538 Matching Annotations
  1. Nov 2021
  2. Oct 2021
    1. Dr Nisreen Alwan 🌻 on Twitter: “New @ONS #LongCovid estimates published today: 1.1 MILLION (1.7% of the whole UK population). Up from the summer estimate of 1.5%. 211,000 people with daily activities ‘limited a lot’. Greatest % in working age (35-69y). Rising prevalence in 17-24y. A tsunami of chronic illness.” / Twitter. (n.d.). Retrieved October 10, 2021, from https://twitter.com/Dr2NisreenAlwan/status/1446110337753829379

    1. Hippisley-Cox, J., Patone, M., Mei, X. W., Saatci, D., Dixon, S., Khunti, K., Zaccardi, F., Watkinson, P., Shankar-Hari, M., Doidge, J., Harrison, D. A., Griffin, S. J., Sheikh, A., & Coupland, C. A. C. (2021). Risk of thrombocytopenia and thromboembolism after covid-19 vaccination and SARS-CoV-2 positive testing: Self-controlled case series study. BMJ, n1931. https://doi.org/10.1136/bmj.n1931

  3. Sep 2021
    1. Sam Wang on Twitter: “These are risk levels that you pose to other people. They’re compared with you as—A nonsmoker—A sober driver—A vaccinated person. Unvaccinated? 5x as likely to get sick, for 3x as long. Total risk to others? 15x a vaccinated person Details:https://t.co/ckTWaivK8n https://t.co/PhpLvX2dsm” / Twitter. (n.d.). Retrieved September 19, 2021, from https://twitter.com/SamWangPhD/status/1438361144759132167

    1. David Dowdy on Twitter: “@NEJM joining the waning immunity debate. I’m going to push back a bit. Data from @UCSDHealth of vax effectiveness in health workers: 94% in June, 65% in July. Interpreted as ‘likely to be due to...delta and waning immunity over time, compounded by end of masking requirements.’ https://t.co/flDOfBbTs7” / Twitter. (n.d.). Retrieved September 2, 2021, from https://twitter.com/davidwdowdy/status/1433254675915157504?s=20

    1. Bracher, J., Wolffram, D., Deuschel, J., Görgen, K., Ketterer, J. L., Ullrich, A., Abbott, S., Barbarossa, M. V., Bertsimas, D., Bhatia, S., Bodych, M., Bosse, N. I., Burgard, J. P., Castro, L., Fairchild, G., Fuhrmann, J., Funk, S., Gogolewski, K., Gu, Q., … Xu, F. T. (2021). A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave. Nature Communications, 12(1), 5173. https://doi.org/10.1038/s41467-021-25207-0

  4. Aug 2021
    1. Tim Plante, MD MHS on Twitter: “Just reported: About half of recent ICU patients with #Covid19 in #Vermont are vaccinated. Sounds like the vaccines aren’t working, right? WRONG. Vaccines are working and here’s why. But first, let’s talk a bit about unprotected sex. A thread. (Refs at the end.) 1/n https://t.co/iyQcfCDAfh” / Twitter. (n.d.). Retrieved August 27, 2021, from https://twitter.com/tbplante/status/1430222978961317896

    1. Prof. Christina Pagel on Twitter: “THREAD latest on B.1.617.2 variant in England: B.1.617.2 (1st discovered in India) is now dominant in England. Here is a thread summarising latest PHE report and Sanger local data. TLDR: it is NOT good news. 1/7” / Twitter. (n.d.). Retrieved August 24, 2021, from https://twitter.com/chrischirp/status/1399333330286415876

    1. Youyang Gu on Twitter: “People were worried cases would spike further when the UK lifted almost all remaining restrictions in July. The opposite happened. We must acknowledge that restrictions aren’t all that effective in Western countries. (Except Australia, who just entered their 6th lockdown) https://t.co/l7tygXQqn7” / Twitter. (n.d.). Retrieved August 13, 2021, from https://twitter.com/youyanggu/status/1423415277765734402

    1. André Picard on Twitter: “Most of those numbers are in the column, which focuses on a) the risk of the Delta variant to children under 12 and b) the 29% of Canadians who are not fully vaccinated. Brace yourself for more COVID-19 nastiness https://t.co/V0agVYvKRx” / Twitter. (n.d.). Retrieved August 13, 2021, from https://twitter.com/picardonhealth/status/1425316861176995840

    1. Sir Patrick Vallance on Twitter: “Correcting a statistic I gave at the press conference today, 19 July. About 60% of hospitalisations from covid are not from double vaccinated people, rather 60% of hospitalisations from covid are currently from unvaccinated people.” / Twitter. (n.d.). Retrieved August 4, 2021, from https://twitter.com/uksciencechief/status/1417204235356213252

    1. Benjy Renton on Twitter: “Over half of those who answered ‘wait and see’ to @KFF’s vaccine poll in January have now received the vaccine. So what changed their mind? - Seeing friends and family without side effects—Doctors and healthcare providers encouraging them https://t.co/iRxWp2BLTQ https://t.co/XStHV975Qt” / Twitter. (n.d.). Retrieved August 2, 2021, from https://twitter.com/bhrenton/status/1415163661291819008?s=20

  5. Jul 2021
  6. Jun 2021
    1. Helen McArdle on Twitter: “The good news: An astonishing 98.2% of over-60s in Scotland are now fully vaccinated. That’s an amazing uptake. It doesn’t mean they are 100% protected of course (and especially not when case rates are high) but their risk of hospitalisation/death is cut by over 90% https://t.co/DzAxkpLvcR” / Twitter. (n.d.). Retrieved June 30, 2021, from https://twitter.com/HMcArdleHT/status/1409821893557768195

    1. Jeremy Faust MD MS (ER physician) on Twitter: “Number of Covid cases among children in US: >4 million. Number of Covid deaths in US children: 300-400. Number of kids vaccinated: 7 million. Number of kids who died from vaccine-related myocarditis: Zero. #vaccinate @bhrenton @RickABright @angie_rasmussen @celinegounder” / Twitter. (n.d.). Retrieved June 28, 2021, from https://twitter.com/jeremyfaust/status/1408880589319647243?s=20

    1. Knock, E. S., Whittles, L. K., Lees, J. A., Perez-Guzman, P. N., Verity, R., FitzJohn, R. G., Gaythorpe, K. A. M., Imai, N., Hinsley, W., Okell, L. C., Rosello, A., Kantas, N., Walters, C. E., Bhatia, S., Watson, O. J., Whittaker, C., Cattarino, L., Boonyasiri, A., Djaafara, B. A., … Baguelin, M. (2021). Key epidemiological drivers and impact of interventions in the 2020 SARS-CoV-2 epidemic in England. Science Translational Medicine, eabg4262. https://doi.org/10.1126/scitranslmed.abg4262

  7. May 2021
    1. 2020 has shown that Ioannidis’s claim does not apply to all areas of science. In amazing speed, bio-tech companies were able to make not just one but several successful vaccine’s with high effectiveness. Clearly some sciences are making real progress. On the other hand, other areas of science suggest that Ioannidis’s claims were accurate. For example, the whole literature on single-gene variations as predictors of human behavior has produced mostly false claims. Social psychology has a replication crisis where only 25% of published results could be replicated (OSC, 2015)
    1. Thematic analysis was used to explore the qualitative data captured in the online survey. [22,23] describe thematic analysis as a method that seeks to find patterns, or categories, that emerge from the data, enabling the researcher to organise and provide detailed description.

      This seems like an interesting area to look into further.

      Two cited sources here:

    2. All statistical comparisons were therefore performed using non-parametric methods, to avoid introducing errors based on assumptions of normality in the data. Repeated measures comparisons were performed using the Friedman test, except where specified, with post-hoc pairwise comparisons made using the Friedman-Nemenyi test. Although no direct measure of effect size for the Friedman test is generally recognized, an indirect measure of effect size was obtained using the Kendall’s W-statistic (KW), computed from the Friedman Q value [19,20]. Effect sizes were interpreted as follows: weak: KW< 0.19; moderate 0.20< KW< 0.39; strong 0.4< KW.

      Delve into these techniques.

    1. Adjiwanou, V., Alam, N., Alkema, L., Asiki, G., Bawah, A., Béguy, D., Cetorelli, V., Dube, A., Feehan, D., Fisker, A. B., Gage, A., Garcia, J., Gerland, P., Guillot, M., Gupta, A., Haider, M. M., Helleringer, S., Jasseh, M., Kabudula, C., … You, D. (2020). Measuring excess mortality during the COVID-19 pandemic in low- and lower-middle income countries: The need for mobile phone surveys [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/4bu3q

  8. Apr 2021
    1. The complement of an event AAA in a sample space SSS, denoted AcAcA^c, is the collection of all outcomes in SSS that are not elements of the set AAA. It corresponds to negating any description in words of the event AAA.

      The complement of an event \(A\) in a sample space \(S\), denoted \(A^c\), is the collection of all outcomes in \(S\) that are not elements of the set \(A\). It corresponds to negating any description in words of the event \(A\).


      The complement of an event \(A\) consists of all outcomes of the experiment that do not result in event \(A\).

      Complement formula:

      $$P(A^c)=1-P(A)$$

    1. Jeremy Faust MD MS (ER physician) on Twitter: “Let’s talk about the background risk of CVST (cerebral venous sinus thrombosis) versus in those who got J&J vaccine. We are going to focus in on women ages 20-50. We are going to compare the same time period and the same disease (CVST). DEEP DIVE🧵 KEY NUMBERS!” / Twitter. (n.d.). Retrieved April 15, 2021, from https://twitter.com/jeremyfaust/status/1382536833863651330

    1. Dr Lea Merone MBChB (hons) MPH&TM MSc FAFPHM Ⓥ. ‘I’m an Introvert and Being Thrust into the Centre of This Controversy Has Been Quite Confronting. I’ve Had a Little Processing Time Right Now and I Have a Few Things to Say. I Won’t Repeat @GidMK and His Wonderful Thread but I Will Say 1 This Slander of Us Both Has Been 1/n’. Tweet. @LeaMerone (blog), 29 March 2021. https://twitter.com/LeaMerone/status/1376365651892166658.

  9. Mar 2021
    1. Ashish K. Jha, MD, MPH. (2020, December 12). Michigan vs. Ohio State Football today postponed due to COVID But a comparison of MI vs OH on COVID is useful Why? While vaccines are coming, we have 6-8 hard weeks ahead And the big question is—Can we do anything to save lives? Lets look at MI, OH for insights Thread [Tweet]. @ashishkjha. https://twitter.com/ashishkjha/status/1337786831065264128

    1. Stefan Simanowitz. (2020, November 14). “Sweden hoped herd immunity would curb #COVID19. Don’t do what we did” write 25 leading Swedish scientists “Sweden’s approach to COVID has led to death, grief & suffering. The only example we’re setting is how not to deal with a deadly infectious disease” https://t.co/azOg6AxSYH https://t.co/u2IqU5iwEn [Tweet]. @StefSimanowitz. https://twitter.com/StefSimanowitz/status/1327670787617198087

  10. Feb 2021
  11. Jan 2021
  12. Dec 2020
    1. Stuaert Rtchie [@StuartJRitchie] (2020) This encapsulates the problem nicely. Sure, there’s a paper. But actually read it & what do you find? p-values mostly juuuust under .05 (a red flag) and a sample size that’s FAR less than “25m”. If you think this is in any way compelling evidence, you’ve totally been sold a pup. Twitter. Retrieved from:https://twitter.com/StuartJRitchie/status/1305963050302877697

  13. Nov 2020
  14. Oct 2020
    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

  15. Sep 2020
    1. The lowest value for false positive rate was 0.8%. Allow me to explain the impact of a false positive rate of 0.8% on Pillar 2. We return to our 10,000 people who’ve volunteered to get tested, and the expected ten with virus (0.1% prevalence or 1:1000) have been identified by the PCR test. But now we’ve to calculate how many false positives are to accompanying them. The shocking answer is 80. 80 is 0.8% of 10,000. That’s how many false positives you’d get every time you were to use a Pillar 2 test on a group of that size.

      Take Away: The exact frequency of false positive test results for COVID-19 is unknown. Real world data on COVID-19 testing suggests that rigorous testing regimes likely produce fewer than 1 in 10,000 (<0.01%) false positives, orders of magnitude below the frequency proposed here.

      The Claim: The reported numbers for new COVID-19 cases are overblown due to a false positive rate of 0.8%

      The Evidence: In this opinion article, the author correctly conveys the concern that for large testing strategies, case rates could become inflated if there is (a) a high false positive rate for the test and (b) there is a very low prevalence of the virus within the population. The false positive rate proposed by the author is 0.8%, based on the "lowest value" for similar tests given by a briefing to the UK's Scientific Advisory Group for Emergencies (1).

      In fact, the briefing states that, based on another analysis, among false positive rates for 43 external quality assessments, the interquartile range for false positive rate was 0.8-4.0%. The actual lowest value for false positive rate from this study was 0% (2).

      An upper limit for false positive rate can also be estimated from the number of tests conducted per confirmed COVID-19 case. In countries with low infection rates that have conducted widespread testing, such as Vietnam and New Zealand, at multiple periods throughout the pandemic they have achieved over 10,000 tests per positive case (3). Even if every single positive was false, the false positive rate would be below 0.01%.

      The prevalence of the virus within a population being tested can affect the positive predictive value of a test, which is the likelihood that a positive result is due to a true infection. The author here assumes the current prevalence of COVID-19 in the UK is 1 in 1,000 and the expected rate of positive results is 0.1%. Data from the University of Oxford and the Global Change Data Lab show that the current (Sept. 22, 2020) share of daily COVID-19 tests that are positive in the UK is around 1.7% (4). Therefore, based on real world data, the probability that a patient is positive for the test and does have the disease is 99.4%.

      Sources: (1) https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/895843/S0519_Impact_of_false_positives_and_negatives.pdf

      (2) https://www.medrxiv.org/content/10.1101/2020.04.26.20080911v3.full.pdf+html

      (3) https://ourworldindata.org/coronavirus-data-explorer?yScale=log&zoomToSelection=true&country=USA~DEU~IND~ITA~AUS~VNM~FIN~NZL~GBR&region=World&testsPerCaseMetric=true&interval=smoothed&aligned=true&smoothing=7&pickerMetric=location&pickerSort=asc

      (4) https://ourworldindata.org/coronavirus-data-explorer?zoomToSelection=true&country=USA~DEU~IND~ITA~AUS~VNM~FIN~NZL~GBR&region=World&positiveTestRate=true&interval=smoothed&aligned=true&smoothing=7&pickerMetric=location&pickerSort=asc

  16. Aug 2020
  17. Jul 2020
  18. Jun 2020
    1. The separating hyperplane for each choice i is the vector (a) that satisfies: 770 771 772 773 Meaning that βi is a vector orthogonal to the separating hyperplane in neuron-774 dimensional space, along which position is proportional to the log odds of that correct 775 response: this is the the coding dimension for that correct response

      Makes sense: If Beta is proportional to the log-odds of a correct response, a is the hyperplane that provides the best cutoff, which must be orthogonal. Multiplying two orthogonal vectors yields 0.

  19. May 2020
    1. Efron developed the bias-corrected and accelerated bootstrap (BCa bootstrap) to account for the skew whilst obtaining the central 95% of the distribution.

      Bias-corrected and accelerated bootstrap (BCa boostrap) deals with skewed sample distributions. However; it must be noted that it "may not give very accurate coverage in a small-sample non-parametric situation" (simply said, take caution with small datasets)

    2. We can calculate the 95% CI of the mean difference by performing bootstrap resampling.

      Bootstrap - simple but powerful technique that creates multiple resamples (with replacement) from a single set of observations, and computes the effect size of interest on each of these resamples. It can be used to determine the 95% CI (Confidence Interval).

      We can use bootstrap resampling to obtain measure of precision and confidence about our estimate. It gives us 2 important benefits:

      1. Non-parametric statistical analysis - no need to assume normal distribution of our observations. Thanks to Central Limit Theorem, the resampling distribution of the effect size will approach normality
      2. Easy construction of the 95% CI from the resampling distribution. For 1000 bootstrap resamples of the mean difference, 25th value and 975th value can be used as boundaries of the 95% CI.

      Bootstrap resampling can be used for such an example:

      Computers can easily perform 5000 resamples: