959 Matching Annotations
  1. Last 7 days
    1. Disabato, D., Aurora, P., Sidney, P. G., Taber, J. M., Thompson, C. A., & Coifman, K. (2021). Taking care with self-care during COVID-19: Affect-behavior associations during early stages of the pandemic. PsyArXiv. https://doi.org/10.31234/osf.io/eycmj

    2. Taking care with self-care during COVID-19: Affect-behavior associations during early stages of the pandemic
    3. Although evidence exists for a feedback loop between positive affect and self-care behaviors, it is unclear if findings generalize to the COVID-19 pandemic. A 10-day daily diary was completed by 324 adult participants in the United States during spring 2020 when national stay-at-home orders were in effect. We hypothesized a reciprocal within-person process whereby positive affect increased self-care behaviors (Aim 1) and self-care behaviors increased positive affect (Aim 2). Lagged analyses for Aim 1 indicated that greater negative affect, rather than positive affect, predicted increased self-care behaviors from one day to the next day. For Aim 2, concurrent analyses, but not lagged analyses, indicated self-care behaviors was associated with more positive affect and less negative affect afterwards. We discuss the ways negative affect might function differently than normal during stressful environments and conclude self-care behaviors continue to have only a short-term (within a day) impact on positive and negative affect.
    4. 10.31234/osf.io/eycmj
    5. 2021-04-08

    1. The BMJ. (2021, April 8). “These data represent a remarkable research resource and illustrate how covid-19 has fostered open science” @jsross119 @BHFDataScience https://t.co/i3ddpBqq7j [Tweet]. @bmj_latest. https://twitter.com/bmj_latest/status/1380062868746469377

    2. 2021-04-08

    3. "These data represent a remarkable research resource and illustrate how covid-19 has fostered open science" @jsross119 @BHFDataScience
    1. Carlos del Rio. (2021, April 7). U.K. variant now dominant form of COVID in US ⁦@CDCDirector⁩ As predicted B.1.1.7 is now the predominant SARS-CoV-2 strain in the US. Let’s remember it is much more transmissible and likely also more severe. Vaccines do cover it. ⁦@ajc⁩ https://t.co/Wc4oaYkxqR [Tweet]. @CarlosdelRio7. https://twitter.com/CarlosdelRio7/status/1379816377356333057

    2. 2021-04-07

    3. U.K. variant now dominant form of COVID in US ⁦@CDCDirector⁩ As predicted B.1.1.7 is now the predominant SARS-CoV-2 strain in the US. Let’s remember it is much more transmissible and likely also more severe. Vaccines do cover it. ⁦@ajc
    1. Joshua Weitz. (2021, April 7). The same approaches that work against SARS-CoV-2 work against variants like B.1.1.7: Mask Avoid indoor gatherings Socialize outdoors And, reinforces the need to continue to scale-up vaccine administration to all 16+. [Tweet]. @joshuasweitz. https://twitter.com/joshuasweitz/status/1379820197696180233

    2. 2021-04-07

    3. The same approaches that work against SARS-CoV-2 work against variants like B.1.1.7: Mask Avoid indoor gatherings Socialize outdoors And, reinforces the need to continue to scale-up vaccine administration to all 16+.
    1. Department of State. (2021, April 6). .@SecBlinken: Stopping COVID-19 is the Biden-Harris Administration’s number one priority. Otherwise, the coronavirus will keep circulating in our communities, threatening people’s lives and livelihoods, holding our economy back. Https://t.co/uk20myyICI [Tweet]. @StateDept. https://twitter.com/StateDept/status/1379554511606280192

    2. 2021-04-06

    3. .@SecBlinken: Stopping COVID-19 is the Biden-Harris Administration’s number one priority. Otherwise, the coronavirus will keep circulating in our communities, threatening people’s lives and livelihoods, holding our economy back.
    1. Prof. Devi Sridhar. (2021, April 8). Biden-Harris Administration gets that it is COVID-19 itself hurting the economy (the virus circulating, not just the restrictions). Stopping COVID-19 is best way to get people’s lives & livelihoods back. [Tweet]. @devisridhar. https://twitter.com/devisridhar/status/1380095008787857409

    2. 2021-04-08

    3. US now explicitly heading in same direction as http://E.Asia/Pacific. Call it whatever you want: max suppression, an 'elimination' strategy, a public health 'measles' approach, Zero COVID, no COVID, or simply just stopping the circulation of COVID-19. Influential & bold step.
    4. Biden-Harris Administration gets that it is COVID-19 itself hurting the economy (the virus circulating, not just the restrictions). Stopping COVID-19 is best way to get people's lives & livelihoods back.
  2. Apr 2021
    1. World Health Organization (WHO). (2020, March 28). FACT: #COVID19 is NOT airborne. The #coronavirus is mainly transmitted through droplets generated when an infected person coughs, sneezes or speaks. To protect yourself: -Keep 1m distance from others -disinfect surfaces frequently -wash/rub your 👐 -avoid touching your 👀👃👄 https://t.co/fpkcpHAJx7 [Tweet]. @WHO. https://twitter.com/WHO/status/1243972193169616898

    2. Watch this short animation to learn more about #COVID19, how it spreads and how to protect yourself against it. #coronavirus
    3. FACT: #COVID19 is NOT airborne. The #coronavirus is mainly transmitted through droplets generated when an infected person coughs, sneezes or speaks. To protect yourself: -keep 1m distance from others -disinfect surfaces frequently -wash/rub your -avoid touching your
    4. 2020-03-28

    1. Atomsk’s Sanakan. (2021, March 27). 1/J John Ioannidis published an article defending his low estimate of COVID-19’s fatality rate. It contains so many distortions that I’ll try something I’ve never done on Twitter for a paper: Go thru distortions page-by-page. This will take awhile. 😑 https://t.co/4wonxc6MFg https://t.co/AyV5RiwQnh [Tweet]. @AtomsksSanakan. https://twitter.com/AtomsksSanakan/status/1375935382139834373

    2. 2021-03-27

    3. Unlikely policies caused more excess deaths; non-COVID-19 deaths dropped. https://sciencedirect.com/science/article/pii/S0091743520303625… https://academic.oup.com/aje/advance-article/doi/10.1093/aje/kwab062/6169297… https://medrxiv.org/content/10.1101/2020.08.28.20183699v3… https://bloomberg.com/opinion/articles/2020-09-17/child-mortality-covid-19-lockdowns-may-have-saved-kids-lives… https://twitter.com/jburnmurdoch/status/1354158357754601472… https://twitter.com/tylerblack32/status/1367239480130740224… https://twitter.com/GidMK/status/1371045429232631810… https://onlinelibrary.wiley.com/doi/10.1111/eci.13554
    4. Most infected people increase antibody levels. In the general population that antibody increase persists for ≥6 months in most people, besides with some assays like Abbott. https://twitter.com/AtomsksSanakan/status/1301777937008652294… https://twitter.com/AtomsksSanakan/status/1362918654141202432… https://twitter.com/AtomsksSanakan/status/1356806587273379840… https://onlinelibrary.wiley.com/doi/10.1111/eci.13554
    5. And "non-participating invitees" are less likely to be infected, so Ioannidis was wrong. We don't the response rate for his Santa Clara study, since he has no targeted sample. https://twitter.com/AtomsksSanakan/status/1363989598498676742… https://medrxiv.org/content/10.1101/2020.08.24.20181206v1… https://twitter.com/AtomsksSanakan/status/1341296083767599104… https://medrxiv.org/content/10.1101/2020.11.02.20221309v1.full.pdf
    6. So his "[n]o consensus" claim is misleading. There's an evidence-based consensus (outside of Ioannidis) that those samples could *luckily* match, but are not designed to + are thus less likely to. Covered in another thread: https://twitter.com/AtomsksSanakan/status/1341288191249297408… https://onlinelibrary.wiley.com/doi/10.1111/eci.13554
    7. Scientists know methods that get representative samples that are more likely to match the general population; they applied them to diseases before COVID-19. Ioannidis discards those methods, + relies on non-representative sampling luckily matching.
    8. The same point applies to seroprevalence studies. Non-representative sampling might *luckily* get results that match the overall population. But representative sampling is *designed* to be more likely to match the population. https://academic.oup.com/cid/advance-article/doi/10.1093/cid/ciaa1868/6041690?login=true
    9. Suppose you want to know what proportion of people in a city like dogs. You could survey people in 1 building. By luck the percentage you get might match the percentage you would get for the city overall. But you didn't design the survey to make that more likely.
    10. And now in his discussion section, Ioannidis turns to the core point. I'll spend a few tweets on this because this is *the* central pillar of his position, and is how he's been misleading millions of people for over a year. https://twitter.com/AtomsksSanakan/status/1375943659779198976… https://onlinelibrary.wiley.com/doi/10.1111/eci.13554
    11. #3 is worst because it extrapolates from inaccurate samples, under-estimating IFR. Yet that's what Ioannidis chooses to do + uses Bobrovitz for. #1 makes sense; that's what "Meyerowitz-Katz" (@GidMK) did. But if you must have data for policy or planning, #2 can work.
    12. There are at least three approaches to dealing with areas lacking representative samples: 1) exclude those areas + wait for data 2) use regions with representative samples to extrapolate over 3) include non-representative samples from those areas https://onlinelibrary.wiley.com/doi/10.1111/eci.13554
    13. I'll leave to others (maybe @GidMK?) to discuss the meta-analysis details. But I can say Ioannidis under-estimates seroprevalence-based IFR in southeast Asian countries such as Japan + South Korea. https://twitter.com/AtomsksSanakan/status/1364464684548644869… https://twitter.com/AtomsksSanakan/status/1364466754337071106… https://onlinelibrary.wiley.com/doi/10.1111/eci.13554
    14. His adjustment makes no sense since it's already implicit in test adjustments for sensitivity. And IgA assessment isn't required, given IgG. https://thelancet.com/action/showPdf?pii=S0140-6736%2821%2900238-5… (table 2) https://ncbi.nlm.nih.gov/pmc/articles/PMC7882210/… https://bmj.com/content/370/bmj.m3364/rapid-responses… https://twitter.com/AtomsksSanakan/status/891040491214688257… https://onlinelibrary.wiley.com/doi/10.1111/eci.13554
    15. - the New York sample under-estimated IFR (see 18/J) - low response rate biases seroprevalence up, under-estimating IFR https://twitter.com/AtomsksSanakan/status/1366078699964149763… - the IFR in Italy was likely over-estimated, due to lower sensitivity of the Abbott assay https://onlinelibrary.wiley.com/doi/10.1111/eci.13554
    16. Why the following studies were non-representative: - Luxembourg: non-probabilistic selection step https://twitter.com/AtomsksSanakan/status/1341298484708839425… page 6: https://medrxiv.org/content/10.1101/2020.05.11.20092916v1.full.pdf… - New York: sampled shoppers https://ncbi.nlm.nih.gov/pmc/articles/PMC7454696/… https://twitter.com/AtomsksSanakan/status/1341303286272413696… https://onlinelibrary.wiley.com/doi/10.1111/eci.13554
    17. Taking a break for a bit. The thread so far covers *less than a page* of the distortions + misleading statements in Ioannidis' paper. I hope people understand why many experts in this field no longer invest time in addressing his nonsensical under-estimating of IFR.
    18. - Kenya used non-representative sampling on blood donors https://science.sciencemag.org/content/371/6524/79… https://twitter.com/AtomsksSanakan/status/1341288191249297408… - Due to co-linearity, the nationwide study ICCRT cites supplants Rio Grande do Sul https://nature.com/articles/s41591-020-0992-3… https://twitter.com/GidMK/status/1283232054646173696… https://imperial.ac.uk/media/imperial-college/medicine/mrc-gida/2020-10-29-COVID19-Report-34-supplement.pdf
    19. ICCRT: https://imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-34-ifr/… https://twitter.com/AtomsksSanakan/status/1343877612914012167… - Ioannidis' IFRs for LA County + Scotland are impossible: https://twitter.com/AtomsksSanakan/status/1369430446271037449… https://twitter.com/AtomsksSanakan/status/1369641571247923203… - Gangelt over-estimated the seroprevalence: https://twitter.com/AtomsksSanakan/status/1329620151571001344… https://onlinelibrary.wiley.com/doi/10.1111/eci.13554
    20. It's 0.31% IFR is unreliable anyway since, for example, the studies for Santa Clara, New York (both), + Chelsea used non-representative sampling. Miami-Dade was wrong. https://ncbi.nlm.nih.gov/pmc/articles/PMC7499676/… https://twitter.com/AtomsksSanakan/status/1363989598498676742… https://twitter.com/AtomsksSanakan/status/1341306679812644865
    21. The "IFR = 0.31%" study Ioannidis mentioned is below. @LeaMerone + @GidMK excluded it because "did not allow for an estimate of confidence bounds" https://sciencedirect.com/science/article/pii/S1201971220321809… "to estimate an overall IFR for the United States of 0.863 percent" https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3590771
    22. The "low IFR" Ioannidis references is one he inferred from a Los Angeles County study. That IFR is impossible since it requires more people are infected than actually exist. https://jamanetwork.com/journals/jama/fullarticle/2766367… https://twitter.com/AtomsksSanakan/status/1369641571247923203… https://onlinelibrary.wiley.com/doi/10.1111/eci.13554
    23. Ioannidis co-authored 2009 PRISMA guidelines that stated one should competently assess studies for risk of bias. @LeaMerone + @GidMK did that. Ioannidis didn't, letting in non-representative samples https://bmj.com/content/339/bmj.b2700… https://twitter.com/AtomsksSanakan/status/1315313977539334144…
    24. Seroprevalence-based IFR was ~0.76% in @LeaMerone + @GidMK's paper, when they focused on seroprevalence studies with a low risk of bias. Ioannidis conveniently leaves that out. https://sciencedirect.com/science/article/pii/S1201971220321809… https://twitter.com/AtomsksSanakan/status/1286771217274482689… https://onlinelibrary.wiley.com/doi/10.1111/eci.13554
    25. Ioannidis' exclusion fits with him under-estimating IFR by using non-representative samples in areas that under-estimate COVID deaths. The WHO + the USA's CDC know better, and so rely on Levin et al.: https://web.archive.org/web/20210324195745/https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html… https://twitter.com/AtomsksSanakan/status/1374617361194565634… https://link.springer.com/article/10.1007/s10654-020-00698-1
    26. With that framework in place, let's start with the page-by-page review of Ioannidis' paper: Ioannidis excludes @GidMK + @BillHanage's paper Levin et al., because it focused on specific countries. https://twitter.com/AtomsksSanakan/status/1336442679689965570… https://link.springer.com/article/10.1007/s10654-020-00698-1… https://onlinelibrary.wiley.com/doi/10.1111/eci.13554
    27. So in this thread, *keep this in mind*: Ioannidis has to keep non-representative samples in, because representative samples show an IFR incompatible with his position. That's his main game, + what he often distracts from https://twitter.com/AtomsksSanakan/status/1375343648129359880…
    28. - he could just wait for representative sampling in less hard hit areas - areas often looked less hard hit because they under-estimated COVID-19 deaths, so including them under-estimates IFR etc. https://bmj.com/content/370/bmj.m2859… https://twitter.com/AtomsksSanakan/status/1369873446642069504… https://medrxiv.org/content/10.1101/2021.01.27.21250604v1.full-text…
    29. Ioannidis defends his use of non-representative samples. But his defense fails. For example: - non-representative samples are still unreliable - he uses non-representative samples even in hard hit areas https://twitter.com/AtomsksSanakan/status/1341285257644027904… https://who.int/bulletin/volumes/99/1/20-265892/en/
    30. One can over-estimate seroprevalence (+ thus under-estimate IFR) by measuring seroprevalence in a sample that does not represent the general population, and then extrapolating that sample to the general population. Ioannidis does this.
    31. Seroprevalence studies (serosurveys) measure antibody levels to estimate the number of infected people. Dividing COVID-19 deaths by that number of infected people gives a seroprevalence-based IFR.
    32. Some context: Infection fatality rate, or IFR, is the proportion of people infected with the virus SARS-CoV-2 who die of the disease COVID-19. There are many IFR estimates, including some from Ioannidis. https://twitter.com/AtomsksSanakan/status/1343836703996440577… https://institutefordiseasemodeling.github.io/nCoV-public/analyses/first_adjusted_mortality_estimates_and_risk_assessment/2019-nCoV-preliminary_age_and_time_adjusted_mortality_rates_and_pandemic_risk_assessment.html
    33. John Ioannidis published an article defending his low estimate of COVID-19's fatality rate. It contains so many distortions that I'll try something I've never done on Twitter for a paper: Go thru distortions page-by-page. This will take awhile. https://onlinelibrary.wiley.com/doi/10.1111/eci.13554
    1. Chen, X., Chen, Z., Azman, A. S., Deng, X., Sun, R., Zhao, Z., Zheng, N., Chen, X., Lu, W., Zhuang, T., Yang, J., Viboud, C., Ajelli, M., Leung, D. T., & Yu, H. (2021). Serological evidence of human infection with SARS-CoV-2: A systematic review and meta-analysis. The Lancet Global Health, 0(0). https://doi.org/10.1016/S2214-109X(21)00026-7

    2. 2021-03-08

    3. BackgroundA rapidly increasing number of serological surveys for antibodies to SARS-CoV-2 have been reported worldwide. We aimed to synthesise, combine, and assess this large corpus of data.MethodsIn this systematic review and meta-analysis, we searched PubMed, Embase, Web of Science, and five preprint servers for articles published in English between Dec 1, 2019, and Dec 22, 2020. Studies evaluating SARS-CoV-2 seroprevalence in humans after the first identified case in the area were included. Studies that only reported serological responses among patients with COVID-19, those using known infection status samples, or any animal experiments were all excluded. All data used for analysis were extracted from included papers. Study quality was assessed using a standardised scale. We estimated age-specific, sex-specific, and race-specific seroprevalence by WHO regions and subpopulations with different levels of exposures, and the ratio of serology-identified infections to virologically confirmed cases. This study is registered with PROSPERO, CRD42020198253.Findings16 506 studies were identified in the initial search, 2523 were assessed for eligibility after removal of duplicates and inappropriate titles and abstracts, and 404 serological studies (representing tests in 5 168 360 individuals) were included in the meta-analysis. In the 82 studies of higher quality, close contacts (18·0%, 95% CI 15·7–20·3) and high-risk health-care workers (17·1%, 9·9–24·4) had higher seroprevalence than did low-risk health-care workers (4·2%, 1·5–6·9) and the general population (8·0%, 6·8–9·2). The heterogeneity between included studies was high, with an overall I2 of 99·9% (p<0·0001). Seroprevalence varied greatly across WHO regions, with the lowest seroprevalence of general populations in the Western Pacific region (1·7%, 95% CI 0·0–5·0). The pooled infection-to-case ratio was similar between the region of the Americas (6·9, 95% CI 2·7–17·3) and the European region (8·4, 6·5–10·7), but higher in India (56·5, 28·5–112·0), the only country in the South-East Asia region with data.InterpretationAntibody-mediated herd immunity is far from being reached in most settings. Estimates of the ratio of serologically detected infections per virologically confirmed cases across WHO regions can help provide insights into the true proportion of the population infected from routine confirmation data.
    4. 10.1016/S2214-109X(21)00026-7
    5. Serological evidence of human infection with SARS-CoV-2: a systematic review and meta-analysis
    1. Ioannidis, J. P. A. (n.d.). Reconciling estimates of global spread and infection fatality rates of COVID-19: An overview of systematic evaluations. European Journal of Clinical Investigation, n/a(n/a), e13554. https://doi.org/10.1111/eci.13554

    2. 2021-03-26

    3. Background Estimates of community spread and infection fatality rate (IFR) of COVID‐19 have varied across studies. Efforts to synthesize the evidence reach seemingly discrepant conclusions. Methods Systematic evaluations of seroprevalence studies that had no restrictions based on country and which estimated either total number of people infected and/or aggregate IFRs were identified. Information was extracted and compared on eligibility criteria, searches, amount of evidence included, corrections/adjustments of seroprevalence and death counts, quantitative syntheses and handling of heterogeneity, main estimates, and global representativeness. Results Six systematic evaluations were eligible. Each combined data from 10‐338 studies (9‐50 countries), because of different eligibility criteria. Two evaluations had some overt flaws in data, violations of stated eligibility criteria, and biased eligibility criteria (e.g. excluding studies with few deaths) that consistently inflated IFR estimates. Perusal of quantitative synthesis methods also exhibited several challenges and biases. Global representativeness was low with 78‐100% of the evidence coming from Europe or the Americas; the two most problematic evaluations considered only 1 study from other continents. Allowing for these caveats, 4 evaluations largely agreed in their main final estimates for global spread of the pandemic and the other two evaluations would also agree after correcting overt flaws and biases. Conclusions All systematic evaluations of seroprevalence data converge that SARS‐CoV‐2 infection is widely spread globally. Acknowledging residual uncertainties, the available evidence suggests average global IFR of ~0.15% and ~1.5‐2.0 billion infections by February 2021 with substantial differences in IFR and in infection spread across continents, countries, and locations.
    4. 10.1111/eci.13554
    5. Reconciling estimates of global spread and infection fatality rates of COVID‐19: an overview of systematic evaluations
    1. Health Nerd. (2021, March 28). Recently, Professor John Ioannidis, most famous for his meta-science and more recently COVID-19 work, published this article in the European Journal of Clinical Investigation It included, among other things, a lengthy personal attack on me Some thoughts 1/n https://t.co/JGfUrpJXh2 [Tweet]. @GidMK. https://twitter.com/GidMK/status/1376304539897237508

    2. 2021-03-28

    3. As to the paper itself? There are obviously more issues – covered here in depth by @AtomsksSanakan – but oddly enough there are also places where Prof Ioannidis and I agree about our paper
    4. But for anyone reading this who is mentoring PhD students, particularly people at Stanford, I would suggest strongly that you check in and assure them that you do indeed find their opinions and perspectives useful
    5. I will be writing to the European Journal of Clinical Investigation. Given that the immediate past Editor In Chief was one professor John Ioannidis, I’m not sure it will do much good, but at least I will have my say
    6. But the point is that we should not have to have Big Fancy Professors on our paper for it to be considered on its own merits. I’m sure we could have twisted our colleagues’ arms, but we did not think that a professor would stoop to our PhDs as a means of attack
    7. I could point out that our paper was reviewed by several very senior epis before we submitted it (including one of the most senior epis in Australia), but that they did not feel they contributed enough to add their names – perhaps this would’ve saved me a tongue-lashing
    8. This issue is not a new one by a long shot. @hertzpodcast covered the issues that PhD students face several times in great detail – I recommend you listen
    9. Imagine reading this as a PhD student at Stanford. This is a senior faculty member telling these students that no matter what work they do, their opinions will always come second to professors Not what I would hope the scientific discourse to be
    10. I may have the wherewithal to defend myself, and I’ll be writing to the journal, but the implication that PhD students have no place in scientific discourse, that their papers are worthless scientifically will, I think, have far greater ramifications
    11. But imagine, for a second, that I had not been in the news a bit and grown a social media platform. Imagine I was one of 1,000s of faceless PhD students watching a tenured professor at Stanford publicly defame one of their comrades It’s quite chilling
    12. I appreciate the many wonderful people who have come to my defense against these attacks, but in all honesty it’s not me that I’m worried about. For better or worse, I have a large platform, and I’m not in any huge danger from a professor being publicly mean to me
    13. It is also worth noting that while I am still doing my PhD, I have been working in public health for more than half a decade, because often the more sought-after qualification is an MPH not PhD
    14. I make no secret of my junior status (it’s there in my twitter bio and every paper I publish), but to say that my research is flawed because of it is a remarkable piece of gatekeeping and I think really quite harmful
    15. For my followers who don’t publish academic research, it’s worth noting that these attacks not only were written by the author, but approved by at least one editor and (usually) 2-3 peers as well
    16. Now, to the personal attacks I must admit, I was quite shocked to read this published in a scientific paper I’m not going to go over them, but please do have a read in the paper itself (appendix 1)
    17. But overall, I think that Prof Ioannidis' review really shows the issues with having people who have staked their reputation on an issue author perspective pieces on the issue. We all tend to think that our own research is the best
    18. I would argue that one of the biggest STRENGTHS of our meta-analysis was the time we spent EXCLUDING biased research, because as has now become fairly obvious these studies often overestimate seroprevalence in a population
    19. There are also parts of this paper that are bizarre. It is, for example, not a strength of meta-research to include MORE studies. Indeed, the phrase “garbage in garbage out” is commonly used to describe analyses that do not attempt to exclude poorly-done studies
    20. The paper which he co-authored is, I suppose, a matter for discussion – perhaps @LeaMerone and I were presumptuous in reading “selection bias is likely...” as an explicit warning against extrapolating to the entire population of LA County
    21. For example, this tabulated estimate includes studies that we reference elsewhere in the review, with 5 of these estimates ~included in our meta-analysis~ It would actually be BAD scientific practice to include these figures twice!
    22. I’m not sure how it is possible to say that something is “overtly biased” when it is transparent and open, but nevertheless there are quite obvious explanations for all of these things (that we give in the paper)
    23. That being said, I disagree with many of these statements. For example, this passage argues that we excluded studies in “overtly biased ways” with these three pieces of research
    24. Now, one thing to note is that these are judgement calls rather than actual scientific critiques. We laid out our methodology quite transparently – saying that this is “implausible” is an opinion, not a fact
    25. He spends quite a bit of time on my and @LeaMerone's paper, arguing that we “cherry-picked” evidence to suit our conclusions and that our analysis methods are “overtly implausible”
    26. The author looks at each review and discusses his view on their limitations and successes, then concludes that the best estimate is his own
    27. So I don't know if the primary purpose of this paper makes sense But what is it exactly? Well, it’s mostly a review of systematic reviews
    28. On the other side of the coin, there’s evidence that in some countries that the death figures from COVID-19 may underestimate the true toll by an order of magnitude (or more!) https://bmj.com/content/372/bmj.n334
    29. For example, this recent systematic review of seroprevalence studies found that even after including more than 400 pieces of research total there was insufficient evidence to infer a truly global estimate
    30. The problem with trying to work out a global IFR – i.e. the total number of people dead/infected for COVID-19 across the world – is that both the death AND infection data is scant in most places in the world
    31. Moreover, I personally find the entire focus of the piece strange. I do not think it is reasonably possible to accurately estimate the GLOBAL IFR (infection fatality rate/ratio) of COVID-19
    32. The article itself is here, and honestly it’s a bit of an odd piece. If I were to commission a review on the small number of SR/MAs on the COVID-19 IFR, I’d probably want it to be written by someone who hadn’t authored one of the 6
    33. Recently, Professor John Ioannidis, most famous for his meta-science and more recently COVID-19 work, published this article in the European Journal of Clinical Investigation It included, among other things, a lengthy personal attack on me Some thoughts
    34. Carl T. Bergstrom. (2021, March 28). In his latest paper about COVID infection fatality rates, John Ioannidis does not address the critiques from @GidMK, but instead engages in the most egregious gatekeeping that I have ever seen in a scientific paper. Https://t.co/P08sFIovD6 [Tweet]. @CT_Bergstrom. https://twitter.com/CT_Bergstrom/status/1376080062131269634

    35. 2021-03-28

    36. In this thread, the researcher in question, @GidMK, offers his thoughts on the whole affair.
    37. Honestly, the biggest takeaway in all of this is that when you're reviewing a paper, you can't afford to merely skim the appendix.
    38. Anyway, you can read it for yourself. It's published in the journal for which Ioannidis previously served as Editor in Chief. https://onlinelibrary.wiley.com/doi/10.1111/eci.13554… Therein John claims the IFR for COVID is 0.15%. By official counts, 0.166% of the US population has already died of COVID.
    39. And as for non-PhD authors? I wrote four papers as a PhD student in which no author had an advanced degree. Theor. Pop. Biol., Phil. Trans. Royal Society, PNAS, Genetics. Cited 86, 116, 178, and 214 times respectively. Maybe they're all crap, but not b/c of my degree status.
    40. The condescension and hypocrisy here is mind-boggling.
    41. John's defenders have done this in the past, but I'm stunned that he'd stoop to the same. Science doesn't work like that, to say the least. Gideon's degree status is irrelevant and in the entirety of my career I've never seen this issue raised in a scientific paper before.
    42. In his latest paper about COVID infection fatality rates, John Ioannidis does not address the critiques from @GidMK, but instead engages in the most egregious gatekeeping that I have ever seen in a scientific paper.
  3. Mar 2021
    1. The Data Visualizations Behind COVID-19 Skepticism. (n.d.). The Data Visualizations Behind COVID-19 Skepticism. Retrieved March 27, 2021, from http://vis.csail.mit.edu/covid-story/

    2. 2021-03-01

    3. How do COVID-19 skeptics use public health data and social media to advocate for reopening the economy and against mask mandates?We studied half a million tweets, over 41,000 visualizations, and spent six months lurking in anti-mask Facebook groups.Here’s what we found.
    4. The Data Visualizations Behind COVID-19 Skepticism
    1. Jones, M. I., Sirianni, A. D., & Fu, F. (2021). Polarization, Abstention, and the Median Voter Theorem. ArXiv:2103.12847 [Physics]. http://arxiv.org/abs/2103.12847

    2. 2021-03-23

    3. 2103.12847
    4. The median voter theorem has long been the default model of voter behavior and candidate choice. While contemporary work on the distribution of political opinion has emphasized polarization and an increasing gap between the "left" and the "right" in democracies, the median voter theorem presents a model of anti-polarization: competing candidates move to the center of the ideological distribution to maximize vote share, regardless of the underlying ideological distribution of voters. These anti-polar results, however, largely depend on the "singled-peakedness" of voter preferences, an assumption that is rapidly loosing relevance in the age of polarization. This article presents a model of voter choice that examines three potential mechanisms that can undermine this finding: a relative cost of voting that deters voters who are sufficiently indifferent to both candidates, ideologically motivated third-party alternatives that attract extreme voters, and a bimodal distribution of voter ideology. Under reasonable sets of conditions and empirically observed voter opinion distributions, these mechanisms can be sufficient to cause strategically-minded candidates to fail to converge to the center, or to even become more polarized than their electorate.
    5. Polarization, Abstention, and the Median Voter Theorem
    1. Conley, D., & Johnson, T. (2021). Opinion: Past is future for the era of COVID-19 research in the social sciences. Proceedings of the National Academy of Sciences, 118(13). https://doi.org/10.1073/pnas.2104155118

    2. Over the last few decades, social scientists have experienced the causal revolution, the replication crisis, and, now in just a matter of months, another epoch: the era of coronavirus disease 2019 (COVID-19) research. According to Google Scholar, roughly 3.55 million COVID-19–related articles have appeared to date. That amounts to about 9,726 articles per day, or, roughly, one article every 9 seconds. Many of these articles are in the social sciences—that is, concerned not directly with medical outcomes but rather with COVID-19’s impact on social, behavioral, and economic outcomes.
    3. 10.1073/pnas.2104155118
    4. Opinion: Past is future for the era of COVID-19 research in the social sciences
    5. 2021-03-30

    1. Lawton, G. (n.d.). US refuses to extend time between coronavirus vaccine doses. New Scientist. Retrieved March 24, 2021, from https://www.newscientist.com/article/mg24933263-700-us-refuses-to-extend-time-between-coronavirus-vaccine-doses/

    2. 2021-03-17

    3. THE UK’s controversial decision to increase the time between covid-19 vaccine doses has been thrust back under the spotlight after the US hasn’t followed suit, amid warnings that the strategy may backfire. However, the UK is no longer alone in its decision, with Canada and Germany both choosing to follow a similar plan.
    4. US refuses to extend time between coronavirus vaccine doses
    1. Jess Rohmann. (2021, March 16). New @PEI_Germany report provides much needed clarity to the #AstraZeneca “pause” in Germany. Not yet available in English. I will try to summarize. /Thread https://t.co/Ev9p2TOdfD [Tweet]. @JLRohmann. https://twitter.com/JLRohmann/status/1371833745272156163

    2. 2021-03-16

    3. Update: English report now available here: https://pei.de/SharedDocs/Downloads/EN/newsroom-en/hp-news/faq-temporary-suspension-astrazeneca.pdf?__blob=publicationFile&v=5
    4. Since #AstraZeneca was the primary vaccine strategy in Germany and much of Europe, every day we wait is a frustrating one. I do appreciate the transparency of this report. Wish these numbers had been released yesterday though!