31 Matching Annotations
  1. Jan 2021
    1. Proteins are made up of building blocks called amino acids. N501Y means that the 501st amino acid was originally an N, which stands for the amino acid asparagine, but has been changed to a Y, which stands for tyrosine.

      The takeaway: Amino acids, represented by single letters such as N or Y make up proteins which are part of the coronavirus (as well as other biology such as animals, plants, microorganisms, etc.). Mutations are written with the original amino acid letter followed by the number of the amino acid and the new amino acid letter.

      The claim: Proteins are made up of building blocks called amino acids. N501Y means that the 501st amino acid was originally an N, which stands for the amino acid asparagine, but has been changed to a Y, which stands for tyrosine.

      The evidence:

      Coronavirus is made up of greater than 20 proteins (1). The spike protein helps coronavirus attach and enter human cells which leads to infection and disease (1). The spike protein on SARS-CoV-2, the virus that causes COVID-19, is the target of many antibodies produced by the human body to fight the SARS-CoV-2 infection (2). Changes in the spike protein sequence may necessitate a change in the human immune system to produce antibodies which stop SARS-CoV-2 from infecting human cells. Changes in the amino acid sequence are written as was stated in the claim: original amino acid, number of the amino acid in the sequence, new amino acid.


      1) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7247499/

      2) https://pubmed.ncbi.nlm.nih.gov/33448402/

  2. Dec 2020
    1. The official definition of a “close contact” — 15 minutes, within six feet — isn’t foolproof.

      The takeaway: The official definition of a "close contact" for COVID-19 is not foolproof.

      The claim: The official definition of a "close contact" - 15 minutes, within six feet - isn't foolproof.

      The evidence: In Korea, a person sitting in a restaurant 6.5 meters (>20ft) away from the COVID index case for five minutes was infected, most likely because airflow from the air conditioner carried droplets with COVID-19 from the infected person to the person who became infected (1). How common transmission across large distances occurs is still debated (2). As several indoor outbreaks were attributed to airborne transmission, precautions to prevent airborne COVID transmission are needed (3). Examples include better air filtration/UV to kill virus in the system, increased air flow from outside, avoidance of recirculating interior air, and avoiding overcrowding in interior spaces.


      1) https://jkms.org/DOIx.php?id=10.3346/jkms.2020.35.e415

      2) https://www.sciencedirect.com/science/article/pii/S0166093420302858?via%3Dihub

      3) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7454469/pdf/ciaa939.pdf

    1. According to the best estimates from the Centers for Disease Control and Prevention, 99.997 percent of individuals aged 19 and younger who contract coronavirus make a full recovery, 99.98 percent of those aged 20 to 49 make a full recovery, and 99.5 percent aged 50 to 69 fully recover.

      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.


      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

  3. Nov 2020
    1. The recommendation to wear surgical masks to supplement other public health measures did not reduce the SARS-CoV-2 infection rate among wearers by more than 50% in a community with modest infection rates, some degree of social distancing, and uncommon general mask use.

      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).


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

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

    1. Gov. Kristi Noem defended her hands-off approach to managing the deadly COVID-19 pandemic while addressing lawmakers earlier this week and called mandatory stay-at home orders "useless" in helping lower the spread.

      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.


      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/

    1. Anxiety From Reactions to Covid-19 Will Destroy At Least Seven Times More Years of Life Than Can Be Saved by Lockdowns

      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

    1. On every measure — new infections, hospitalizations, and deaths — the U.S. is headed in the wrong direction

      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.


      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

    1. We have designed a dimeric lipopeptide fusion inhibitor that blocks this critical first step of infection for emerging coronaviruses and document that it completely prevents SARS-CoV-2 infection in ferrets.

      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.


      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

    1. Many COVID-19 survivors are likely to be at greater risk of developing mental illness, psychiatrists said on Monday, after a large study found 20% of those infected with the coronavirus are diagnosed with a psychiatric disorder within 90 days.

      The takeaway: COVID-19 survivors are at a higher risk for mental illness.

      The claim: COVID-19 survivors are at a higher risk for mental illness.

      The evidence: Infection by SARS-CoV-2 was associated with an increase in anxiety disorders, insomnia, and dementia (1). Prior mental illness was also associated with an increased risk of SARS-CoV-2 infection (1). Approximately 1/3 of COVID patients were reported to have central nervous symptom issues in a study of 214 hospitalized Chinese patients (2). SARS-CoV-2 has been found in the brain and cerebral spinal fluid (3). Social isolation, pathology of SARS-CoV-2, and sedation are a few of the reasons why ICU patients experience delirium and the subsequent mental health risks (4).

      All of these factors support the statement that COVID-19 survivors are at a higher risk of mental illness.

      As a reminder, there is help for suicide. National Suicide Prevention Lifeline is a toll-free number for those in a suicidal crisis or emotional distress. The number is: 1-800-273-8255

      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.


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

      2) https://pubmed.ncbi.nlm.nih.gov/32399719/

      3) https://pubmed.ncbi.nlm.nih.gov/32240762/

      4) https://www.termedia.pl/COVID-19-What-do-we-need-to-know-about-ICU-delirium-during-the-SARS-CoV-2-pandemic-,118,40590,1,1.html

    1. The vaccine may in fact make COVID19 much, much worse in many people.

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

    1. mink are now considered a public health risk

      Takeaway: Mink are capable of contracting and transmitting SARS-CoV-2 to each other and to humans which had resulted in mutated SARS-CoV-2.

      The claim: Mink are now considered a public health risk.

      The evidence: SARS-CoV-2 infects and kills mink (1). The lung damage in mink from SARS-CoV-2 is similar to the damage in human lungs from SARS-CoV-2. The range of symptoms from asymptomatic to deadly is exhibited by the mink. Based on this pre-print article, SARS-CoV-2 is mutating in mink farms and had documented transmission from mink to humans (2).

      Extensive sequencing of SARS-CoV-2 genomes has been done (3). Mutations tend to occur in certain hot spots of the genome. The stated purpose of the sequencing research is to identify relatively stable parts of the genome to use as vaccine targets to help avoid mutant escape. The genomes of SARS-CoV-2 from mink infections had more nucleotide differences than SARS-CoV-2 from human COVID outbreaks (2). This may be due to a faster mutation rate or to the fact that so many mink were infected.


      1) https://journals.sagepub.com/doi/10.1177/0300985820943535?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub++0pubmed&

      2) https://www.biorxiv.org/content/10.1101/2020.09.01.277152v1

      3) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7199730/

    1. How can we better protect nursing home residents? This is the most vulnerable population.

      The takeaway: Nursing home residents are the most vulnerable population though others with similar age and comorbidities may be at a similar risk.

      The claim: Nursing home residents are the most vulnerable population.

      The evidence: Older, more vulnerable people live in nursing homes (1). The setting is also communal which leads to rapid spread once the virus is in the home (1). The CDC reports 61,765 deaths (2, accessed 11/2/2020). A significant percentage of the deaths occurred in nursing homes which makes sense because older people live in the homes often with multiple comorbidities (3). Probability of death from COVID-19 increases with age and comorbidity (4-5). COVID spreads easier inside than outside (6).

      Considering all of these factors, nursing home residents are the most vulnerable population. Others with similar age and comorbidities may be at a similar risk if they interact with many people.


      1) https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-in-nursing-homes.html

      2) https://data.cms.gov/stories/s/COVID-19-Nursing-Home-Data/bkwz-xpvg

      3) https://onlinelibrary.wiley.com/doi/10.1111/jgs.16784

      4) https://www.cdc.gov/coronavirus/2019-ncov/covid-data/investigations-discovery/hospitalization-death-by-age.html

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

      6) https://www.cdc.gov/coronavirus/2019-ncov/daily-life-coping/deciding-to-go-out.html

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

      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).


      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

  4. Oct 2020
    1. Experts say closing borders early and tightly regulating travel have gone a long way toward fighting the virus. Other factors include rigorous contact tracing, technology-enforced quarantine and universal mask wearing. Further, Taiwan’s deadly experience with SARS has scared people into compliance.

      The Takeaway: The combination of closing borders, tightly regulating travel, effective quarantine of all exposed people using cell phone data for enforcement, and universal mask wearing contributed to effectively keeping COVID-19 from infecting most of Taiwan's population.

      The claim: Closing borders early, tightly regulating travel, contact tracing, technology-enforced quarantine, universal mask wearing, and Taiwan's previous deadly experience with SARS resulted in control of SARS-CoV-2 in Taiwan.

      The evidence: The earlier COVID-19 cases are stopped from entering a country, the fewer cases will be present to spread the disease to others. To illustrate, it is easier to stop a trickle of water than to try to dam up a flood and easier to extinguish a candle than a forest fire. Taiwan closed its borders on January 23rd, 2020 (1). The Philippines closed their borders on February 2nd, 2020 (2). Tightly regulating travel will help to stop cases before they enter the country. Effective quarantining the few cases and contacts of the cases which do enter a country is critical to preventing the spread of the disease within the country. Taiwan used mobile telephone data to enforce quarantine (1). Without quarantine, each infected person will spread COVID-19 to 2-6 additional people based on the R0 (3, 4). Universal masking will help slow the spread of disease (5). Previous experience with controlling a deadly disease will most likely increase compliance to methods to control the disease.

      Per Our World in Data website, Taiwan had one of the least stringent government responses to COVID-19 (6). The biweekly number of COVID-19 cases in Taiwan was 23 on October 29, 2020 (7). Neighboring countries had biweekly COVID-19 cases of 372 (China), 28,644 (Philippines), 11,871 (Malaysia), 51 (Vietnam), and 8,142 (Japan). These neighboring countries had more stringent government responses to COVID-19 (6).


      1) https://focustaiwan.tw/society/202001230011


      3) https://pubmed.ncbi.nlm.nih.gov/32234343/

      4) https://pubmed.ncbi.nlm.nih.gov/32097725/

      5) https://www.nature.com/articles/s41591-020-1132-9#annotations:7jRWRheWEeuY8x_rXDuRjg

      6) https://ourworldindata.org/grapher/covid-stringency-index

      7) https://ourworldindata.org/grapher/biweekly-confirmed-covid-19-cases

    1. We find that COVID-19 has likely become the leading cause of death (surpassing unintentional overdoses) among young adults aged 25-44 in some areas of the United States during substantial COVID-19 outbreaks.

      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

    1. But that could be a drop in the ocean compared to the humanitarian fallout. “We’ve seen 400,000 die from COVID-19,” David Beasley, the Executive Director of the World Food Programme, warned in June. “We could see 300,000 die a day, for several months, if we don’t handle this right.”

      Take away: The humanitarian fallout from prolonged lockdowns to control COVID-19 could be worse than the deaths due to COVID-19.

      The claim: The humanitarian fallout from COVID-19 could be worse than the deaths caused directly by the disease.

      The evidence: Food supply chains have been disrupted due to COVID-19 (1). The World Health Organization predicts that 130 million additional people could become chronically hungry due to COVID-19 (2). Per the International Labor Organization, 1.6 billion workers have the prospect of their employment destroyed, at least partially due to the prolonged lockdowns (3).

      “For millions of workers, no income means no food, no security and no future. [...] As the pandemic and the jobs crisis evolve, the need to protect the most vulnerable becomes even more urgent."

      Guy Ryder, ILO Director-General

      A number of socio-economic consequences have resulted from COVID-19 lock-down measures to control the virus (4). 900 million learners are affected by lockdowns which results in high risk children lacking access to free meals provided by school systems, drop out rates, and social isolation/mental health (4). Affects have been seen in the agricultural, manufacturing, petroleum and oil, finance industry, travel and aviation industry, hospitality, and others (4).

      Considering the drastic increase in job loss with resulting hunger from financial instability and other social-economic factors resulting from lock-downs, the fall out from prolonged lockdowns to control COVID-19 will most likely be worse than the number of deaths due to COVID-19 directly.

      Disclaimer: This annotation is not intended to downplay the seriousness of COVID-19. Rather it is intended to put the seriousness of the disease in context of other problems that are resulting from measures to control COVID-19.


      1) https://www.nature.com/articles/d41586-020-01181-3

      2) https://www.who.int/news/item/13-07-2020-as-more-go-hungry-and-malnutrition-persists-achieving-zero-hunger-by-2030-in-doubt-un-report-warns#:~:text=Across%20the%20planet%2C%20the%20report,by%20the%20end%20of%202020.&text=further%20at%20times.)-,The%20State%20of%20Food%20Security%20and%20Nutrition%20in%20the%20World,towards%20ending%20hunger%20and%20malnutrition.

      3) https://www.ilo.org/global/about-the-ilo/newsroom/news/WCMS_743036/lang--en/index.htm

      4) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7162753/

    1. 50 percent effective

      Take away: Cloth face masks filter approximately 50% of bacteriophage five times smaller than one SARS-CoV-2 virus. Therefore it is reasonable to assume that masks, including cloth masks, are 50% effective.

      The claim: Masks are assumed to be 50% effective.

      The evidence: Face masks, including home made face masks, were shown to reduce aerosol exposure (1). Masks made from various materials were shown to filter 50-68% of Bacteriophage CS2 which is 20 nm (2). When NaCl aerosols were used instead of a bacteriophage, penetration by NaCl occurred 9-98% of the time depending on the size of the particles (3). Two well written reviews detail the efficacy of facemasks (4, 5). SARS-CoV-2 virus is ~100 nm in size (6).

      Sources: 1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2440799/

      2 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7108646/

      3 https://academic.oup.com/annweh/article/54/7/789/202744

      4 https://www.preprints.org/manuscript/202004.0203/v1

      5 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7497125/#ref23

      6 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7224694/#:~:text=SARS%2DCoV%2D2%20is%20an,they%20do%20more%20than%20that.

    1. The events did not seem to trigger spikes in infections

      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.


      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/

    1. The model predicted that school closures and isolation of younger people would increase the total number of deaths, albeit postponed to a second and subsequent waves. The findings of this study suggest that prompt interventions were shown to be highly effective at reducing peak demand for intensive care unit (ICU) beds but also prolong the epidemic, in some cases resulting in more deaths long term. This happens because covid-19 related mortality is highly skewed towards older age groups. In the absence of an effective vaccination programme, none of the proposed mitigation strategies in the UK would reduce the predicted total number of deaths below 200 000.

      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.


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

    1. In testimony before US Congress on March 11, 2020, members of the House Oversight and Reform Committee were informed that estimated mortality for the novel coronavirus was 10-times higher than for seasonal influenza. Additional evidence, however, suggests the validity of this estimation could benefit from vetting for biases and miscalculations. The main objective of this article is to critically appraise the coronavirus mortality estimation presented to Congress. Informational texts from the World Health Organization and the Centers for Disease Control and Prevention are compared with coronavirus mortality calculations in Congressional testimony. Results of this critical appraisal reveal information bias and selection bias in coronavirus mortality overestimation, most likely caused by misclassifying an influenza infection fatality rate as a case fatality rate.

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

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

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

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

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

      Comparison of influenza and COVID-19 deaths:

      Influenza CFR = 0.1-0.2%

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

      COVID-19 CFR = 2.8%

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

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

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


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

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

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

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

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

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

    1. The CDC summarized it succinctly, “For 6% of the deaths, COVID-19 was the only cause mentioned.

      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.


      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

    1. Not so novel coronavirus?

      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.


      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

    1. Dr. Anthony Fauci  is lying to himself. In his public statements he says that Covid is “Ten Times Worse than Seasonal Flu”.

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

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

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

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


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

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

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

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

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

  5. Sep 2020
    1. Hydroxychloroquine is a relatively cheap and readily available drug that has been used for decades to treat malaria. Throughout the COVID-19 pandemic, doctors around the world have vouched for positive results seen in patients who take it.

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

    1. There are two possible approaches to build widespread SARS-CoV-2 immunity: (1) a mass vaccination campaign, which requires the development of an effective and safe vaccine, or (2) natural immunization of global populations with the virus over time. However, the consequences of the latter are serious and far-reaching—a large fraction of the human population would need to become infected with the virus, and millions would succumb to it.

      Take away: Mass infection without vaccination to achieve herd immunity will result in millions of deaths based on the observed death rate and may not result in herd immunity due to virus mutation. Historically, vaccination results in less deaths than the disease.

      The claim: Herd immunity from widespread disease instead of vaccination will lead to many people dying.

      The evidence: Approximately 50-67% of a given population is estimated to need to be infected for herd immunity to COVID-19 to exist which will result in millions of deaths. This is supported by additional publications (1, 2). This number assumes that the virus will not mutate to the point where re-infection is possible. If mutation occurs, COVID could become established in the general population similar to influenza or the common cold (3). A third publication estimates a needed infected percentage of 29-74% (4). These publications support the statement that millions will die if herd immunity is achieved via infection without vaccination. Historically, vaccination results in fewer deaths/disease on a population level than the disease for which the vaccine is designed to prevent (5-7).


      1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7314002/

      2 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7262166/pdf/JMV-9999-na.pdf

      3 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7164482/

      4 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/A1480DAE803D4CD4A3E9F79B82309584/S1935789320001913a.pdf/covid19_reflections.pdf

      5 https://pubmed.ncbi.nlm.nih.gov/28708957/

      6 https://pubmed.ncbi.nlm.nih.gov/29668817/

      7 https://pubmed.ncbi.nlm.nih.gov/12531323/

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

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

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

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

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

    1. 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

    1. Take away: Though not a guarantee of health, wearing masks reduces the number of respiratory infections compared to no/inconsistent mask wearing.

      The claim: Masks are protective against clinical respiratory illness.

      The evidence: The authors performed a meta-analysis of random controlled trials and observational studies examining mask use in health care workers. The results showed that wearing masks resulted in fewer infections compared to people without masks. These results agree with other publications (1, 2). One pre-print article which performed meta-analysis showed inconclusive results concerning the effectiveness of masks (3). Based on these meta-analyses, mask wearing results in fewer respiratory infections, though it will not prevent all infections when used as the sole protective measure.


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

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

      3) https://www.medrxiv.org/content/10.1101/2020.03.30.20047217v2

    1. Detection of viruses using Polymerase Chain Reaction (PCR) is helpful so long as its accuracy can be understood: it offers the capacity to detect RNA in minute quantities, but whether that RNA represents infectious virus is another matter. RT-PCR uses enzymes called reverse transcriptase to change a specific piece of genetic material called RNA into a matching piece of genetic DNA. The test then amplifies this DNA exponentially; millions of copies of DNA can be made from a single viral RNA strand.

      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).


      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/

    1. 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).


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

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

    1. It is so mild that half of infected people are asymptomatic, shown in early data from the Diamond Princess ship, and then in Iceland and Italy.

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

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

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

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


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

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

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

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