8,902 Matching Annotations
  1. Oct 2020
    1. 2020-10-16

    2. Maia, H. P., Ferreira, S. C., & Martins, M. L. (2020). Adaptive network approach for emergence of societal bubbles. ArXiv:2010.08635 [Nlin, Physics:Physics]. http://arxiv.org/abs/2010.08635

    3. 2010.08635
    4. Far beyond its relevance for commercial and political marketings, opinion formation and decision making processes are central for representative democracy, government functioning, and state organization. In the present report, a stochastic agent-based model is investigated. The model assumes that bounded confidence and homophily mechanisms drive both opinion dynamics and social network evolution through either rewiring or breakage of social contacts. In addition to the classical transition from global consensus to opinion polarization, our main findings are (i) a cascade of fragmentation of the social network into echo chambers (modules) holding distinct opinions and rupture of the bridges interconnecting these modules as the tolerance for opinion differences increases. There are multiple surviving opinions associated to these modules within which consensus is formed; and (ii) the adaptive social network exhibits a hysteresis-like behavior characterized by irreversible changes in its topology as the opinion tolerance cycles from radicalization towards consensus and backward to radicalization.
    5. Adaptive network approach for emergence of societal bubbles
    1. 2020-10-19

    2. Brañas-Garza, P., Jorrat, D., Espín, A. M., & Sánchez, A. (2020). Paid and hypothetical time preferences are the same: Lab, field and online evidence. ArXiv:2010.09262 [Physics]. http://arxiv.org/abs/2010.09262

    3. 2010.09262
    4. The use of hypothetical instead of real decision-making incentives remains under debate after decades of economic experiments. Standard incentivized experiments involve substantial monetary costs due to participants' earnings and often logistic costs as well. In time preferences experiments, which involve future payments, real payments are particularly problematic. Since immediate rewards frequently have lower transaction costs than delayed rewards in experimental tasks, among other issues, (quasi)hyperbolic functional forms cannot be accurately estimated. What if hypothetical payments provide accurate data which, moreover, avoid transaction cost problems? In this paper, we test whether the use of hypothetical - versus real - payments affects the elicitation of short-term and long-term discounting in a standard multiple price list task. One-out-of-ten participants probabilistic payment schemes are also considered. We analyze data from three studies: a lab experiment in Spain, a well-powered field experiment in Nigeria, and an online extension focused on probabilistic payments. Our results indicate that paid and hypothetical time preferences are mostly the same and, therefore, that hypothetical rewards are a good alternative to real rewards. However, our data suggest that probabilistic payments are not.
    5. Paid and hypothetical time preferences are the same: Lab, field and online evidence
    1. Outbreak.info. (n.d.). Outbreak.Info. Retrieved October 25, 2020, from https://outbreak.info/

    2. In response to the current outbreak of SARS-CoV-2 (the virus that causes COVID-19), researchers worldwide have been generating and openly sharing data, publications, reagents, code, protocols, and more. Broad sharing of these research resources improves the speed and efficiency of science. Unfortunately, there are no uniform standards and repositories for collecting all this information in one place. Outbreak.info focuses on aggregating all SARS-CoV-2 / COVID-19 information into a single site. We focus on making the metadata about these resources more standardized, on creating web interfaces to make the resources more findable, and on a few focused data integration efforts to make data more usable.
    1. 2020-10-16

    2. Dennis, A., Wamil, M., Kapur, S., Alberts, J., Badley, A. D., Decker, G. A., Rizza, S. A., Banerjee, R., Banerjee, A., & Investigators, O. behalf of the C. study. (2020). Multi-organ impairment in low-risk individuals with long COVID. MedRxiv, 2020.10.14.20212555. https://doi.org/10.1101/2020.10.14.20212555

    3. Background: Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) infection has disproportionately affected older individuals and those with underlying medical conditions. Research has focused on short-term outcomes in hospital, and single organ involvement. Consequently, impact of long COVID (persistent symptoms three months post-infection) across multiple organs in low-risk individuals is yet to be assessed. Methods: An ongoing prospective, longitudinal, two-centre, observational study was performed in individuals symptomatic after recovery from acute SARS-CoV-2 infection. Symptoms and organ function (heart, lungs, kidneys, liver, pancreas, spleen) were assessed by standardised questionnaires (EQ-5D-5L, Dyspnoea-12), blood investigations and quantitative magnetic resonance imaging, defining single and multi-organ impairment by consensus definitions. Findings: Between April and September 2020, 201 individuals (mean age 44 (SD 11.0) years, 70% female, 87% white, 31% healthcare workers) completed assessments following SARS-CoV-2 infection (median 140, IQR 105-160 days after initial symptoms). The prevalence of pre-existing conditions (obesity: 20%, hypertension: 6%; diabetes: 2%; heart disease: 4%) was low, and only 18% of individuals had been hospitalised with COVID-19. Fatigue (98%), muscle aches (88%), breathlessness (87%), and headaches (83%) were the most frequently reported symptoms. Ongoing cardiorespiratory (92%) and gastrointestinal (73%) symptoms were common, and 42% of individuals had ten or more symptoms. There was evidence of mild organ impairment in heart (32%), lungs (33%), kidneys (12%), liver (10%), pancreas (17%), and spleen (6%). Single (66%) and multi-organ (25%) impairment was observed, and was significantly associated with risk of prior COVID-19 hospitalisation (p<0.05). Interpretation: In a young, low-risk population with ongoing symptoms, almost 70% of individuals have impairment in one or more organs four months after initial symptoms of SARS-CoV-2 infection. There are implications not only for burden of long COVID but also public health approaches which have assumed low risk in young people with no comorbidities.
    4. 10.1101/2020.10.14.20212555
    5. Multi-organ impairment in low-risk individuals with long COVID
    1. 2020-10-16

    2. Brown, R. C. H., Kelly, D., Wilkinson, D., & Savulescu, J. (2020). The scientific and ethical feasibility of immunity passports. The Lancet Infectious Diseases, 0(0). https://doi.org/10.1016/S1473-3099(20)30766-0

    3. There is much debate about the use of immunity passports in the response to the COVID-19 pandemic. Some have argued that immunity passports are unethical and impractical, pointing to uncertainties relating to COVID-19 immunity, issues with testing, perverse incentives, doubtful economic benefits, privacy concerns, and the risk of discriminatory effects. We first review the scientific feasibility of immunity passports. Considerable hurdles remain, but increasing understanding of the neutralising antibody response to COVID-19 might make identifying members of the community at low risk of contracting and transmitting COVID-19 possible. We respond to the ethical arguments against immunity passports and give the positive ethical arguments. First, a strong presumption should be in favour of preserving people's free movement if at all feasible. Second, failing to recognise the reduced infection threat immune individuals pose risks punishing people for low-risk behaviour. Finally, further individual and social benefits are likely to accrue from allowing people to engage in free movement. Challenges relating to the implementation of immunity passports ought to be met with targeted solutions so as to maximise their benefit.
    4. 10.1016/S1473-3099(20)30766-0
    5. The scientific and ethical feasibility of immunity passports
    1. 2020-10-19

    2. ReconfigBehSci on Twitter. (n.d.). Twitter. Retrieved October 25, 2020, from https://twitter.com/SciBeh/status/1318119595497168897

    3. or: "Masks have been determined to be unnecessary even in surgical settings, and of no benefit in preventing infections.7" on basis of 1981 study with a single "trial" observing no significant rise in operating theatre infections in a 6 month period in 1981, in 1 hostpital
    4. such as this on danger of cloth masks: "Healthcare workers wearing cloth masks had significantly higher rates of influenza-like illness after four weeks of continuous on-the-job use, when compared to controls. (39)" where 'control' turns out to be *medical masks*
    5. the piece claims: "Masks have been shown through overwhelming clinical evidence to have no effect against transmission of viral pathogens." citation for this is a blog post on a naturopath website bu one of the authors. That blog post includes tendentious misrepresentations
    6. Researchgate is hosting (and "featuring") pseudoscience that is appearing as 'science' in anti-lockdown, anti-mask Twitter feeds I have no idea about scale of this, as just only stumbled on it myself. see, e.g.,
    1. 2020-10-12

    2. Woolf, S. H., Chapman, D. A., Sabo, R. T., Weinberger, D. M., Hill, L., & Taylor, D. D. H. (2020). Excess Deaths From COVID-19 and Other Causes, March-July 2020. JAMA, 324(15), 1562. https://doi.org/10.1001/jama.2020.19545

    3. Previous studies of excess deaths (the gap between observed and expected deaths) during the coronavirus disease 2019 (COVID-19) pandemic found that publicly reported COVID-19 deaths underestimated the full death toll, which includes documented and undocumented deaths from the virus and non–COVID-19 deaths caused by disruptions from the pandemic.1,2 A previous analysis found that COVID-19 was cited in only 65% of excess deaths in the first weeks of the pandemic (March-April 2020); deaths from non–COVID-19 causes (eg, Alzheimer disease, diabetes, heart disease) increased sharply in 5 states with the most COVID-19 deaths.1 This study updates through August 1, 2020, the estimate of excess deaths and explores temporal relationships with state reopenings (lifting of coronavirus restrictions).
    4. 10.1001/jama.2020.19545
    5. Excess Deaths From COVID-19 and Other Causes, March-July 2020
    1. 2020-10-16

    2. Health, T. L. P. (2020). COVID-19 in Spain: A predictable storm? The Lancet Public Health, 0(0). https://doi.org/10.1016/S2468-2667(20)30239-5

    3. As of Oct 12, there have been 861 112 confirmed cases and 32 929 deaths due to COVID-19 in Spain. More than 63 000 health-care workers have been infected. Spain was one of the most affected countries during the first wave of COVID-19 (March to June), and it has now been hit hard again by a second wave of COVID-19 infections. While the reasons behind this poor outcome are still to be fully understood, Spain's COVID-19 crisis has magnified weaknesses in some parts of the health system and revealed complexities in the politics that shape the country.
    4. 10.1016/S2468-2667(20)30239-5
    5. COVID-19 in Spain: a predictable storm?
    1. 2020-10-15

    2. Raimondo, S., Benigni, B., & De Domenico, M. (2020). Environmental conditions and human activity nexus. The case of Northern Italy during COVID-19 lockdown. ArXiv:2010.07721 [Physics]. http://arxiv.org/abs/2010.07721

    3. 2010.07721
    4. During COVID-19, draconian countermeasures forbidding non-essential human activities have been adopted worldwide, providing an unprecedented setup for testing sustainability policies. We unravel causal relationships among 16 environmental conditions and human activity variables and argue that, despite a measurable decrease in NO2 concentration due to human activities, locking down a region is insufficient to significantly reduce emissions. Policy strategies more effective than lockdowns must be considered for pollution control and climate change mitigation.
    5. Environmental conditions and human activity nexus. The case of Northern Italy during COVID-19 lockdown
    1. 2020-10-15

    2. Vaughan, A. (n.d.). Exclusive: Concerns raised about vital UK covid-19 infection survey. New Scientist. Retrieved October 18, 2020, from https://www.newscientist.com/article/2256942-exclusive-concerns-raised-about-vital-uk-covid-19-infection-survey/

    3. The UK’s largest scheme for tracking the spread of the coronavirus is at risk of providing a misleading picture of the epidemic, as a growing share of people invited to take part fail to respond or complete a test. The UK’s Office for National Statistics (ONS) launched its survey in April to estimate how many people are infected with the virus each week. At first, it randomly sampled thousands of homes in England, later adding those in Wales and Northern Ireland. The UK government’s top scientific advisers consider it the gold standard for measuring the state of the epidemic because other methods such as testing can miss many cases. Households who respond to the invitation to take part are visited by a survey worker, who provides the tests for people to complete themselves. When the survey began, 51 per cent of English households invited to take part completed at least one test. However, that figure has now dropped to just 5 per cent.
    4. Exclusive: Concerns raised about vital UK covid-19 infection survey
    1. 2020-10-14

    2. “Herd Immunity” is Not an Answer to a Pandemic. (n.d.). Retrieved October 17, 2020, from https://www.idsociety.org/news--publications-new/articles/2020/herd-immunity-is-not-an-answer-to-a-pandemic/

    3. Promoting the concept of “herd immunity” as framed in a recently circulated document as an answer to the COVID-19 pandemic is inappropriate, irresponsible and ill-informed. “Community immunity,” or “herd immunity,” a goal of vaccination campaigns, should never come at the cost of planned exposure to infection of millions of additional people as well as the severe illness and preventable deaths of hundreds of thousands of people. To assert that stepping away from the vigilance needed to control the spread of this novel coronavirus and that abdication of efforts to control a pandemic that has overwhelmed health systems worldwide is a “compassionate approach” is profoundly misleading.
    4. “Herd Immunity” is Not an Answer to a Pandemic
    1. 2020-10-15

    2. Martin, G. P., Sperrin, M., & Sotgiu, G. (2020). Performance of Prediction Models for Covid-19: The Caudine Forks of the External Validation. European Respiratory Journal. https://doi.org/10.1183/13993003.03728-2020

    3. Development and implementation of risk prediction models to aid risk stratification and resource allocation could improve the current scenario. Clinical prediction models (CPMs) aim to predict an individual's expected outcome value, or an individual's risk of an outcome being present (diagnostic) or happening in the future (prognostic), based on sets of identified predictor variables [1, 2]. A plethora of such models have been described during the first wave of the Covid-19 epidemic: a recent “living” systematic review identified (at the time of writing) 145 CPMs focused on Covid-19 patients [3].Unfortunately, many of the existing Covid-19 CPMs have been identified to be at high risk of bias, due to poor reporting, over-estimation of predictive performance, and lack of external validation [3]. External validation, which is an important aspect during the development process of any CPM, can independently evaluate the model focusing on data independent to those data used to derive the model [1, 2]. Crucially, this step assesses the generalisability/transportability of the CPM into new populations before they are recommended for widespread clinical implementation.
    4. 10.1183/13993003.03728-2020
    5. Performance of Prediction Models for Covid-19: The Caudine Forks of the External Validation
    1. 2020-10-14

    2. David Rothschild on Twitter. (n.d.). Twitter. Retrieved October 17, 2020, from https://twitter.com/DavMicRot/status/1316429651988877312

    3. This is not just for the mainstream media, but the academic research community that obsessed with fake news on social media, but barely explores mainstream news, especially TV, which includes about 85% of news consumption. Thread borrows liberally from my work with @duncanjwatts
    4. Will the mainstream media try to balance the sheet by spreading anti-Biden propaganda in last 20 days? They have certainty not learned from their past mistakes and @NBCNews decided today to undermine spirit of debates. But, let's keep the pressure on them to do better this time.
    5. Mainstream media has had a lot of bad coverage of Trump lately: terrible debate performance where he gave a shout-out to White Supremacists, diagnosis & subsequent hospitalization for COVID-19 spotlighting his personal and administration failures to contain the pandemic, etc.
    6. Which brings US to today: This morning @maggieNYT of @nytimes amplified extremely suspect reporting on emails taken from Hunter Biden’s laptop, while Democratic member of Facebook’s Communication team issued a statement saying they will suppress the story
    7. Journalists and editors—at least those not at Fox—tend to see themselves as detached observers of events whose job is simply to report them. They are actually active—and highly influential—participants in shaping public opinion through agenda setting and narrative construction.
    8. (2) Mainstream media believes that social media is stealing their business, making them an appetizing target (which is partly true).
    9. (1) Everyone agrees Russian propaganda is bad so it is easier to call it out. But, awkward mainstream media, which draw their sense of worth from providing equal validity to both sides and embracing the patriotic greatness of our institutions, to discuss their misinformation.
    10. Too much effort has been put into finding where the Russians have seeded some really fake news that few will ever see, but too little effort has gone into how mainstream media actually has magnitudes more power to inform or misinform the public.
    11. General public thinks that Clinton is a bad at IT Security & Trump is fine at IT Security because mainstream media *chose* to selectively promote anti-Clinton propaganda, while ignoring true issues w/ Trump. Their choices matter: IT Security won Trump 2016 election, not Pizzagate
    12. Trump actually does have a big IT Security situation: not legal to hide government correspondence from public record AND between Trump’s unsecured cell phone & Jared’s use of open calling tools, highly probable that malicious actors hacked sensitive information from US.
    13. Clinton actually did have a tiny IT Security situation: despite being legal and secure, she should not have run government emails through a private server. And, her campaign manager did get hacked by the Russians, in a completely separate incident.
    14. When it comes to IT Security the mainstream media did not spread misinformation, but they made the general population misinformed.
    15. Mainstream media's light handling of Trump’s *way worse* IT Security failures show that the issue is not inherently important, but that the mainstream media made it important for Clinton, while *choosing* to not make it important for Trump.
    16. Mainstream media defines balance by falsely equating the competency, scruples, and policy merit of the two major party candidates: it was hard to bring Donald Trump up (dude is super incompetent, corrupt, policy was ill-defined and/or cruel), so they dragged Clinton down.
    17. .@nytimes did not actually think Clinton had an IT Security problem that the American people needed to know in order to choose wisely in the 2016 election: expecting Clinton to win the election, they needed a story to show how “balanced” they were by bashing her.
    18. One particular story was broke by @nytimes just a few month ago: Jared Kushner used http://FreeConferenceCall.com to run the US’ COVID-19 response. This tantalizing IT Security failure was noted in the 21st paragraph of an article that ran on the 14th page
    19. There were numerous other egregious IT Security lapses by Trump Team that followed in 4 years of their rule (including Jared using WhatsApp to *potentially* greenlight Saudi Arabia murdering a US-based journalist), most of them flying under the radar of national mainstream news.
    20. In 2017 @USATODAY reported that Jared Kushner & Ivanka Trump were routing personal and government emails through a private server hosted by the Trump Organization. @nytimes did run one front-page article on this, before the story was eclipsed by other news
    21. Consequently other publications also wrote up this story and Clinton's IT Security became the most consumed & remembered issue of the election.
    22. In 2016 @nytimes published 10 front-page articles on Secretary Clinton’s IT Security in a 6 day period from 10/29-11/3/16. NYT was signaling this is an important story, that people should pay attention to this story above and beyond other possible topics about the 2016 election.
    1. 2020-10-14

    2. Lawton, G. (n.d.). It is bad science to say covid-19 infections will create herd immunity. New Scientist. Retrieved October 16, 2020, from https://www.newscientist.com/article/2257258-it-is-bad-science-to-say-covid-19-infections-will-create-herd-immunity/

    3. According to the signatories of an open letter called the Great Barrington Declaration, lockdown measures are doing more harm than good and we should open up society and let the virus rip. OK, that is a bit of an exaggeration. The declaration – named after the US town where it was signed – advocates a strategy called “focused protection” under which the most vulnerable people shield and everybody else “should immediately be allowed to resume life as normal”. This will then allow herd immunity to build up. Advertisement googletag.cmd.push(function() { googletag.display('mpu-mid-article'); }); The declaration publicly exposed a scientific disagreement that has been simmering for months. On one side are mainstream scientists who reluctantly see restrictions on freedom as the only way to keep a lid on the pandemic while we wait for vaccines; on the other, the libertarians who see the damage done to economies and individual lives as too high a price.
    4. It is bad science to say covid-19 infections will create herd immunity
    1. 2020-10-14

    2. Vaughan, A. (n.d.). England & Wales had most excess deaths in Europe’s covid-19 first wave. New Scientist. Retrieved October 16, 2020, from https://www.newscientist.com/article/2256986-england-wales-had-most-excess-deaths-in-europes-covid-19-first-wave/

    3. England, Wales and Spain suffered the biggest increases in deaths by all causes during the first wave of the covid-19 pandemic, while countries including New Zealand, Norway and Poland appear to have escaped relatively unscathed. The three worst-hit countries each saw around 100 “excess deaths” per 100,000 people between February and May, which researchers say was probably due to governments being slow to implement lockdowns and scale up testing and tracing.
    4. England & Wales had most excess deaths in Europe’s covid-19 first wave
  2. realrisk.wintoncentre.uk realrisk.wintoncentre.uk
    1. 2020-10-15

    2. https://realrisk.wintoncentre.uk/. Retrieved 16-10-2020

    3. What does it do?You put in specialist measures of relative risk and get out clear, accessible absolute risks in the form of text, icon arrays and bar charts. Think of it as a fancy calculator.Who is it for?RealRisk is suitable for anyone working to communicate research findings from the medical & social sciences to a broad audience - journalists, press officers, healthcare professionals and others.What does it do?You put in specialist measures of relative risk and get out clear, accessible absolute risks in the form of text, icon arrays and bar charts. Think of it as a fancy calculator.Who is it for?RealRisk is suitable for anyone working to communicate research findings from the medical & social sciences to a broad audience - journalists, press officers, healthcare professionals and others.What do you need to get started?An original research paper - RealRisk can be used with any study which reports a relative risk Relative Risk (RR)In a research study, a risk is the probability of an outcome occurring in one group (e.g. the number participants having a heart attack over the total number of participants).A relative risk is the risk in the experimental group divided by the risk in a control or baseline group (also sometimes called the ‘Risk Ratio’).A relative risk greater than 1 means the outcome was more common in the experimental group than the control/baseline group, and a RR less than 1 means it was less common.Where do I find it?Relative Risks will normally be reported in the abstract of a paper and in the results section. They generally look like this: “RR 3.6” or sometimes “aRR 3.6”. Often, several RRs will be reported for different comparisons. For RealRisk, pick the RR most worth reporting.Close (RR), hazard ratio Hazard Ratio (HR)A hazard is the rate at which some outcome of interest occurs over a given period of time (e.g. heart attacks or cancer diagnoses per year).A hazard ratio is the hazard in an experimental group (exposed to the risk factor) divided by the hazard in a control or baseline group.A hazard ratio bigger than 1 means the outcome of interest has occurred at a higher rate in the experimental group than the control or baseline group, and a hazard ratio smaller than 1 means it’s occurred at a lower rate.Where do I find it?Hazard ratios will normally be reported in the abstract of a paper and/or in the results section. They generally look like this: “HR 3.6.”. Often, several HRs will be reported for different comparisons. For RealRisk, pick the HR most worth reporting.Close (HR) an odds ratio Odds Ratio (OR)In a research study, the odds of some outcome is the number of times it happened over the number of times it didn’t happen (e.g. the number of people who had heart attacks over the number who didn’t)An odds ratio is the ratio of two odds: the odds of the outcome of interest in the experimental group divided by the odds in a control or baseline group.An odds ratio greater than 1 means the outcome of interest (heart attacks or cancer diagnoses) was more common in the experimental group (usually the group exposed to the risk factor), and an OR below 1 means it was less common. Where do I find it?Odds ratios will normally be reported in the abstract of a paper and/or in the results section. They generally look like this: “OR 3.6” or sometimes “aOR 3.6”. Often, several ORs will be reported for different comparisons. For RealRisk, pick the OR most worth reporting.Close (OR) or a percentage change Percentage ChangeThis refers to a percentage increase or decrease as in, “Women taking HRT were 80% more likely to develop breast cancer” or “People who exercise are 70% less likely to be depressed”.For a percentage increase just type in the number e.g. 80%To indicate a percentage decrease add a minus sign, e.g -70%Close. (Click here for more)You will also need to find a baseline risk relevant to the study. Help is provided.
    1. 2020-09-26

    2. Science as Amateur Software Development. (2020, September 26). https://www.youtube.com/watch?v=zwRdO9_GGhY&feature=youtu.be

    3. Science is one of humanity's greatest inventions. Academia, on the other hand, is not. It is remarkable how successful science has been, given the often chaotic habits of scientists. In contrast to other fields, like say landscaping or software engineering, science as a profession is largely *unprofessional*—apprentice scientists are taught less about how to work responsibly than about how to earn promotions. This results in ubiquitous and costly errors. Software development has become indispensable to scientific work. I want to playfully ask how it can become even more useful by transferring some aspects of its professionalism, the day-to-day tracking and back-tracking and testing that is especially part of distributed, open-source software development. Science, after all, aspires to be distributed, open-source knowledge development.
    4. Science as Amateur Software Development