557 Matching Annotations
  1. Last 7 days
    1. Darren Dahly. (2021, February 24). @SciBeh One thought is that we generally don’t ‘press’ strangers or even colleagues in face to face conversations, and when we do, it’s usually perceived as pretty aggressive. Not sure why anyone would expect it to work better on twitter. Https://t.co/r94i22mP9Q [Tweet]. @statsepi. https://twitter.com/statsepi/status/1364482411803906048

    1. Adam Kucharski. (2021, February 6). It’s flattering being asked for your opinion by the media (especially if you have lots of them) but I do think it’s important to defer to others if you’re being asked on as a ‘scientific expert’ and the subject of the interview falls outside your area of research/expertise. [Tweet]. @AdamJKucharski. https://twitter.com/AdamJKucharski/status/1358050473098571776

    1. Dr. Jonathan N. Stea. (2021, January 25). Covid-19 misinformation? We’re over it. Pseudoscience? Over it. Conspiracies? Over it. Want to do your part to amplify scientific expertise and evidence-based health information? Join us. 🇨🇦 Follow us @ScienceUpFirst. #ScienceUpFirst https://t.co/81iPxXXn4q. Https://t.co/mIcyJEsPXe [Tweet]. @jonathanstea. https://twitter.com/jonathanstea/status/1353705111671869440

    1. Dr Nisreen Alwan 🌻. (2020, March 14). Our letter in the Times. ‘We request that the government urgently and openly share the scientific evidence, data and modelling it is using to inform its decision on the #Covid_19 public health interventions’ @richardhorton1 @miriamorcutt @devisridhar @drannewilson @PWGTennant https://t.co/YZamKCheXH [Tweet]. @Dr2NisreenAlwan. https://twitter.com/Dr2NisreenAlwan/status/1238726765469749248

  2. Apr 2021
  3. Mar 2021
    1. ‘bold’ means to have many observational consequences

      As I said at the wiki I saw this link from, you don't test the hypothesis directly, you test the predictions, the "observational consequences", from the hypothesis.

    1. Kai Kupferschmidt. (2021, March 16). “I’m not here to give you the outcome of any scientific review”, says EMA director Emer Cooke at start of press conference on AstraZeneca vaccine safety. ‘I’m here to explain the steps in the process, what we’re doing, and when you can expect us to come to a conclusion.’ [Tweet]. @kakape. https://twitter.com/kakape/status/1371811123197001729

    1. Cailin O’Connor. (2020, November 10). New paper!!! @psmaldino look at what causes the persistence of poor methods in science, even when better methods are available. And we argue that interdisciplinary contact can lead better methods to spread. 1 https://t.co/C5beJA5gMi [Tweet]. @cailinmeister. https://twitter.com/cailinmeister/status/1326221893372833793

  4. Feb 2021
  5. Jan 2021
  6. Dec 2020
    1. A scientist who does not utilize the scientific method is as much use as a carpenter who cannot make chairs or a plumber who cannot fix toilets. A science that exists as a fixed absolute, whose premises are not to be questioned, whose data is not to be examined and whose conclusions are not to be debated, is a pile of wood or a leaky toilet. Not the conclusion of a process, but its absence.

      Understanding science is a process.

  7. Nov 2020
    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. 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

  8. Oct 2020
    1. Weber notes that according to any economic theory that posited man as a rational profit-maximizer, raising the piece-work rate should increase labor productivity. But in fact, in many traditional peasant communities, raising the piece-work rate actually had the opposite effect of lowering labor productivity: at the higher rate, a peasant accustomed to earning two and one-half marks per day found he could earn the same amount by working less, and did so because he valued leisure more than income. The choices of leisure over income, or of the militaristic life of the Spartan hoplite over the wealth of the Athenian trader, or even the ascetic life of the early capitalist entrepreneur over that of a traditional leisured aristocrat, cannot possibly be explained by the impersonal working of material forces,

      Science could learn something from this. Science is too far focused on the idealized positive outcomes that it isn't paying attention to the negative outcomes and using that to better define its outline or overall shape. We need to define a scientific opportunity cost and apply it to the negative side of research to better understand and define what we're searching for.

      Of course, how can we define a new scientific method (or amend/extend it) to better take into account negative results--particularly in an age when so many results aren't even reproducible?

    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


      (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. 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

  9. Sep 2020