: 1 https://academic.oup.com/cid/advance-article/doi/10.1093/cid/ciaa638/5842165

2 https://www.fda.gov/media/134922/download

3 https://www.fda.gov/media/138150/download

4 https://www.fda.gov/media/137120/download

5 https://www.fda.gov/media/136231/download

6 https://www.fda.gov/media/136472/download

7 https://www.fda.gov/media/139279/download

8 https://www.fda.gov/media/136314/download

9 https://www.fda.gov/media/140776/download

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: The claim that virus can be detected for a long time but is not infectious needs further clarification. This claim was based on a Lancet article (1). Within the Lancet article, some of the studies cited detected RNA in stool/blood/seminal fluid samples instead of nasal swabs. Other studies cited did not test infectious nature of virus detected by PCR. It is several logic steps to travel from detecting virus in stool/blood/seminal fluid in Lancet article to concluding that PCR of nasal swabs for COVID-19 results in large numbers of false positives.

The claim: RNA from coronavirus is present and can be detected for a long time but may not be infectious.

The evidence: The Spectator article links to the article "SARS-CoV-2 shedding and infectivity" in the Lancet (1). This article cites seven articles to support the statement that RNA persists long after virus is not infectious. Of these articles, only one reports that virus was detected at ~30 days but could not be cultured beyond three weeks (2). This article also states that detection was easier in stool samples than nasal samples after the first five days. Several articles cited by source 1 did not report infectivity of virus detected (3, 4, 5, and 7). Of the two remaining articles, virus was detected in serum/blood (6 and 8). In the serum study, 58% of tested specimens were infectious (6).

Source:

1 https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30868-0/fulltext

2 https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(03)13412-5/fulltext

3 https://pubmed.ncbi.nlm.nih.gov/15030700/

4 https://pubmed.ncbi.nlm.nih.gov/27682053/

5 https://pubmed.ncbi.nlm.nih.gov/29648602/

6 https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(16)30243-1/fulltext

7 https://pubmed.ncbi.nlm.nih.gov/28195756/

8 https://pubmed.ncbi.nlm.nih.gov/22872860/

Take away: Though many articles are referenced, additional context is needed because the conclusions of the publications do not always agree with the conclusion of the author of this article. Additionally, publications which conflict with the claims in this article are not presented.

The claim: Masks are neither effective nor safe.

The evidence: The data in the articles referenced here is inconclusive regarding whether masks are or are not effective. Though several studies referenced here did not see a statistically significant difference between those who wore masks and controls, several facts need to be considered. The sample size of people who became sick in the individual studies was small (often <10 people). Compliance in the mask group was not enforced. A number of the articles referenced are pre-prints lacking peer-review and validation. Some of the articles compare N95 masks to surgical masks but do not have a control no mask group. Additionally, the claim of the author of this article sometimes differs from the conclusions written by the authors of the publications cited. Publications which contradict the conclusions of the author are not presented (1, 2).

Sources:

1 https://pubmed.ncbi.nlm.nih.gov/32497510/

2 https://pubmed.ncbi.nlm.nih.gov/27632416/

These are good prescriptive questions that social media users should frequently use. (Sadly most will not unless they're forced to by design.)

A fascinating viewpoint on social media, journalism, and information. There are some great implied questions for web designers hiding in here.

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.

Krueger, D., Uhlig, H., & Xie, T. (2020). Macroeconomic Dynamics and Reallocation in an Epidemic (Working Paper No. 27047; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27047

Take away: As with most things in nature, there are always exceptions – transmission occurring at greater distances than 3 ft and evidence of aerosolization have been reported.

Discussion: In scientific terms, this virus is still very new so the data supporting an optimal physical distance to prevent transmission remains scarce. In the absence of data, public health agencies have used what they understand about this virus and similar viruses to infer a “best” answer. Public health agencies try to simplify the recommendation to a single answer, but the reality is much more complex.

According to reports the WHO bases their recommendation for 1 meter (~3 ft) distancing off of an understanding that SARS-CoV-2 behaves like similar respiratory viruses that are primarily transmitted via larger droplets (as opposed to smaller aerosols). Assuming most spread is via droplets, the WHO reportedly follows the results of a 1934 study indicating most respiratory droplets fall to the ground within 3 feet.

However, as with most things in nature, there are always exceptions – transmission occurring at greater distances than 3 ft and evidence of aerosolization have been reported.

The evidence basis for the CDCs guidance for 6 feet of separation is less clear, but probably reflects lower risk tolerance, or greater weight to evidence of aerosolization or wider droplet spread.

Even with further study, there may never be a clear answer for optimal physical distancing. This is because, (1) the area of high risk for transmission is probably dependent on the specific conditions of the interaction (e.g. loud talking, windy environment), and (2) the “optimal” distance is based on risk tolerance. There is no single distance between individuals where risk of transmission drops off precipitously to zero.

All evidence indicates that greater distances are safer but, for example, consider how restrictive a physical distancing recommendation of >50 ft would be. In the end, because we can’t control how far others stand away from us, we ask governments to consider these tradeoffs and deliver a “best” answer to guide their citizenry.

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".

Darren L Dahly, PhD on Twitter: “Share this with whoever needs to hear it: ‘risk factors’, even strong ones that are robustly identified, are almost never useful for making actionable predictions, especially when considered in isolation. TLDR: [Big OR] does not equal [Good, Useful, or Easy Decision]” / Twitter. (n.d.). Twitter. Retrieved July 11, 2020, from https://twitter.com/statsepi/status/1249680569463721984

Miller, J. G., Chahal, R., Kirshenbaum, J. S., Ho, T. C., Gifuni, A. J., & Gotlib, I. (2020). Heart Rate Variability Moderates the Link Between COVID-19 Stress and Emotional Problems in Adolescents: Evidence for Differential Susceptibility [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/mp7wt

Bayer, J. (2020). Technology habits: Progress, problems, and prospects [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/ft6am

Williams, T. C. (2020, June 8). We often accuse the right of distorting science. But the left changed the coronavirus narrative overnight | Thomas Chatterton Williams. The Guardian. https://www.theguardian.com/commentisfree/2020/jun/08/we-often-accuse-the-right-of-distorting-science-but-the-left-changed-the-coronavirus-narrative-overnight

Lagnado, D. (2020 April 27). What if.... Changing Minds. https://www.youtube.com/watch?v=2W11CRLjRgo&app=desktop

Even though a larger user base will increase your odds of getting more feedback, you can still get valuable contextual feedback with less users.

A distinction is made between the kind of logic (first-order logic) where this other distinction exists and propositional logic, where the distinction doesn't exist (the two terms coincide in that context).

https://twitter.com/AdamJKucharski/status/1245336637497913345

https://twitter.com/STWorg/status/1245410250934620169

Ting, C., Palminteri, S., Lebreton, M., & Engelmann, J. B. (2020, March 25). The elusive effects of incidental anxiety on reinforcement-learning. https://doi.org/10.31234/osf.io/7d4tc MLA

Here is their reasoning about why the public key and the ciphertext are used in the “context” of the key derivation.

Overall scientific credibility: 'very low', according to scientists who analyzed this article.

Find more details in Health Feedback's analysis

I'm glad they've included an image of a hockey stick to provide the context here, but I've always thought of it rotated so that the blade was on the ground and the sharp angle of the handle itself indicated the exponential growth curve!

This is a valid concern that needs to be addressed. While some engineers certainly do use IRB for their projects, it is not nearly as common as it should be.

The sad part of this is how painfully limiting the conversation can be on social with the character limitations and too many issues with branching conversations and following all the context.

I find that using Webmentions on my site adds a lot of value because it brings all the conversation back to my site, where it really should be for more context.