7,256 Matching Annotations
  1. Last 7 days
    1. 2020-09-16

    2. Peel, L., & Schaub, M. T. (2020). Detectability of hierarchical communities in networks. ArXiv:2009.07525 [Physics, Stat]. http://arxiv.org/abs/2009.07525

    3. 2009.07525
    4. We study the problem of recovering a planted hierarchy of partitions in a network. The detectability of a single planted partition has previously been analysed in detail and a phase transition has been identified below which the partition cannot be detected. Here we show that, in the hierarchical setting, there exist additional phases in which the presence of multiple consistent partitions can either help or hinder detection. Accordingly, the detectability limit for non-hierarchical partitions typically provides insufficient information about the detectability of the complete hierarchical structure, as we highlight with several constructive examples.
    5. Detectability of hierarchical communities in networks
    1. 2020-08-26

    2. Ecker, U. K. H., Lewandowsky, S., & Chadwick, M. (2020). Can corrections spread misinformation to new audiences? Testing for the elusive familiarity backfire effect. Cognitive Research: Principles and Implications, 5(1), 41. https://doi.org/10.1186/s41235-020-00241-6

    3. 10.1186/s41235-020-00241-6
    4. Misinformation often continues to influence inferential reasoning after clear and credible corrections are provided; this effect is known as the continued influence effect. It has been theorized that this effect is partly driven by misinformation familiarity. Some researchers have even argued that a correction should avoid repeating the misinformation, as the correction itself could serve to inadvertently enhance misinformation familiarity and may thus backfire, ironically strengthening the very misconception that it aims to correct. While previous research has found little evidence of such familiarity backfire effects, there remains one situation where they may yet arise: when correcting entirely novel misinformation, where corrections could serve to spread misinformation to new audiences who had never heard of it before. This article presents three experiments (total N = 1718) investigating the possibility of familiarity backfire within the context of correcting novel misinformation claims and after a 1-week study-test delay. While there was variation across experiments, overall there was substantial evidence against familiarity backfire. Corrections that exposed participants to novel misinformation did not lead to stronger misconceptions compared to a control group never exposed to the false claims or corrections. This suggests that it is safe to repeat misinformation when correcting it, even when the audience might be unfamiliar with the misinformation.
    5. Can corrections spread misinformation to new audiences? Testing for the elusive familiarity backfire effect
    1. 2020-09-03

    2. The Contagion Externality of a Superspreading Event: The Sturgis Motorcycle Rally and COVID-19 | NCRC. (2020, September 3). 2019 Novel Coronavirus Research Compendium (NCRC). https://ncrc.jhsph.edu/research/the-contagion-externality-of-a-superspreading-event-the-sturgis-motorcycle-rally-and-covid-19/

    3. This study, which was available as a preprint and thus had not yet been peer reviewed, uses county-level SARS-CoV-2 testing data to show that the Sturgis motorcycle rally likely led to substantial increases in cases in the local community where the rally took place. However, there is considerable uncertainty surrounding the broader, national impact of the rally and its associated costs given limitations in the methodological approaches used. Results from this study should be interpreted cautiously.
    4. The Contagion Externality of a Superspreading Event: The Sturgis Motorcycle Rally and COVID-19
    1. Offeddu, V., Yung, C. F., Low, M. S. F., & Tam, C. C. (2017). Effectiveness of Masks and Respirators Against Respiratory Infections in Healthcare Workers: A Systematic Review and Meta-Analysis. Clinical Infectious Diseases, 65(11), 1934–1942. https://doi.org/10.1093/cid/cix681

    2. 2017-08-07

    3. This systematic review and meta-analysis quantified the protective effect of facemasks and respirators against respiratory infections among healthcare workers. Relevant articles were retrieved from Pubmed, EMBASE, and Web of Science. Meta-analyses were conducted to calculate pooled estimates. Meta-analysis of randomized controlled trials (RCTs) indicated a protective effect of masks and respirators against clinical respiratory illness (CRI) (risk ratio [RR] = 0.59; 95% confidence interval [CI]:0.46–0.77) and influenza-like illness (ILI) (RR = 0.34; 95% CI:0.14–0.82). Compared to masks, N95 respirators conferred superior protection against CRI (RR = 0.47; 95% CI: 0.36–0.62) and laboratory-confirmed bacterial (RR = 0.46; 95% CI: 0.34–0.62), but not viral infections or ILI. Meta-analysis of observational studies provided evidence of a protective effect of masks (OR = 0.13; 95% CI: 0.03–0.62) and respirators (OR = 0.12; 95% CI: 0.06–0.26) against severe acute respiratory syndrome (SARS). This systematic review and meta-analysis supports the use of respiratory protection. However, the existing evidence is sparse and findings are inconsistent within and across studies. Multicentre RCTs with standardized protocols conducted outside epidemic periods would help to clarify the circumstances under which the use of masks or respirators is most warranted.
    4. 10.1093/cid/cix681
    5. Effectiveness of Masks and Respirators Against Respiratory Infections in Healthcare Workers: A Systematic Review and Meta-Analysis
    1. 2020-08-21

    2. Eyre, D. W., Lumley, S. F., O’Donnell, D., Campbell, M., Sims, E., Lawson, E., Warren, F., James, T., Cox, S., Howarth, A., Doherty, G., Hatch, S. B., Kavanagh, J., Chau, K. K., Fowler, P. W., Swann, J., Volk, D., Yang-Turner, F., Stoesser, N., … Walker, T. M. (2020). Differential occupational risks to healthcare workers from SARS-CoV-2 observed during a prospective observational study. ELife, 9, e60675. https://doi.org/10.7554/eLife.60675

    3. 10.7554/eLife.60675
    4. We conducted voluntary Covid-19 testing programmes for symptomatic and asymptomatic staff at a UK teaching hospital using naso-/oro-pharyngeal PCR testing and immunoassays for IgG antibodies. 1128/10,034 (11.2%) staff had evidence of Covid-19 at some time. Using questionnaire data provided on potential risk-factors, staff with a confirmed household contact were at greatest risk (adjusted odds ratio [aOR] 4.82 [95%CI 3.45–6.72]). Higher rates of Covid-19 were seen in staff working in Covid-19-facing areas (22.6% vs. 8.6% elsewhere) (aOR 2.47 [1.99–3.08]). Controlling for Covid-19-facing status, risks were heterogenous across the hospital, with higher rates in acute medicine (1.52 [1.07–2.16]) and sporadic outbreaks in areas with few or no Covid-19 patients. Covid-19 intensive care unit staff were relatively protected (0.44 [0.28–0.69]), likely by a bundle of PPE-related measures. Positive results were more likely in Black (1.66 [1.25–2.21]) and Asian (1.51 [1.28–1.77]) staff, independent of role or working location, and in porters and cleaners (2.06 [1.34–3.15]).
    5. Differential occupational risks to healthcare workers from SARS-CoV-2 observed during a prospective observational study
    1. 2020-08-12

    2. Covid-19: Herd immunity in Sweden fails to materialise | The Royal Society of Medicine. (n.d.). Retrieved September 18, 2020, from https://www.rsm.ac.uk/media-releases/2020/covid-19-herd-immunity-in-sweden-fails-to-materialise/

    3. Sweden’s policy of allowing the controlled spread of Covid-19 viral infection among the population has so far failed to deliver the country’s previously stated goal of herd immunity. Commenting on recent antibody testing clinical and research findings, authors of a paper published by the Journal of the Royal Society of Medicine, write that Sweden’s higher rates of viral infection, hospitalisation and mortality compared with neighbouring countries may have serious implications for Scandinavia and beyond.
    4. Covid-19: herd immunity in Sweden fails to materialise
    1. 2020-03-30

    2. Report 13—Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries. (n.d.). Imperial College London. Retrieved September 18, 2020, from http://www.imperial.ac.uk/medicine/departments/school-public-health/infectious-disease-epidemiology/mrc-global-infectious-disease-analysis/covid-19/report-13-europe-npi-impact/

    3. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number – a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.
    4. Report 13 - Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries
    1. 2020-04-15

    2. Gardner, J. M., Willem, L., Wijngaart, W. van der, Kamerlin, S. C. L., Brusselaers, N., & Kasson, P. (2020). Intervention strategies against COVID-19 and their estimated impact on Swedish healthcare capacity. MedRxiv, 2020.04.11.20062133. https://doi.org/10.1101/2020.04.11.20062133

    3. Objectives: During March 2020, the COVID-19 pandemic has rapidly spread globally, and non-pharmaceutical interventions are being used to reduce both the load on the healthcare system as well as overall mortality. Design: Individual-based transmission modelling using Swedish demographic and Geographical Information System data and conservative COVID-19 epidemiological parameters. Setting: Sweden Participants: A model to simulate all 10.09 million Swedish residents. Interventions: 5 different non-pharmaceutical public-health interventions including the mitigation strategy of the Swedish government as of 10 April; isolation of the entire household of confirmed cases; closure of schools and non-essential businesses with or without strict social distancing; and strict social distancing with closure of schools and non-essential businesses. Main outcome measures: Estimated acute care and intensive care hospitalisations, COVID-19 attributable deaths, and infections among healthcare workers from 10 April until 29 June. Findings: Our model for Sweden shows that, under conservative epidemiological parameter estimates, the current Swedish public-health strategy will result in a peak intensive-care load in May that exceeds pre-pandemic capacity by over 40-fold, with a median mortality of 96,000 (95% CI 52,000 to 183,000). The most stringent public-health measures examined are predicted to reduce mortality by approximately three-fold. Intensive-care load at the peak could be reduced by over two-fold with a shorter period at peak pandemic capacity. Conclusions: Our results predict that, under conservative epidemiological parameter estimates, current measures in Sweden will result in at least 40-fold over-subscription of pre-pandemic Swedish intensive care capacity, with 15.8 percent of Swedish healthcare workers unable to work at the pandemic peak. Modifications to ICU admission criteria from international norms would further increase mortality.
    4. 0.1101/2020.04.11.20062133
    5. Intervention strategies against COVID-19 and their estimated impact on Swedish healthcare capacity
    1. 2020-09-12

    2. ACTUAL DOCTOR WATCHES COVID PSEUDOSCIENCE VIDEO. (2020, September 15). https://www.youtube.com/watch?v=DUDg5ossirU&feature=youtu.be

    3. 1. Claims 20% of the population were infected by COVID (no evidence to date of that level of infection, serology estimates around 6%) 2. Claims 80% of the unexposed population are already immune through T-Cell crossreactivity with COVID - best prevalence studies show 20-50% cross-reactive T-cells and NO EVIDENCE PRODUCES IMMUNITY 3. Ignores the fact that if the above were true we would ALREADY HAVE HERD IMMUNITY. 4. Claims to be a 'scientist'- no publications, 'research' pulled from Twitter with no references and many incorrect attributions to actual papers. 5. Confuses the seasons on multiple occasions, including the summer. 6. Attributes fall in cases and deaths in countries with full lockdown to immunity, without any evidence. 7. Claims Sweden had 'minimal distancing methods' and is now 'immune' - Sweden had 50% working from home, a reduction in 40% travel in Stockholm and 25% reduction in spending, banned mass gatherings early March and advised to avoid travel and shielded 70s at the same time. Took a 8% drop in GDP as well. 8. Ignores many countries that were successful on economy and virus control (South Korea, Singapore, Germany) 9. Contrasts measures taken with Spanish Flu (essentially none in 1918) with the above measures taken in Sweden and concludes lockdown doesn't work - the opposite is demonstrated. 10. Claims a R value of 0.39 suggests a very strong correlation, when this would be considered weak. 11. Discusses 'fatality rate' without reference to either IFR or CFR. 12. Claims initially that epidemic is 'over' due to immunity, but then later that a 'second hump' is normal, despite this being directly contradictory to two earlier statements. 13. Goes on to claim that the summer is the time to build immunity, despite directly contradicting his own points in the prior three claims. 14. Neglects to mention that healthcare workers have been found to be 2.5x as likely to contract COVID than the general pop. and ICU workers 0.7x due to the PPE (including full respiratory masks). 15. Neglects to mention multiple meta-analyses of observational data concluding masks are very effective for reducing spread of respiratory viruses. 16. Neglects to mention widespread mask use has been common in Asia for over a decade. 17. Neglects to mention early mask use in Vietnam was associated with the best COVID outcomes in the world, despite minimal resources. 18. Claims rise in cases in the UK is due to over testing - between 1st Aug and 1st Septemeber the number of tests performed in the UK was static, while cases rose 292%. The percentage positive has also increased 19. Claims this is due to PCR testing - hospitalisations in the past fornight have tripled and patients in hospital WITH COVID have doubled. 20. Then claims this is part of the 'normal' winter excess death, despite mixing up excess death and COVID specific death 21. Provides no calculations or working and appears to have no medical, epidemiological, virological, genetic qualification or papers published with peer-review. 22. Claims a new wave in winter is normal for coronavirus, despite claiming on four occassions that we are now immune to SARS COV 2. 23. Claims lastly that we have ancestral community immunity in the summer - despite cases being traditionally very low and therefore exposure very low. Does not propose a mechanism where humans can produce immunity without exposure. (There isn't one) 24. Then claims this is 'science' - despite no attempt to engage with any scientific process of peer-review, observable or verifiable results. 25. Calls his network of Twitter enthusiasts 'solid scientists' ..... case closed.
    4. ACTUAL DOCTOR WATCHES COVID PSEUDOSCIENCE VIDEO
    1. 2020-07-15

    2. Le Bert, N., Tan, A. T., Kunasegaran, K., Tham, C. Y. L., Hafezi, M., Chia, A., Chng, M. H. Y., Lin, M., Tan, N., Linster, M., Chia, W. N., Chen, M. I.-C., Wang, L.-F., Ooi, E. E., Kalimuddin, S., Tambyah, P. A., Low, J. G.-H., Tan, Y.-J., & Bertoletti, A. (2020). SARS-CoV-2-specific T cell immunity in cases of COVID-19 and SARS, and uninfected controls. Nature, 584(7821), 457–462. https://doi.org/10.1038/s41586-020-2550-z

    3. 10.1038/s41586-020-2550-z
    4. Memory T cells induced by previous pathogens can shape susceptibility to, and the clinical severity of, subsequent infections1. Little is known about the presence in humans of pre-existing memory T cells that have the potential to recognize severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here we studied T cell responses against the structural (nucleocapsid (N) protein) and non-structural (NSP7 and NSP13 of ORF1) regions of SARS-CoV-2 in individuals convalescing from coronavirus disease 2019 (COVID-19) (n = 36). In all of these individuals, we found CD4 and CD8 T cells that recognized multiple regions of the N protein. Next, we showed that patients (n = 23) who recovered from SARS (the disease associated with SARS-CoV infection) possess long-lasting memory T cells that are reactive to the N protein of SARS-CoV 17 years after the outbreak of SARS in 2003; these T cells displayed robust cross-reactivity to the N protein of SARS-CoV-2. We also detected SARS-CoV-2-specific T cells in individuals with no history of SARS, COVID-19 or contact with individuals who had SARS and/or COVID-19 (n = 37). SARS-CoV-2-specific T cells in uninfected donors exhibited a different pattern of immunodominance, and frequently targeted NSP7 and NSP13 as well as the N protein. Epitope characterization of NSP7-specific T cells showed the recognition of protein fragments that are conserved among animal betacoronaviruses but have low homology to ‘common cold’ human-associated coronaviruses. Thus, infection with betacoronaviruses induces multi-specific and long-lasting T cell immunity against the structural N protein. Understanding how pre-existing N- and ORF1-specific T cells that are present in the general population affect the susceptibility to and pathogenesis of SARS-CoV-2 infection is important for the management of the current COVID-19 pandemic.
    5. SARS-CoV-2-specific T cell immunity in cases of COVID-19 and SARS, and uninfected controls
    1. The Behaviour Change Wheel Book—A Guide To Designing Interventions. (n.d.). Retrieved September 17, 2020, from http://www.behaviourchangewheel.com/

    2. This is a practical guide to designing and evaluating behaviour change interventions and policies. It is based on the Behaviour Change Wheel, a synthesis of 19 behaviour change frameworks that draw on a wide range of disciplines and approaches. The guide is for policy makers, practitioners, intervention designers and researchers and introduces a systematic, theory-based method, key concepts and practical tasks.
    3. The Behaviour Change Wheel
    1. 2020-09-16

    2. r/BehSciAsk—A comprehensive compliance model? (n.d.). Reddit. Retrieved September 17, 2020, from https://www.reddit.com/r/BehSciAsk/comments/itylzl/a_comprehensive_compliance_model/

    3. In order to convince people to wear a mask or to do social distancing, it is helpful to know why people are or are not compliant. Once these factors are defined, one could find out how prevalent those beliefs, attitudes or situational pressures are in society and therefore adapt communication or other interventions to adress the most important factors
    4. A comprehensive compliance model?
    1. 2020-09-08

    2. Gao, S., Rao, J., Kang, Y., Liang, Y., Kruse, J., Dopfer, D., Sethi, A. K., Reyes, J. F. M., Yandell, B. S., & Patz, J. A. (2020). Association of Mobile Phone Location Data Indications of Travel and Stay-at-Home Mandates With COVID-19 Infection Rates in the US. JAMA Network Open, 3(9), e2020485–e2020485. https://doi.org/10.1001/jamanetworkopen.2020.20485

    3. Importance  A stay-at-home social distancing mandate is a key nonpharmacological measure to reduce the transmission rate of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), but a high rate of adherence is needed.Objective  To examine the association between the rate of human mobility changes and the rate of confirmed cases of SARS-CoV-2 infection.Design, Setting, and Participants  This cross-sectional study used daily travel distance and home dwell time derived from millions of anonymous mobile phone location data from March 11 to April 10, 2020, provided by the Descartes Labs and SafeGraph to quantify the degree to which social distancing mandates were followed in the 50 US states and District of Columbia and the association of mobility changes with rates of coronavirus disease 2019 (COVID-19) cases.Exposure  State-level stay-at-home orders during the COVID-19 pandemic.Main Outcomes and Measures  The main outcome was the association of state-specific rates of COVID-19 confirmed cases with the change rates of median travel distance and median home dwell time of anonymous mobile phone users. The increase rates are measured by the exponent in curve fitting of the COVID-19 cumulative confirmed cases, while the mobility change (increase or decrease) rates were measured by the slope coefficient in curve fitting of median travel distance and median home dwell time for each state.Results  Data from more than 45 million anonymous mobile phone devices were analyzed. The correlation between the COVID-19 increase rate and travel distance decrease rate was –0.586 (95% CI, –0.742 to –0.370) and the correlation between COVID-19 increase rate and home dwell time increase rate was 0.526 (95% CI, 0.293 to 0.700). Increases in state-specific doubling time of total cases ranged from 1.0 to 6.9 days (median [interquartile range], 2.7 [2.3-3.3] days) before stay-at-home orders were enacted to 3.7 to 30.3 days (median [interquartile range], 6.0 [4.8-7.1] days) after stay-at-home social distancing orders were put in place, consistent with pandemic modeling results.Conclusions and Relevance  These findings suggest that stay-at-home social distancing mandates, when they were followed by measurable mobility changes, were associated with reduction in COVID-19 spread. These results come at a particularly critical period when US states are beginning to relax social distancing policies and reopen their economies. These findings support the efficacy of social distancing and could help inform future implementation of social distancing policies should they need to be reinstated during later periods of COVID-19 reemergence.
    4. 10.1001/jamanetworkopen.2020.20485
    5. Association of Mobile Phone Location Data Indications of Travel and Stay-at-Home Mandates With COVID-19 Infection Rates in the US
    1. 2020-09-15

    2. Leuker, C., Hertwig, R., Gumenik, K., Eggeling, L. M., Hechtlinger, S., Kozyreva, A., Samaan, L., & Fleischhut, N. (2020). Wie informiert sich die Bevölkerung in Deutschland rund um das Coronavirus? Umfrage zu vorherrschenden Themen und Gründen, dem Umgang mit Fehlinformationen, sowie der Risikowahrnehmung und dem Wissen der Bevölkerung rund um das Coronavirus (Version 5, p. 966670) [Application/pdf]. Max-Planck-Institut für Bildungsforschung. https://doi.org/10.17617/2.3247925

    3. Am 27. Januar 2020 wird der erste Fall einer Infektion mit dem neuartigen Coronavirus in Deutschland offiziell bestätigt. Kurz darauf richtet die Regierung einen Krisenstab ein, der Kreis Heinsberg meldet eine steigende An-zahl an Infektionen. Anfang März wird klar, dass das Coronavirus sich auch in Deutschland verbreitet. Es folgen weitreichende Einschränkungen des öffentlichen und privaten Lebens: Großveranstaltungen werden abgesagt, Schulschließungen angekündigt, soziale Kontaktbeschränkungen treten in Kraft.Die Bedrohung ist Anfang März neu, global und schwer abschätzbar. Das Coronavirus dominiert die Medien ge-nauso wie private Gespräche in Deutschland. Die Bevölkerung ist einer beispiellosen Informationsflut, einschließ-lich Fehlinformationen und Unsicherheiten, ausgesetzt: von täglichen Statistiken zu Infektionen, über Symptome, Risiken und Verhaltensempfehlungen, bis hin zu persönlichen Berichten, globalen Vergleichen und Maßnahmen, die das Virus stoppen oder dessen Verbreitung verlangsamen sollen.Dabei ist unklar, wie die Bevölkerung mit dieser Informationsflut umgegangen ist und wie sich das Informations-verhalten mit dem Rückgang der Infektionszahlen und den Lockerungen der Maßnahmen Anfang Juni verän-derte. So musste die Bevölkerung Anfang Juni damit rechnen, dass Risiken sich regional unterscheiden und Maßnahmen an das aktuelle Infektionsgeschehen angepasst werden. Gleichzeitig sind die wirtschaftlichen und gesellschaftlichen Folgen der Einschränkungen durch die Pandemie zu bewältigen. Wir konzentrieren uns im folgenden Bericht auf vier zentrale Fragen: (1) Wie informiert sich die Bevölkerung nach eigenen Angaben zu Beginn der Lockerungsphase Anfang Juni rund um das Coronavirus und wie hat sich das Verhalten im Vergleich zu Anfang März verändert? (2) Über welche Themen, aus welchen Gründen und über welche Quellen informiert sich die Bevölkerung? (3) Wie geht die Bevölkerung mit Fehlinformationen um? (4) Wie nimmt die Bevölkerung Risiken rund um das Coronavirus wahr und wie gut ist sie informiert? Auch wenn einige Bevölkerungsgruppen durch eine Infektion stärker gefährdet sind (z.B. Ältere oder Personen mit Vorerkrankun-gen), ist es wichtig, dass sich alle Bürger*innen ausreichend über Risiken und Maßnahmen informieren, um die Ausbreitung des Coronavirus zu kontrollieren und Risikogruppen zu schützen.Um diese Fragen zu beantworten, führte Respondi im Auftrag des Max-Planck-Instituts für Bildungsforschung zwischen dem 03. und 06. Juni 2020 eine repräsentative Onlineumfrage mit N = 1107 durch. Die aktuelle Bevöl-kerungsverteilung wurde hinsichtlich Alter (18–69 Jahre), Geschlecht und Bundesland durch Quotenstichproben berücksichtigt.
    4. 10.17617/2.3247925
    5. WIE INFORMIERT SICH DIE BEVÖLKERUNG IN DEUTSCHLAND RUND UM DAS CORONAVIRUS?
    1. 2020-09-15

    2. Is the 4C Mortality Score fit for purpose? Some comments and concerns. (2020). https://www.bmj.com/content/370/bmj.m3339/rr-3

    3. We read with interest the paper in the BMJ by Knight et al.,[1] proposing a new risk prediction model for patients admitted to hospital with COVID-19, which the Guardian indicate is expected to be rolled out in the NHS this week (https://www.theguardian.com/world/2020/sep/09/risk-calculator-for-covid-...). On the whole, the paper appears of higher quality than most other articles we have reviewed in our living review [2]. For example, the dataset was large enough;3 there was a very clear target population; missing data was handled using multiple imputation; multiple metrics of predictive performance were considered (including calibration and net benefit, which are often ignored); and reporting followed the TRIPOD guideline [4 5]. However, we have identified some concerns and issues, that we want to flag to BMJ readers.
    4. 10.1136/bmj.m3339
    5. Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score
    1. 2020-09-14

    2. If rich countries monopolize COVID-19 vaccines, it could cause twice as many deaths as distributing them equally. (n.d.). Retrieved September 17, 2020, from https://news.northeastern.edu/2020/09/14/if-rich-countries-monopolize-covid-19-vaccines-it-could-cause-twice-as-many-deaths-as-distributing-them-equally/

    3. the unequal distribution of vaccines stands to do even more damage as countries with greater financial resources preemptively stockpile limited doses of future COVID-19 vaccines, a move that Northeastern’s MOBS Lab determined could cause almost twice as many coronavirus deaths than if vaccines were equally distributed once available.
    4. If rich countries monopolize COVID-19 vaccines, it could cause twice as many deaths as distributing them equally
    1. 2020-09-15

    2. Giles, J. R., Erbach-Schoenberg, E. zu, Tatem, A. J., Gardner, L., Bjørnstad, O. N., Metcalf, C. J. E., & Wesolowski, A. (2020). The duration of travel impacts the spatial dynamics of infectious diseases. Proceedings of the National Academy of Sciences, 117(36), 22572–22579. https://doi.org/10.1073/pnas.1922663117

    3. 10.31234/osf.io/dyz29
    4. A cue that indicates imminent threat elicits a wide range of physiological, hormonal, autonomic, cognitive, and emotional fear responses in humans and facilitates threat-specific avoidance behavior. The occurrence of a threat cue can, however, also have general motivational effects and affect behavior. That is, the encounter with a threat cue can increase our tendency to engage in general avoidance behavior that does neither terminate nor prevent the threat-cue or the threat itself. Furthermore, the encounter with a threat-cue can substantially reduce our likelihood to engage in behavior that leads to rewarding outcomes. Such general motivational effects of threat-cues on behavior can be informative about the transition from normal to pathological anxiety and could also explain the development of comorbid disorders, such as depression and substance abuse. Despite the unmistakable relevance of the motivational effects of threat for our understanding of anxiety disorders, their investigation is still in its infancy. Pavlovian-to-Instrumental transfer is one paradigm that allows us to investigate such motivational effects of threat cues. Here, we review studies investigating aversive transfer in humans and discuss recent results on the neural circuits mediating Pavlovian-to-Instrumental transfer effects. Finally, we discuss potential limitations of the transfer paradigm and future directions for employing Pavlovian-to-Instrumental transfer for the investigation of motivational effects of fear and anxiety.
    5. A review on aversive Pavlovian-to-Instrumental transfer in humans
    1. 2020-09-15

    2. Giles, J. R., Erbach-Schoenberg, E. zu, Tatem, A. J., Gardner, L., Bjørnstad, O. N., Metcalf, C. J. E., & Wesolowski, A. (2020). The duration of travel impacts the spatial dynamics of infectious diseases. Proceedings of the National Academy of Sciences, 117(36), 22572–22579. https://doi.org/10.1073/pnas.1922663117

    3. the collation and sharing of high-quality data have pushed the field forward, identifying the importance of movement of individuals between discrete populations in the persistence and spread of infectious diseases
    4. 10.1073/pnas.2015730117
    5. Trip duration modifies spatial spread of infectious diseases
    1. 2020-09-01

    2. Ehlert, A., Kindschi, M., Algesheimer, R., & Rauhut, H. (2020). Human social preferences cluster and spread in the field. Proceedings of the National Academy of Sciences, 117(37), 22787–22792. https://doi.org/10.1073/pnas.2000824117

    3. While it is undeniable that the ability of humans to cooperate in large-scale societies is unique in animal life, it remains open how such a degree of prosociality is possible despite the risks of exploitation. Recent evidence suggests that social networks play a crucial role in the development of prosociality and large-scale cooperation by allowing cooperators to cluster; however, it is not well understood if and how this also applies to real-world social networks in the field. We study intrinsic social preferences alongside emerging friendship patterns in 57 freshly formed school classes (n = 1,217), using incentivized measures. We demonstrate the existence of cooperative clusters in society, examine their emergence, and expand the evidence from controlled experiments to real-world social networks. Our results suggest that being embedded in cooperative environments substantially enhances the social preferences of individuals, thus contributing to the formation of cooperative clusters. Partner choice, in contrast, only marginally contributes to their emergence. We conclude that cooperative preferences are contagious; social and cultural learning plays an important role in the development and evolution of cooperation.
    4. 10.1073/pnas.2000824117
    5. Human social preferences cluster and spread in the field
    1. 2020-09-15

    2. Gallagher, R. J., Doroshenko, L., Shugars, S., Lazer, D., & Welles, B. F. (2020). Sustained Online Amplification of COVID-19 Elites in the United States. ArXiv:2009.07255 [Physics]. http://arxiv.org/abs/2009.07255

    3. The ongoing, fluid nature of the COVID-19 pandemic requires individuals to regularly seek information about best health practices, local community spreading, and public health guidelines. In the absence of a unified response to the pandemic in the United States and clear, consistent directives from federal and local officials, people have used social media to collectively crowdsource COVID-19 elites, a small set of trusted COVID-19 information sources. We take a census of COVID-19 crowdsourced elites in the United States who have received sustained attention on Twitter during the pandemic. Using a mixed methods approach with a panel of Twitter users linked to public U.S. voter registration records, we find that journalists, media outlets, and political accounts have been consistently amplified around COVID-19, while epidemiologists, public health officials, and medical professionals make up only a small portion of all COVID-19 elites on Twitter. We show that COVID-19 elites vary considerably across demographic groups, and that there are notable racial, geographic, and political similarities and disparities between various groups and the demographics of their elites. With this variation in mind, we discuss the potential for using the disproportionate online voice of crowdsourced COVID-19 elites to equitably promote timely public health information and mitigate rampant misinformation.
    4. 2009.07255
    5. Sustained Online Amplification of COVID-19 Elites in the United States
    1. 2020-09

    2. Moreau, D., & Gamble, B. (2020). Conducting a meta-analysis in the age of open science: Tools, tips, and practical recommendations. Psychological Methods, No Pagination Specified-No Pagination Specified. https://doi.org/10.1037/met0000351

    3. Psychology researchers are rapidly adopting open science practices, yet clear guidelines on how to apply these practices to meta-analysis remain lacking. In this tutorial, we describe why open science is important in the context of meta-analysis in psychology, and suggest how to adopt the 3 main components of open science: preregistration, open materials, and open data. We first describe how to make the preregistration as thorough as possible—and how to handle deviations from the plan. We then focus on creating easy-to-read materials (e.g., search syntax, R scripts) to facilitate reproducibility and bolster the impact of a meta-analysis. Finally, we suggest how to organize data (e.g., literature search results, data extracted from studies) that are easy to share, interpret, and update as new studies emerge. For each step of the meta-analysis, we provide example templates, accompanied by brief video tutorials, and show how to integrate these practices into the Open Science Framework (https://osf.io/q8stz/). (PsycInfo Database Record (c) 2020 APA, all rights reserved)
    4. 10.1037/met0000351
    5. Conducting a meta-analysis in the age of open science: Tools, tips, and practical recommendations.
    1. 2020-09-13

    2. (((Howard Forman))) on Twitter. (n.d.). Twitter. Retrieved September 16, 2020, from https://twitter.com/thehowie/status/1305232493071736834

    3. We can't predict the future. But we know only fools repeat errors of the past over & over without giving consideration to the best evidence available. The next 2 weeks will fill in a lot of details. Be patient. Be vigilant. Be careful. #MaskUp #SocialDistance #TestTestTest 8/end
    4. For those who think we are reopening too slow, look to Sweden: they are not relaxing measures until October. Their Universities remain in hybrid format. Or look to Israel, where they are on the cusp of a full lockdown after throwing caution to the wind. 7/8
    5. College testing skews data. In NYS, Tompkins county(Ithaca/Cornell) is responsible for 0.5% of the population but 4% of the total testing. 0.2% positive rate. OTOH - Oswego (SUNY) -0.5% of pop is responsible for 0.5% of total testing with 5% positive rate. 6/8
    6. We KNOW we have more college testing. We also know testing, overall, is trending lower = non-college-related testing is dropping sharply. Over the last 2 weeks many hot-spots have either already re-opened bars or have announced plans to do so. 5/8
    7. Case counts continue to drift lower. But we have recent experience where average age of cases drifted lower when bars reopened & severe cases showed up much later. Florida, for instance, hit a low for positive rate in late May - 2 weeks later cases started to take off. 4/8
    8. This is reducing the positive rate, giving us a less reliable indicator. We know from hospitalization and death data a lot about infections that happened in early to mid August, and that news is good. But what is happening NOW? I don't think we know enough. 3/8
    9. The national figures that we have come to rely on are more reliable but subject to MUCH more challenging interpretation than at any time. A LARGE percent (20% of Illinois in this article) of current tests are from colleges doing MASSIVE testing, in some cases. 2/8
    10. Thread/ Colleges, testing, & why we need caution. I am FAR LESS worried about the students. I worry about this - "One major risk is that infections could spread to at-risk faculty and staff and those in the surrounding community." 1/8
    11. 2020-09-12

    12. The COVID Tracking Project on Twitter. (n.d.). Twitter. Retrieved September 16, 2020, from https://twitter.com/COVID19Tracking/status/1304910646404739073