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  1. Last 7 days
    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.

      Sources:

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

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

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

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

  2. Nov 2020
    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.

      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

    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.

      Sources:

      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

  3. Oct 2020
    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. 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.

      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/

    1. In comparison, the ratio is approximately 2.5 times greater than the estimated IFR for seasonal influenza

      Take away:

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

      The claim:

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

      The evidence:

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

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

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

      Sources:

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

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

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

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

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

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

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

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

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

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

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

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

      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/

    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.

      Source:

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

    1. CDC reverses course on testing for asymptomatic people who had Covid-19 contact

      Take Away

      Transmission of viable SARS-CoV-2 RNA can occur even from an infected but asymptomatic individual. Some people never become symptomatic. That group usually becomes non-infectious after 14 days from initial infection. For persons displaying symptoms , the SARS-CoV-2 RNA can be detected for 1 to 2 days prior to symptomatology. (1)

      The Claim

      Asymptomatic people who had SARS-CoV-2 contact should be tested.

      The Evidence

      Yes, this is a reversal of August 2020 advice. What is the importance of asymptomatic testing?

      Studies show that asymptomatic individuals have infected others prior to displaying symptoms. (1)

      According to the CDC’s September 10th 2020 update approximately 40% of infected Americans are asymptomatic at time of testing. Those persons are still contagious and are estimated to have already transmitted the virus to some of their close contacts. (2)

      In a report appearing in the July 2020 Journal of Medical Virology, 15.6% of SARS-CoV-2 positive patients in China are asymptomatic at time of testing. (3)

      Asymptomatic infection also varies by age group as older persons often have more comorbidities causing them to be susceptible to displaying symptoms earlier. A larger percentage of children remain asymptomatic but are still able to transmit the virus to their contacts. (1) (3)

      Transmission modes

      Droplet transmission is the primary proven mode of transmission of the SARS-CoV-2 virus, although it is believed that touching a contaminated surface then touching mucous membranes, for example, the mouth and nose can also serve to transmit the virus. (1)

      It is still unclear how big or small a dose of exposure to viable viral particles is needed for transmission; more research is needed to elucidate this. (1)

      Citations

      (1) https://www.who.int/news- room/commentaries/detail/transmission-of-sars-cov-2- implications-for-infection-prevention-precautions

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

      (3) He J, Guo Y, Mao R, Zhang J. Proportion of asymptomatic coronavirus disease 2019: A systematic review and metaanalysis. J Med Virol. 2020;1– 11.https://doi.org/10.1002/jmv.26326

    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.

      Sources:

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

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

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

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

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

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

    1. For every one confirmed case, Redfield said, the CDC estimates that 10 more people have been infected.

      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

    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.

      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

    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.

      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

    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).

      Sources:

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

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

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

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

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

  4. Sep 2020
    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. Giles, J. R., Erbach-Schoenberg, E. zu, Tatem, A. J., Gardner, L., Bjørnstad, O. N., Metcalf, C. J. E., & Wesolowski, A. (2020). The duration of travel impacts the spatial dynamics of infectious diseases. Proceedings of the National Academy of Sciences, 117(36), 22572–22579. https://doi.org/10.1073/pna