It’s #PeerReviewWeek, that time of the year when scholarly communication folks draw  attention to peer review. The 2020 theme is “Trust in Peer Review,” focusing  on how peer review is done and why it builds trust in research. 

The recent outbreak of the novel coronavirus (COVID-19) has infected millions of citizens worldwide and claimed many lives. This pandemic has changed human life in many ways, and the research community is putting efforts to understand these impacts in various sectors. This paper focuses on its impact on the Chinese market by analyzing the e-commerce behavioral changes seen at an online shopping platform before and during the pandemic. We present how the epidemic affected consumer behaviors via time series analysis. We find that disruptions in the supply of essential goods needed during a disaster can be leveraged for epidemic forecasting, and further design a hierarchical attention learning model to predict the newly confirmed cases. Experiment results demonstrate that our predictions outperform existing baselines, which also extends to the long-term and province-level forecasts. We believe models like ours contribute to better preparing for future epidemics by gaining extra time to launch preventive steps.

The COVID-19 pandemic poses considerable challenges that threaten health and well-being. Initial data supports that many people experienced elevated psychological distress as the pandemic emerged. Yet, prior examinations of average changes in well-being fail to identify who is at greater risk for poor psychological health. The aim of the current research was to examine whether the use of different emotion regulation strategies (emotional suppression, rumination, cognitive reappraisal) predicted residual changes in psychological and physical health during a nationwide COVID-19 lockdown. We leveraged an ongoing study in which participants had reported on their psychological and physical health prior to the pandemic. Participants then reported on the same health outcomes as well as their use of emotion regulation strategies, stress and emotion control difficulties during a nationwide lockdown involving confinement in the home for 5 weeks. Accounting for pre-pandemic psychological health, greater emotional suppression and rumination predicted greater depressive symptoms, lower emotional well-being, greater limitations due to emotional problems, and poorer social functioning during the lockdown, even when controlling for the detrimental effects of stress and emotion control difficulties. Accounting for pre-pandemic physical health, greater rumination predicted greater fatigue and poorer physical health, but the amount of stress people experienced was a stronger predictor across physical health outcomes. The results validate concerns that the stress of the pandemic risks declines in psychological and physical health and identify emotional suppression and rumination as important risk factors of poor psychological health during the COVID-19 pandemic.

Objective: How does our personality relate to the ways in which we judge right from wrong? Drawing on influential theories of moral judgment, we identify candidate traits that may be linked with inclinations toward (a) consequentialist judgments (i.e., those based on the outcomes of an action) and (b) deontological judgments (i.e., those based on the alignment of an action with particular moral rules). Method: Across two studies (total N = 843), we examined domains and aspects of the Big Five in relation to inclinations toward consequentialist and deontological judgments. Results: In both studies, we found a unique association between intellect (curiosity, cognitive engagement) and consequentialist inclinations, in line with the view that deliberative cognitive processes drive such inclinations. We also found a consistent unique association between politeness (respectfulness, etiquette) and deontological inclinations, in line with the view that norm-adherence drives such inclinations. Neither study yielded a significant unique relation between deontological inclinations and compassion (sympathy, empathic concern)—or any other emotion-infused trait (e.g., neuroticism)—as would be expected based on emotion- centered views of deontological moral judgment. Conclusions: These findings have implications for theories of moral judgment, and reveal how our personality guides our approach to questions of ethics and morality.

Speaking at a campaign rally in Ohio on Tuesday, President Trump doubled down on previous comments he’s made downplaying the severity of COVID-19, arguing that the coronavirus “affects virtually nobody,” except the elderly and those with “other problems.” The president’s words categorically dismissed the importance of protecting people with preexisting health issues, including more than a quarter of the U.S. population who were born with or have acquired a disability.

Importance: The global infection outbreak by the new SARS-CoV-2 prompted community containment schedules; however, social isolation is a predictor of psychological distress. Objective: To determine whether social isolation, in Brazil, led to higher signs of psychological distress, and which intra- and inter-psychic variables mediated this effect. The following hypotheses were tested: 1) in isolated individuals, loneliness activates distancing and escape-avoidance coping strategies with intensities that are directly correlated with symptoms of anxiety and common health disorders; 2) in isolated individuals, poor reliance on social support coping strategies increase the effects of loneliness on symptoms of anxiety and common mental disorders; 3) in isolated individuals, External Entrapment moderates the effects of loneliness such that the higher the feelings of entrapment, the higher the effects of loneliness on symptoms of anxiety and common mental disorders. 4) in both isolated and non-isolated individuals, intense reliance on positive reappraisal coping strategies decrease (moderate) the effects of information consumption on symptoms of anxiety and common mental disorders. No a priori hypothesis on the specific nature of the subjective experiences of social isolation were established. We proposed that the semantic field of social isolation should present a complex and multidimensional nature. Design: Two phases of web-based surveys were applied to participants between May 25th 2020 and August 19th 2020. Setting: Brazilian participants responded surveys on the web. Participants: For Phase 1, 440 participants responded to the survey. Participants were a volunteer sample of Brazilian nationality and above 18 years old. For Phase 2, a sub-sample of 55 participants was drawn from the pool of the first phase. Main outcomes and measures: For Phase 1, the primary endpoint was score in the SRQ-20 scale (an instrument to screen symptoms of common mental disorders), and the secondary endpoint was score in an anxiety scale that screened feelings of anxiety related to illness and medical procedures. Results: For Phase 1, 51% of the sample reported leaving the house less than once a week during the period of the research, 27.6% reported leaving the house 1-2 times per week, 9.8% reported leaving the house 3-4 times per week, and 11.6% reported leaving the house every day. Using SRQ-20, we found that 76.9% of the female respondents and 58.0% of the male respondents that reported leaving their houses less than once a week showed clinically significant symptoms, while these proportions fell below 65% for females and 44% for males that reported leaving their houses more than 3 times per week. Reliance on escape/avoidance as well as distancing coping strategies were significant mediators of the effect of isolation-induced loneliness. We did not find support for the hypothesis that reliance on social support coping strategies significantly altered the effects of social isolation-induced loneliness on psychological distress, nor for the hypothesis that external entrapment moderated the effects of loneliness. We also found that the impact of reliance on positive reappraisal coping strategies on the relationship between frequency of media use for COVID-19-related information and psychological distress depended on the type of media, with individuals which sought information from print or online newspapers, social networks, and podcasts at higher frequencies consistently showing more psychological distress; however, higher levels of positive reappraisal coping strategies increased this impact instead of decreasing it. In Phase 2 (qualitative survey), 47.3% of the sample reported leaving the house less than once a week during the period of the research, 21.8% reported leaving the house 1-2 times per week, 10.9% reported leaving the house 3-4 times per week, and 20% reported leaving the house every day. At the qualitative survey we found that individuals interpreted isolation as producing self-assessment with protective and introspective dimensions, but also ruminative and emotional experiences of distress. Conclusions and relevance: Our results reveal that social isolation during the COVID-19 pandemic significantly increased psychological distress at clinically relevant rates, with loneliness being an important predictor of this effect. We also found that escape-avoidance and distancing coping strategies mediated this effect. Psychological distress was also related to high consumption of COVID-19-related information in social networks, print or online newspapers, and podcasts, but that relying on positive reappraisal coping strategies increased this effect instead of decreasing it. Our results suggest the need for policies that diminish the impact of social isolation on mental health; the need to assess and teach alternative coping strategies in clinical settings; and the need to address the impact of Internet-based sources.

Individuals generally revise their misconceptions when corrected with carefully designed educational materials. However, early reports suggest that correcting COVID-19 misconceptions may be especially challenging, which may be due to conflicts with individuals’ moral values and emotions. The present study explores the mechanisms and boundaries of correction effectiveness. Those highest in moral concerns for group cohesion or for individual freedoms were more likely to affectively or cognitively reject corrective information. Corrections of COVID-19 misconceptions should be carefully framed to connect with the morality of recipients and anticipate their emotional and cognitive reactions.

This study will explore how COVID-19 impacts physical and mental health among convenience samples taken from the general US adult population to improve health and wellbeing for vulnerable groups. Any knowledge thus gained should clarify how the pandemic has affected the overall physical and mental health of these individuals. The CHSS approach to measuring health status should help evaluate general populational needs, treatments, and programs in the COVID-19 context. Study significance. There is a lack of research on how populational health is affected by the COVID-19 pandemic, especially in the context of physical and mental health functioning among the general population. This study holds significant potential in three main areas. First, the US faces an unprecedented and sweeping pandemic, which threatens both the quality of life and life itself across a broad populational spectrum. It is urgent to enhance our understanding of how COVID-19 affects the general population, which requires a rapid investigation of its widespread effects on physical and mental health. Second, the knowledge gained from exploring the relationship between COVID-19 and physical/mental health status will help design new clinical and community interventions that are tailored to general populational needs. Any new health and welfare interventions must be evaluated based on their actual impact. Third, a reliable, valid, and practical outcome measure is a priority area for understanding the physical and mental health consequences of COVID-19. Although physical and mental health care outcomes can be assessed through a variety of parameters, such as satisfaction and cost, health status is the most important.

Gendered languages assign masculine and feminine grammatical gender to all nouns, including nonhuman entities. In French, Italian, and Spanish, the name of the disease resulting from the virus (COVID-19) is grammatically feminine, whereas the virus that causes the disease (coronavirus) is masculine. In this research, we test whether the grammatical gender mark matters. In a series of experiments with French and Spanish speakers, we find that grammatical gender affects virus-related judgments consistent with gender stereotypes: feminine- (vs. masculine-) marked terms for the virus decrease perceptions of future danger of the virus and reduce intentions to take precautionary behavioral measures to mitigate contraction and spread of the virus (e.g., avoiding restaurants, movies, travel). Secondary data analyses of online search behavior for France, Spain, and Italy further demonstrate this negative relation between the anticipated threat (daily new cases and deaths, search for masks) and usage of the feminine- (vs. masculine-) marked terms for the coronavirus. These effects occur even though the grammatical gender assignment is semantically arbitrary.

We read with interest the Comment by Emanuel Goldman1Goldman E Exaggerated risk of transmission of COVID-19 by fomites.Lancet Infect Dis. 2020; (published online July 3.)https://doi.org/10.1016/S1473-3099(20)30561-2Summary Full Text Full Text PDF Scopus (2) Google Scholar highlighting experiments done under controlled laboratory conditions that suggest persistence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on inanimate surfaces for days, with potential implications for viral transmission.2van Doremalen N Bushmaker T Morris DH et al.Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1.N Engl J Med. 2020; 382: 1564-1567Crossref PubMed Scopus (1409) Google Scholar Yet, at the same time, Goldman laments the absence of real-life studies investigating the infectious potential of SARS-CoV-2 on contaminated inanimate material and patient fomites, particularly in high-risk hospital wards. A study done in a hospital environment showed that most surfaces were contaminated, including air-conditioning vents, bed rails, bedside lockers, and rarely, toilets.3Chia PY Coleman KK Tan YK et al.Detection of air and surface contamination by SARS-CoV-2 in hospital rooms of infected patients.Nat Commun. 2020; 112800Crossref PubMed Scopus (25) Google Scholar Of note, environmental surface contamination declined after week 1 of illness, and intriguingly, no surface contamination was detected in intensive care unit (ICU) rooms. A limitation of the study by Chia and colleagues3Chia PY Coleman KK Tan YK et al.Detection of air and surface contamination by SARS-CoV-2 in hospital rooms of infected patients.Nat Commun. 2020; 112800Crossref PubMed Scopus (25) Google Scholar is that no attempt was made to culture SARS-CoV-2 from the environmental swabs, which would have helped to understand the significance of SARS-CoV-2 RNA positive samples in terms of infectious potential.

Jails and prisons are exceptionally susceptible to viral outbreaks, such as severe acute respiratory syndrome coronavirus 2. The USA has extremely high rates of incarceration and COVID-19 is causing an urgent health crisis in correctional facilities and detention centres. Epidemics happening in prisons are compounding the elevated risks that COVID-19 poses to people of colour, older people, and those with comorbidities. Intersectoral community re-entry efforts in the USA and other countries have shown that releasing people from correctional facilities as a pandemic-era public health intervention is safe and can support both public safety and community rebuilding. Therefore, substantial decarceration in the USA should be initiated. A point of focus for such efforts is that many people in prison are serving excessively long sentences and pose acceptable safety risks for release. Properly managed, correctional depopulation will prevent considerable COVID-19 morbidity and mortality and reduce prevailing socioeconomic and health inequities.

Damage to the heart, liver and kidney are just a few of the long-term effects of Covid-19, according to a Oxford-based study. Academics have already warned people could suffer from the impact of having coronavirus for years, with many experiencing prolonged symptoms. Now, a joint national research programme between the EU, and private companies Innovate UK and Perspectum, called COVERSCAN is analysing the effects of Covid-19 on the body's key organs.

The trend in job postings was 17.5% lower than in 2019 as of September 25 — the smallest gap with last year’s trend since late March.

Earlier this semester, a visiting professor from New York City gave a lecture to a class at Yale University. The professor later tested positive for COVID-19 — and so did two students who attended the class, according to Dr. Maritza Bond, New Haven’s director of health . Officials have not identified any other exposures in the New Haven community pertaining to the event, and no other students in the class had tested positive for COVID-19, Bond said. When asked for comment on the matter, Yale University spokeswoman Karen Peart said in an email that Yale became aware earlier this month of three COVID-19 cases “among people who had been present at an in-person class of fewer than 20 students.”

Misinformation promoting spurious treatments can have serious consequences, arising both from direct harm and opportunity costs. The COVID-19 pandemic has seen a surge of health misinformation, which fact-checkers have struggled to cope with. We investigated (N = 678) the impact of such health misinformation on two behavioral measures, viz. willingness to pay for a spurious treatment, and propensity to share misinformation online. This is a novel approach, as previous research has used mainly questionnaire-based measures. We also compared two interventions to counteract the misinformation, viz. a standard refutation based on materials used by health authorities, and an enhanced refutation based on best-practice recommendations. We found exposure to misinformation promoted sharing of misinformation online, and that both refutations reduced demand for the spurious treatment and discouraged misinformation sharing. Importantly, the enhanced refutation had significantly greater impact on misinformation sharing. This highlights the need for debunking interventions to follow current best-practice guidelines.

For decades, cities relied on performing arts groups to help drive revitalization. Now nearly every company in the country has been shuttered for months, acting as a drag on local business.

In the late stages of an epidemic, infections are often sporadic and geographically distributed. Spatially structured stochastic models can capture these important features of disease dynamics, thereby allowing a broader exploration of interventions. Here we develop a stochastic model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission among an interconnected group of population centers representing counties, municipalities, and districts (collectively, “counties”). The model is parameterized with demographic, epidemiological, testing, and travel data from Ontario, Canada. We explore the effects of different control strategies after the epidemic curve has been flattened. We compare a local strategy of reopening (and reclosing, as needed) schools and workplaces county by county, according to triggers for county-specific infection prevalence, to a global strategy of province-wide reopening and reclosing, according to triggers for province-wide infection prevalence. For trigger levels that result in the same number of COVID-19 cases between the two strategies, the local strategy causes significantly fewer person-days of closure, even under high intercounty travel scenarios. However, both cases and person-days lost to closure rise when county triggers are not coordinated and when testing rates vary among counties. Finally, we show that local strategies can also do better in the early epidemic stage, but only if testing rates are high and the trigger prevalence is low. Our results suggest that pandemic planning for the far side of the COVID-19 epidemic curve should consider local strategies for reopening and reclosing.

Disinformation campaigns used to require a lot of human effort, but artificial intelligence will take them to a whole new level.

Clusters of cases have emerged in Brooklyn and New York City’s northern suburbs, in areas with large Orthodox Jewish communities.

present paper presents a SARS-CoV-2 epidemic model to serve as abasis for estimating the incidence of infection, and shows mathemati-cally how modeled transmission dynamics translate into infection indi-cators by incorporating probability distributions for indicator-specifictime lags from infection. Hospital admissions and SARS-CoV-2 RNAin municipal sewage sludge are simultaneously modeled via maximumlikelihood scaling to the underlying transmission model. The resultsdemonstrate that both data series plausibly follow from the transmis-sion model specified and provide a 95% confidence interval estimate ofthe reproductive number0≈24±02. Sensitivity analysis account-ing for alternative lag distributions from infection until hospitalizationand sludge RNA concentration respectively suggests that the detectionof viral RNA in sewage sludge leads hospital admissions by 3 to 5 dayson average. The analysis suggests that stay-at-home restrictions plau-sibly removed 89% of the population from the risk of infection withthe remaining 11% exposed to an unmitigated outbreak that infected9.3% of the total population.

Ascertaining the state of coronavirus outbreaks is crucial for pub-lic health decision-making. Absent repeated representative viral testsamples in the population, public health officials and researchers alikehave relied on lagging indicators ofinfection to make inferences aboutthe direction of the outbreak and attendant policy decisions. Recentlyresearchers have shown that SARS-CoV-2 RNA can be detected in mu-nicipal sewage sludge with measured RNA concentrations rising andfalling suggestively in the shape of an epidemic curve while provid-ing an earlier signal of infection than hospital admissions data. The

We can learn from parallels between the coronavirus and HIV crises

The assays may not detect all cases, but they are cheap and could be used at home

The repliCATS project evaluates published scientific research. As the acronym—Collaborative Assessments for Trustworthy Science—suggests, repliCATS is a group activity, centred around assessing the trustworthiness of research claims. Reviewers first make private individual assessments about a research claim—judging its comprehensibility, the prior plausibility of underlying effect, and its likely replicability. Reviewers then share their judgements and reasoning with group members, providing both new information and the opportunity for feedback and calibration. The group interrogates differences in opinion and explores counterfactuals. After discussion, there is a final opportunity for privately updating individual judgements. Importantly the repliCATS process is not consensus-driven – reviewers can disagree, and their ratings and probability judgements are mathematically aggregated into a final assessment. At the moment, the repliCATS platform exists primarily to predict replicability. Launched in January 2019 as part of the DARPA SCORE program, over 18 months repliCATS elicited group assessments and captured associated reasoning and discussion, for 3,000 published social scientific research claims in 8 disciplines (Business, Criminology, Economics, Education, Political Science, Psychology, Public Administration, and Sociology). The repliCATS team are now working to extend the platform beyond merely predicting replicability, to deliver a more comprehensive peer review protocol. Suspected advantages of a repliCATS process over traditional peer review include: inbuilt training and calibration; feedback that is intrinsically rewarding; an inherently interactive process, but one which does not implicitly rely on ‘consensus by fatigue’; and a process that actively encourages interrogation. This talk will present some preliminary findings, and discuss the future of the platform.

The COVID-19 pandemic has challenged health systems around the world, displacing attention to other much-needed services and conditions. It has particularly impacted access to sexual and reproductive health goods and services⁠—not only in the U.S., as discussed in Reproductive Rights in 2020: June Medical Services v. Russo and COVID-19, but around the globe. While in some places governments have made concerted efforts to mitigate the displacement of sexual and reproductive health services by telehealth and other means, in many others the pandemic has provided cover for policies that neglect and even undermine reproductive health and rights. Reproductive rights movements and mobilizations (including around abortion) have been interrupted; contraception access has been affected; and sexual and obstetric violence have both increased. Join us for a discussion of the impact that COVID-19 has had on sexual and reproductive health and rights around the world.

The Conference on Computational Sociology will showcase work that applies computational methods to important sociological problems. Presented research will display creative use of data, whether applying machine learning methods, analyzing text, images, or network structures, or any other form of computational analysis.

Machine learning methods, combined with large electronic health databases, could enable a personalised approach to medicine through improved diagnosis and prediction of individual responses to therapies. If successful, this strategy would represent a revolution in clinical research and practice. However, although the vision of individually tailored medicine is alluring, there is a need to distinguish genuine potential from hype. We argue that the goal of personalised medical care faces serious challenges, many of which cannot be addressed through algorithmic complexity, and call for collaboration between traditional methodologists and experts in medical machine learning to avoid extensive research waste.

The development and distribution of a COVID-19 vaccine has the potential to greatly change the course of the pandemic; however, ensuring equitable access will require countries, organisations, and corporations to place their trust in global health. The COVID-19 vaccine initiative (COVAX) shows how public–private partnerships can exacerbate existing chasms1Spinney L How the race for a Covid-19 vaccine is getting dirty.https://www.theguardian.com/society/2020/aug/30/how-the-race-for-a-covid-19-vaccine-got-dirtyDate: Aug 30, 2020Date accessed: September 5, 2020Google Scholar or allow organisations, such as WHO, to guide a realistic and adequate approach.Development of vaccines that meet regulatory and licensing requirements involves high costs in terms of facilities, equipment, and human resources and is a lengthy process that often fails. The high cost restricts many countries from developing a vaccine,2Plotkin S Robinson JM Cunningham G Iqbal R Larsen S The complexity and cost of vaccine manufacturing—an overview.Vaccine. 2017; 35: 4064-4071Crossref PubMed Scopus (59) Google Scholar which causes low income and middle-income countries to rely on research and development from more powerful economies. Additionally, research highlights the challenges in reaching population-level effectiveness with a vaccine, regardless of production capacity, because of weak delivery infrastructures and barriers to access that determine uptake.3

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in late 2019 in China and caused a coronavirus disease 2019 (COVID-19) pandemic. To mitigate the public health, economic and societal impacts of the virus, a vaccine is urgently needed. The development of SARS-CoV-2 vaccines was initiated in early January 2020 when the sequence of the virus became available and moved at record speed with one Phase I trial already starting in March 2020 and currently more than 180 vaccines in various stages of development. Phase I/II trial data is already available for several vaccine candidates and many have moved into Phase III trials. The data available so far suggests that effective and safe vaccines might become available within months rather than years.

Christian Drosten helped spare his country from the worst of Covid. Now he’s worried about the second wave.

Sir Simon Stevens rejects idea proposed by scientists to contain Covid second wave

Incorporating nudging and other behavioral insights from psychological science into public policy has become de rigueur for governments around the world. Nudging was originally developed as an attempt to reconcile state intervention and individual liberty. It is based on the assumption that people’s cognitive abilities and self-control are so limited relative to the complexity of the world that structural changes to their environments, or “choice architectures,” are often required for them to act in their own best interest.

Wales' two biggest cities have gone into lockdown, which started at 18:00.

The government says that it wants to learn from what went wrong with the Covid-19 response and that a public inquiry will be held. What lessons can be learnt, what are the psychological underpinnings of accountability in policy and can we balance the tension between learning lessons and apportioning blame in order to inform better decision making in the future?

learning online is not always easy. How do you concentrate when staring at a screen for hours at a time? How do you manage your workload? And what is the best strategy for note-taking? Here’s our digest of the findings that could help to make online learning stick.

Anxiety about social distancing and infection is altering how much we dream and the nature of our dreams themselves

A new nationwide study of the blood of more than 28,000 dialysis patients may help answer one of the big questions surrounding the pandemic: how many people have antibodies to SARS-CoV-2, the novel coronavirus? The answer could tell us a lot about how many people in the United States have been exposed to the virus and how much community spread there has been. Plus, the testing strategy used in the study points to a relatively easy way to track Covid-19 disease activity over the long-term, especially among vulnerable populations. The short answer to how many people had antibodies, as of July, is approximately 9.3% -- although numbers ranged from an average of 3.5% in the West to an average of 27% in the Northeast.

The COVID-19 outbreak has forced individuals to adjust to a new order in which their liberties are restricted and uncertainty rules. The current work examined the role of other-focusedemotion regulation (ER) training in enhancing coping efficacy and reducing COVID-19 worries. For that, during the first COVID-19 lockdown in Israel, we trained 59 young individuals to perform other-focused emotion regulation, by reappraising others’ written upsetting events. We compared this procedure to a self-reappraisal training, in which 49 participants were asked to reappraise own upsetting events. Both procedures were performed every other day for three weeks. Participants’ coping efficacy was assessed at the daily level, while worries concerning theCOVID-19 effects on health, economic status, and social life were assessed following the training and at a two-month follow-up. The results demonstrated that other-focused ER surpassed self-focused ER. Specifically, participants in the other-reappraisal group exhibited an increase in coping efficacy across the training sessions and a reduction in COVID-19 worries that persisted two months after the training. These findings highlight the role of interpersonal emotion regulation at times of crisis and social isolation.

Uncertainty has been shown to impact political evaluation, yet the exact mechanisms by which uncertainty affects the minds of citizens remain unclear. This experiment examines the neural underpinnings of uncertainty in political evaluation using functional MRI (fMRI). During fMRI, participants completed an experimental task where they evaluated policy positions attributed to hypothetical political candidates. Policy positions were either congruent or incongruent with candidates’ political party affiliation and presented with varying levels of certainty. Neural activity was modeled as a function of uncertainty and incongruence. Analyses suggest that neural activity in brain regions previously implicated in affective and evaluative processing (anterior cingulate cortex, insular cortex) differed as a function of the interaction between uncertainty and incongruence, such that activation in these areas was greatest when information was both certain and incongruent and uncertainty influenced processing differently as a function of the valence of the attached information. These findings suggest that individuals are attuned to uncertainty in the stated issue positions of politicians, and that the neural processing of this uncertainty is dependent on congruence of these positions with expectations based on political party identification. Implications for the study of emotion and politics and political cognition are discussed.

In response to the COVID-19 pandemic, many Governments are instituting mobile tracking technologies to perform rapid contact tracing. However, these technologies are only effective if the public is willing to use them, implying that their perceived public health benefits must outweigh personal concerns over privacy and security. The Australian federal government recently launched the `COVIDSafe' app, designed to anonymously register nearby contacts. If a contact later identifies as infected with COVID-19, health department officials can rapidly followup with their registered contacts to stop the virus' spread. The current study assessed attitudes towards three tracking technologies (telecommunication network tracking, a government app, and Apple and Google's Bluetooth exposure notification system) in two representative samples of the Australian public prior to the launch of COVIDSafe. We compared these attitudes to usage of the COVIDSafe app after its launch in a further two representative samples of the Australian public. Using Bayesian methods, we find widespread acceptance for all tracking technologies, however, observe a large intention-behaviour gap between people’s stated attitudes and actual uptake of the COVIDSafe app. We consider the policy implications of these results for Australia and the world at large.

Context: Physical distancing and health measures, such as school closures and work-at-home mandates, implemented to mitigate the transmission of COVID-19, will likely have far-reaching impacts on children’s psychosocial health and well-being. Objective: We aimed to examine extant literature on pandemics to identify the expected impact of COVID-19 on children’s psychosocial health and secondary outcomes, including nutritional, financial and child safety outcomes. Data Sources: Articles were searched within the Medline, Global Health, PsycINFO, and CINAHL databases on June 11th, 2020. Gray literature was also examined from the World Health Organization (WHO) and United Nations International Fund (UNICEF) until July 24th, 2020. Study Selection: A total of 8332 articles were screened for eligibility by two independent reviewers. Of these, 7,919 and 413 articles were from academic databases and additional sources, respectively. Data Extraction: Results on child outcomes were extracted and collated. Results: Seventy-three articles met inclusion criteria. Children have faced significant challenges with 12% of total articles indicating loneliness/depression, 21% anxiety, 7% grief, 10% stress-related disorders, 25% child abuse, 8% family conflict, and 12% stigma during pandemics/epidemics. Furthermore, 25% of articles indicated economic challenges, 23% negative academic impacts, 33% improper nutrition, and 21% reduced opportunities for play/increased screen time. These challenges were exacerbated among children who were female, having a disability, or being a migrant/displaced child. Conclusion: Pandemics and epidemics have had diverse and widespread negative consequences for children. Findings can inform the development and implementation of resources during the COVID-19 pandemic to protect child health and well-being.

In the study of infectious diseases on networks, researchers calculate epidemic thresholds as informative measures to help forecast whether a disease will eventually infect a large fraction of a population. The structure of network typically changes in time, which fundamentally influence the dynamics of spreading processes on them and in turn affect epidemic thresholds for disease propagation. Most existing studies on epidemic thresholds in temporal networks have focused on models in discrete time, but most real-world networked systems evolve continuously in time. In our work, we encode the continuous dependency of time into the evaluation of the epidemic threshold of an susceptible--infected--susceptible (SIS) process by studying an SIS model on tie-decay networks. We formulate epidemic thresholds for an SIS compartmental model of disease spread on tie-decay networks, and we perform numerical experiments to verify the threshold condition that we derive. We also examine how different factors---the decay coefficients of the networks, the frequency of interactions, and the sparsity of the underlying social network in which interactions occur---lead to decreases or increases of the critical values of the threshold condition and hence contribute to facilitating or impeding the spread of a disease. We thereby demonstrate how the tie-decay features of these networks alter the outcome of disease spread.

Ten thousand UK volunteers have been invited to join the world’s first phase three COVID-19 vaccine study to test the effectiveness of the new Novavax COVID-19 vaccine. This comes as the number of people in the UK who have signed up to take part in COVID-19 research through the NHS COVID-19 vaccine research registry hits 250,000.  Participants aged between 18-85 years will help test the safety and effectiveness of a new COVID-19 vaccine, developed by US biotechnology company Novavax, at a number of National Institute for Health Research (NIHR) regional sites across the UK, including Lancashire, the Midlands, Greater Manchester, London, Glasgow and Belfast. The Novavax trials are Phase 3, a large trial focusing on the vaccine’s effectiveness, with further checks on safety in a larger population. This followed earlier, smaller studies (phase 1 and phase 2) that reported positive results, and which focused on safety and whether there were signs the vaccine could work.

Student union says move disregards mental health and ‘applies different rules from the rest of the adult population’

This paper analyzes the human and financial costs of the COVID-19 pandemic on 92 countries. We compare country-by-country equity market dynamics to cumulative COVID-19 case and death counts and new case trajectories. First, we examine the multivariate time series of cumulative cases and deaths, particularly regarding their changing structure over time. We reveal similarities between the case and death time series, and key dates that the structure of the time series changed. Next, we classify new case time series, demonstrate five characteristic classes of trajectories, and quantify discrepancy between them with respect to the behavior of waves of the disease. Finally, we show there is no relationship between countries' equity market performance and their success in managing COVID-19. Each country's equity index has been unresponsive to the domestic or global state of the pandemic. Instead, these indices have been highly uniform, with most movement in March.

Forecasting plays a critical role in the development of organisational business strategies. Despite a considerable body of research in the area of forecasting, the focus has largely been on the financial and economic outcomes of the forecasting process as opposed to societal benefits. Our motivation in this study is to promote the latter, with a view to using the forecasting process to advance social and environmental objectives such as equality, social justice and sustainability. We refer to such forecasting practices as Forecasting for Social Good (FSG) where the benefits to society and the environment take precedence over economic and financial outcomes. We conceptualise FSG and discuss its scope and boundaries in the context of the "Doughnut theory". We present some key attributes that qualify a forecasting process as FSG: it is concerned with a real problem, it is focused on advancing social and environmental goals and prioritises these over conventional measures of economic success, and it has a broad societal impact. We also position FSG in the wider literature on forecasting and social good practices. We propose an FSG maturity framework as the means to engage academics and practitioners with research in this area. Finally, we highlight that FSG: (i) cannot be distilled to a prescriptive set of guidelines, (ii) is scalable, and (iii) has the potential to make significant contributions to advancing social objectives.

Europe’s flagship science agency will be crucial to a post-coronavirus world. Slashing its budget will be a senseless act.

The big picture: Since the onset of pandemic-induced telework, companies have oscillated between can't-wait-to-go-back and work-from-home-forever. Now, it's becoming increasingly clear that the future of work will land somewhere in the middle — a remote/in-person hybrid.

In this tutorial, we descibe an expert consensus procedure for scientific projects. Following such a procedure can achieve two main aims: (1) to decrease the chance that the product will be subject to conceptual or methodological mistakes, and (2) to increase the chance that the product of the project would be acceptable by the stakeholders on the field. The expert consensus procedure (ECO) aims to facilitate consensus among a panel of experts on a target output. This is achieved through an iterative process including surveys and feedback to the panel members, at the end of which a consensus-based Target Output is created, or lack of consensus is declared. The paper provides a step-by-step guide for the implementation of the ECO.

Background The number of proposed prognostic models for COVID-19 is growing rapidly, but it is unknown whether any are suitable for widespread clinical implementation.Methods We independently externally validated the performance candidate prognostic models, identified through a living systematic review, among consecutive adults admitted to hospital with a final diagnosis of COVID-19. We reconstructed candidate models as per original descriptions and evaluated performance for their original intended outcomes using predictors measured at admission. We assessed discrimination, calibration and net benefit, compared to the default strategies of treating all and no patients, and against the most discriminating predictor in univariable analyses.Results We tested 22 candidate prognostic models among 411 participants with COVID-19, of whom 180 (43.8%) and 115 (28.0%) met the endpoints of clinical deterioration and mortality, respectively. Highest areas under receiver operating characteristic (AUROC) curves were achieved by the NEWS2 score for prediction of deterioration over 24 h (0.78; 95% CI 0.73–0.83), and a novel model for prediction of deterioration <14 days from admission (0.78; 0.74–0.82). The most discriminating univariable predictors were admission oxygen saturation on room air for in-hospital deterioration (AUROC 0.76; 0.71–0.81), and age for in-hospital mortality (AUROC 0.76; 0.71–0.81). No prognostic model demonstrated consistently higher net benefit than these univariable predictors, across a range of threshold probabilities.Conclusions Admission oxygen saturation on room air and patient age are strong predictors of deterioration and mortality among hospitalised adults with COVID-19, respectively. None of the prognostic models evaluated here offered incremental value for patient stratification to these univariable predictors.

The pandemic closed hundreds of thousands of businesses across the country. But now applications for new U.S. businesses are rising at the fastest rate since 2007. Why? A mix of necessity and opportunity.

We present a modelling framework for the spreading of epidemics on temporal networks from which both the individual-based and pair-based models can be recovered. The proposed pair-based model that is systematically derived from this framework offers an improvement over existing pair-based models by moving away from edge-centric descriptions while keeping the description concise and relatively simple. For the contagion process, we consider the Susceptible-Infected-Recovered (SIR) model, which is realized on a network with time-varying edges. We show that the shift in perspective from individual-based to pair-based quantities enables exact modelling of Markovian epidemic processes on temporal tree networks. On arbitrary networks, the proposed pair-based model provides a substantial increase in accuracy at a low computational and conceptual cost compared to the individual-based model. From the pair-based model, we analytically find the condition necessary for an epidemic to occur, otherwise known as the epidemic threshold. Due to the fact that the SIR model has only one stable fixed point, which is the global non-infected state, we identify an epidemic by looking at the initial stability of the model.

Computational Social Science offers tools that can assist both policy makers and the private sector in the design and implementation of measures to address the economic and social challenges posed by COVID-19. How can the growing sources of (alternative) data and data science be used in a meaningful and responsible way, i.e. providing reliable and practical knowledge without compromising privacy and safety of people whose data is collected and analyzed? What are the methodological and regulatory solutions that could be applied to address the challenges and mitigate the risks? Our panelists will share their experience and ideas on these issues. There will also be enough time for questions from the audience.

The information environment we need would ensure information that is Rapid: facilitating new research, evidence aggregation, and critique in real-time Relevant: managing information flood while delivering information in contents and formats that match the needs of diverse users, from scientists to policy makers Reliable: generating and promoting high quality content The workshop will bring together an interdisciplinary group of experts and practitioners to help conceptualise, plan and build the tools for such an environment.

Objectives: To investigate rates of adherence to the UKs test, trace and isolate system over time. Design: Time series of cross-sectional online surveys. Setting: Data were collected between 2 March and 5 August 2020. Participants: 42,127 responses from 31,787 people living in the UK, aged 16 years or over, are presented (21 survey waves, n≈2,000 per wave). Main outcome measures: Identification of the key symptoms of COVID-19 (cough, high temperature / fever, and loss of sense of smell or taste), self-reported adherence to self-isolation if symptomatic, requesting an antigen test if symptomatic, intention to share details of close contacts, self-reported adherence to quarantine if alerted that you had been in contact with a confirmed COVID-19 case. Results: Only 48.9% of participants (95% CI 48.2% to 49.7%) identified key symptoms of COVID-19. Self-reported adherence to test, trace and isolate behaviours was low (self-isolation 18.2%, 95% CI 16.4% to 19.9%; requesting an antigen test 11.9%, 95% CI 10.1% to 13.8%; intention to share details of close contacts 76.1%, 95% CI 75.4% to 76.8%; quarantining 10.9%, 95% CI 7.8% to 13.9%) and largely stable over time. By contrast, intention to adhere to protective measures was much higher. Non-adherence was associated with: men, younger age groups, having a dependent child in the household, lower socio-economic grade, greater hardship during the pandemic, and working in a key sector. Conclusions: Practical support and financial reimbursement is likely to improve adherence. Targeting messaging and policies to men, younger age groups, and key workers may also be necessary.

Digital transformation is a buzzword that has been around for some time. For many, digital transformation started to kick into full gear in the 1990s as personal computers started to become more prevalent. This is when businesses looked at providing more digital experiences for customers and end users and also began transforming the way they worked internally to keep up with the pace of technology. While nearly every industry is undergoing or at least considering digital transformation, according to McKinsey, “the average digital transformation ... stands a 45 percent chance of delivering less profit than expected.” Nonetheless, businesses are still investing in these initiatives; IDC estimates that worldwide spending on the technologies and services that enable digital transformation will reach $2.3 trillion in 2023. So, why should you embark or continue on a digital transformation journey? And what is digital transformation, exactly? As the president of a company that offers software development and digital transformation services, I'll attempt to outline what success looks like and how to get there.

Purpose of ReviewThe goal of this article is to provide an introduction to the intuition behind the difference-in-difference method for epidemiologists. We focus on the theoretical aspects of this tool, including the types of questions for which difference-in-difference is appropriate, and what assumptions must hold for the results to be causally interpretable.Recent FindingsWhile currently under-utilized in epidemiologic research, the difference-in-difference method is a useful tool to examine effects of population level exposures, but relies on strong assumptions.SummaryWe use the famous example of John Snow’s investigation of the cause of cholera mortality in London to illustrate the difference-in-difference approach and corresponding assumptions. We conclude by arguing that this method deserves a second look from epidemiologists interested in asking causal questions about the impact of a population level exposure change on a population level outcome for the group that experienced the change.

New epidemiological model shows urgent need for large-scale testing

Nafeez Ahmed reveals how a high-profile letter to Boris Johnson was based on ‘fringe pseudoscience’ and co-drafted by a Government advisor who downplayed the COVID-19 death toll

Blood donations suggest that up to half of Manaus was infected at the epidemic’s peak

The Sunshine Act shed some light on industry influence over medicine. Paul D Thacker helped pass the bill but says one small step for medicine is no giant leap for science

Today, the U.S. Food and Drug Administration issued an emergency use authorization (EUA) for the first serology (antibody) point-of-care (POC) test for COVID-19. The Assure COVID-19 IgG/IgM Rapid Test Device was first authorized for emergency use by certain labs in July 2020 to help identify individuals with antibodies to SARS-CoV-2, indicating recent or prior COVID-19 infection. Today, that EUA is being reissued to authorize the test for POC use using fingerstick blood samples. This authorization means that fingerstick blood samples can now be tested in POC settings like doctor’s offices, hospitals, urgent care centers and emergency rooms rather than having to be sent to a central lab for testing.

The celebrated economist has built a data tool with a God’s-eye view of the pandemic’s damage—and soaring inequality.

The appeal of herd immunity is easy to understand: if it is reached, an epidemic ends. But the illness and death such an approach would require have prompted a strong backlash. The language of herd immunity is part of the problem. A herd usually describes domesticated animals, especially livestock. Herd animals like cows, goats, or sheep are sacrificed for human consumption. Few humans want to be part of that kind of herd.

Neil Ferguson is known to many as Professor Lockdown. The mathematical models he created to predict the spread of Covid-19 were influential but, he says, it took him quite a long time to be persuaded that full lockdown was a good idea. A physicist by training, Neil switched from studying string theory to the spread of disease and presented scientific advice to government during the BSE crisis, an outbreak of foot and mouth disease in livestock in 2001 and the swine flu pandemic of 2009. In January 2020, he issued his first report on Covid-19 estimating the extent of the outbreak in Wuhan City in China. In March, he predicted that 510,000 people in the UK could die if nothing was done to mitigate the spread of this pandemic. Does he stand by that prediction? And how worried is he now? Jim Al-Khalili talks to Neil Ferguson about his life and work, the tricky relationship between politics and science and asks if he has any regrets about lockdown.

Western democracies, most notably the United States, have recently experienced a wave of protests, some characterized by lethal violence. While police brutality served as a catalyst, the eruption of protests coincided with the COVID-19 pandemic---the most severe global crisis of the 21st century. The pandemic has caused, inter alia, social stress, marginalization, and loss of economic status, which constitute psychological elicitors of aggression. Given this, we examined whether the psychological burden of the COVID-19 pandemic promotes anti-systemic attitudes and behavior. Analyses of two-wave panel data collected in April--July 2020 in the US, Denmark, Italy, and Hungary (N = 10,699), indicated that COVID-19 burden increased sentiments to ``watch the world burn'' and intentions to engage in political violence but not in peaceful protests. In the US, COVID-19 burden furthermore predicted engagement in the most violent actions during the George Floyd protests and counter-protests, including physical confrontation with the police. These results suggest that a second wave of the COVID-19 pandemic during the fall of 2020 may increase the risk of political violence in Western democracies, especially in contexts of potential political instability, such as the US presidential election.

The K-shaped recovery applies to geography too. 

The COVID-19 pandemic is an unprecedented global crisis. Many countries have implemented restrictions on population movement to slow the spread of severe acute respiratory syndrome coronavirus 2 and prevent health systems from becoming overwhelmed; some have instituted full or partial lockdowns. However, lockdowns and other extreme restrictions cannot be sustained for the long term in the hope that there will be an effective vaccine or treatment for COVID-19. Governments worldwide now face the common challenge of easing lockdowns and restrictions while balancing various health, social, and economic concerns. To facilitate cross-country learning, this Health Policy paper uses an adapted framework to examine the approaches taken by nine high-income countries and regions that have started to ease COVID-19 restrictions: five in the Asia Pacific region (ie, Hong Kong [Special Administrative Region], Japan, New Zealand, Singapore, and South Korea) and four in Europe (ie, Germany, Norway, Spain, and the UK). This comparative analysis presents important lessons to be learnt from the experiences of these countries and regions. Although the future of the virus is unknown at present, countries should continue to share their experiences, shield populations who are at risk, and suppress transmission to save lives.

This study sought to assess the extent of SARS-CoV-2 transmission among asymptomatic persons on a long-haul flight from Milan, Italy to South Korea. It found an estimated attack rate of 0.3% from infections likely obtained during the flight, and an overall prevalence of 2.3% for the disease from people who boarded the flight without symptoms. While it is impossible for them to determine for sure whether these infections were from the flight or prior, it highlights that with high adherence to passengers using high-quality masks whenever possible and social distancing by at least 6 feet, transmission can be very low during air travel.

All European Union countries are undergoing severe output losses as a consequence of COVID-19, but some have been hurt more than others. Factors potentially influencing the degree of economic contraction include the severity of lockdown measures, the structure of national economies, public indebtedness, and the quality of governance in different countries. With the exception of public indebtedness, we find all these factors are significant to varying degrees.

In this paper we use a Bayesian latent variable model to identify the effect of sociopolitical covariates on the historical COVID-19 infection rate among the 50 states. The model is calibrated using serology surveys issued by the Center for Disease Control. We show that as of July 14th, there are approximately 10 million people who have been infected with COVID-19 in the United States, and these people are concentrated in states that voted against President Donald Trump in 2016, are less concerned about COVID-19, are relatively unlikely to wear masks, and have fewer economic resources. Second, we find that increased mobility measured by Google cell phones in grocery stores and retail establishments has the highest correlation with subsequent COVID-19 spread, and that mobility is an important mediator of covariates and the spread of the disease. However, although support for President Trump correlates strongly with reduced COVID-19 infections, we find that this result does not come about via reduced mobility. Instead, it would appear more likely that conservative states were spared early outbreaks due to random or exogenous factors, and instead people may be inferring that partisanship has a causal effect on the disease when in fact it is likely a confounded association.

With a corona study at Deutsche Bahn, the Charité Research Organization provides scientific findings on the infection process in trains. The result: "Train travel is safe," says DB Passenger Transport Board Member Berthold Huber.

The COVID-19 pandemic has exposed weaknesses in health and care systems and global public health responses, some of which can be addressed through data and digital science. The Riyadh Declaration on Digital Health was formulated during the Riyadh Global Digital Health Summit, Aug 11–12, 2020, a landmark forum that highlighted the importance of digital technology, data, and innovation for resilient global health and care systems.

As the coronavirus continues its assault on the United States, throwing all aspects of everyday life into upheaval, the courts offer a lens into how treacherous things have gotten in one of those arenas -- the American workplace.

When the scale of the coronavirus pandemic became clear in March, people with symptoms were advised to contact NHS 111. Olufemi did so. But his family fear he was given the wrong advice

Without a vaccine, building up population-level immunity to the Covid-19 coronavirus could take a year or more and catching the virus does not necessarily provide lasting protection, writes Professor Devi Sridhar.

“We know there is a tsunami outside. We know it’s going to hit the beach. We just don’t know when,” said Donald Wise, a commercial real estate expert and co-founder and senior managing director at Turnbull Capital Group.The steep decline in tourism and business travel has devastated the hotel industry.“We anticipate many hotels won’t survive,” said Heather Rozman, executive director of the Hotel Assn. of Los Angeles. “Industry data shows 1 in 4 properties already are struggling to pay mortgages, risking foreclosure.”

Two years ago, Reddit had the internet’s biggest QAnon problem. Today, that problem is gone—but the company can’t really explain why.

the Knesset House Committee approved a special session of the Knesset plenum later Thursday, that will be the final step in authorizing the lockdown.

Sceptics who talk up 'false positives' are engaging in wishful thinking

COVID may have provided an opening, if we're willing to take it.

As much as people might be fearful today, and as much as important aspects of the COVID-19 pandemic remain to be understood, medical science has illuminated a path not only to understanding the biology of this coronavirus but also to defining ways to control its spread. The international research community has provided the foundation for protecting and strengthening our societies. Despite political disagreements and policy uncertainties, there is one proposition we can perhaps agree on—scientists, from molecular biologists to mathematical modellers, epidemiologists to pathologists, have mobilised their skills as never before. Their contributions must be remembered.

COVID-19: open, reasoned, detailed, discussion of the options is overdue and welcome At last, differing perspectives are being aired. This is healthy. People are mostly well educated and understand the situation, and are stoical. They and their elected representatives in Parliament must no longer be sidelined. We must hear their voice. However, there is no reason to divide into camps, and I do not see myself as being in one. As one of those calling for public debate and involvement including on the issue of population immunity, a phrase which should replace herd immunity for human populations, I welcome this exchange of knowledge and opinion.

Be creative and keep methodology in mind: two key takeaways from the APS webinar Online Research: Tools and Techniques.

As public health officials raise alarms about surging coronavirus cases among young people, new research suggests that Americans under 25 are most likely to believe virus-related misinformation about the severity of the disease and how it originated.

"Fake news" are widely acknowledged as an important challenge for Western democracies. Yet, surprisingly little effort has been devoted to measuring the effects of various counter-strategies. We address this void by running a pre-registered field experiment analyzing the causal effects of popular fact-checking videos on both believing and sharing fake news among Twitter users (N = 1,600). We find that the videos improve truth discernment ability as measured by performance in a fake news quiz immediately after exposure. However, the videos have not reduced sharing links from verified "fake news" websites on Twitter in the weeks following the exposure. Indeed, we find no relationship between truth discernment ability and fake news sharing. These results imply that the development of effective interventions should be based on a nuanced view of the distinct psychological motivations of sharing and believing "fake news".

Political social media use has become the topic of a growing amount of scholarship. In this regard, the role of user behavior in the formation of politically homogeneous environments (so-called echo chambers) is not fully understood. Building on the concept of selective exposure, we introduce the notion of selective political friending, i.e., the preference for political like-mindedness in social affiliations on social networking sites. In a pre-registered laboratory experiment (N = 199), we find that users preferably build connections to those who share opinions toward controversial political issues. Political like-mindedness outperforms advantages based on the popularity of another user or the career-related fit with another user. Political friending is particularly pronounced when individuals’ pre-existing opinions are strong, while tendencies toward cognitive closure and the desire for shared reality do not impact like-minded friending. The present study unravels psychological patterns in the process of tie-building on SNS and points to the necessity to take motivational complexity into account when studying phenomena linked to political homogeneity. Being the first study to systematically address politically motivated contact choices on social networking sites jointly with their psychological antecedents, this study sheds new light on the debate about like-mindedness in online communication. See less

Psychology journals rarely publish nonsignificant results. At the same time, it is often very unlikely (or "too good to be true") that a set of studies yields exclusively significant results. Here, we use likelihood ratios to explain when sets of studies that contain a mix of significant and nonsignificant results are likely to be true or "too true to be bad." As we show, mixed results are not only likely to be observed in lines of research but also, when observed, often provide evidence for the alternative hypothesis, given reasonable levels of statistical power and an adequately controlled low Type 1 error rate. Researchers should feel comfortable submitting such lines of research with an internal meta-analysis for publication. A better understanding of probabilities, accompanied by more realistic expectations of what real sets of studies look like, might be an important step in mitigating publication bias in the scientific literature.

Diagnosing previous infection with respiratory viruses is challenging. Our understanding of individual and population-level immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains incomplete and developing reliable serological assays to detect previous infection has been an intense focus of the global scientific effort. For public health planning we need scalable assays validated against large banks of samples from individuals who had proven seasonal (non-severe acute respiratory syndrome) coronaviruses and those who had well characterised symptomatic and asymptomatic confirmed SARS-CoV-2 infection. False-positive results, due to cross-reactivity with seasonal coronaviruses, are important to avoid, particularly if seropositive-individuals consider themselves immune

As rates of new coronavirus disease 2019 (COVID-19) cases decline across Europe owing to nonpharmaceutical interventions such as social distancing policies and lockdown measures, countries require guidance on how to ease restrictions while minimizing the risk of resurgent outbreaks. We use mobility and case data to quantify how coordinated exit strategies could delay continental resurgence and limit community transmission of COVID-19. We find that a resurgent continental epidemic could occur as many as 5 weeks earlier when well-connected countries with stringent existing interventions end their interventions prematurely. Further, we find that appropriate coordination can greatly improve the likelihood of eliminating community transmission throughout Europe. In particular, synchronizing intermittent lockdowns across Europe means that half as many lockdown periods would be required to end continent-wide community transmission.

BackgroundTo date, research on the indirect impact of the COVID-19 pandemic on the health of the population and the health-care system is scarce. We aimed to investigate the indirect effect of the COVID-19 pandemic on general practice health-care usage, and the subsequent diagnoses of common physical and mental health conditions in a deprived UK population.MethodsWe did a retrospective cohort study using routinely collected primary care data that was recorded in the Salford Integrated Record between Jan 1, 2010, and May 31, 2020. We extracted the weekly number of clinical codes entered into patient records overall, and for six high-level categories: symptoms and observations, diagnoses, prescriptions, operations and procedures, laboratory tests, and other diagnostic procedures. Negative binomial regression models were applied to monthly counts of first diagnoses of common conditions (common mental health problems, cardiovascular and cerebrovascular disease, type 2 diabetes, and cancer), and corresponding first prescriptions of medications indicative of these conditions. We used these models to predict the expected numbers of first diagnoses and first prescriptions between March 1 and May 31, 2020, which were then compared with the observed numbers for the same time period.FindingsBetween March 1 and May 31, 2020, 1073 first diagnoses of common mental health problems were reported compared with 2147 expected cases (95% CI 1821 to 2489) based on preceding years, representing a 50·0% reduction (95% CI 41·1 to 56·9). Compared with expected numbers, 456 fewer diagnoses of circulatory system diseases (43·3% reduction, 95% CI 29·6 to 53·5), and 135 fewer type 2 diabetes diagnoses (49·0% reduction, 23·8 to 63·1) were observed. The number of first prescriptions of associated medications was also lower than expected for the same time period. However, the gap between observed and expected cancer diagnoses (31 fewer; 16·0% reduction, −18·1 to 36·6) during this time period was not statistically significant.InterpretationIn this deprived urban population, diagnoses of common conditions decreased substantially between March and May 2020, suggesting a large number of patients have undiagnosed conditions. A rebound in future workload could be imminent as COVID-19 restrictions ease and patients with undiagnosed conditions or delayed diagnosis present to primary and secondary health-care services. Such services should prioritise the diagnosis and treatment of these patients to mitigate potential indirect harms to protect public health.

The future trajectory of the Covid-19 pandemic hinges on the dynamics of adaptive immunity against SARS-CoV2; however, salient features of the immune response elicited by natural infection or vaccination are still uncertain. We use simple epidemiological models to explore estimates for the magnitude and timing of future Covid-19 cases given different protective efficacy and duration of the adaptive immune response to SARS-CoV-2, as well as its interaction with vaccines and nonpharmaceutical interventions. We find that variations in the immune response to primary SARS-CoV-2 infections and a potential vaccine can lead to dramatically different immune landscapes and burdens of critically severe cases, ranging from sustained epidemics to near elimination. Our findings illustrate likely complexities in future Covid-19 dynamics, and highlight the importance of immunological characterization beyond the measurement of active infections for adequately projecting the immune landscape generated by SARS-CoV-2 infections.

For many U.S. colleges and universities that opted for in-person instruction this fall, the return to campus during the COVID-19 pandemic has proven disastrous, and prompted the question: who’s to blame for these new outbreaks? Although administrators are quick to blame student behavior, in this post, I will argue that the administrations are ultimately at fault – their negligence has put students’ health at risk and exacerbated the public health catastrophe.

The foundation of the scientific method rests on access to data, and yet such access is often restricted or costly. We investigate how improved data access shifts the quantity, quality, and diversity of scientific research. We examine the impact of reductions in cost and sharing restrictions for satellite imagery data from NASA’s Landsat program (the longest record of remote-sensing observations of the Earth) on academic science using a sample of about 24,000 Landsat publications by over 34,000 authors matched to almost 3,000 unique study locations. Analyses show that improved access had a substantial and positive effect on the quantity and quality of Landsat-enabled science. Improved data access also democratizes science by disproportionately helping scientists from the developing world and lower-ranked institutions to publish using Landsat data. This democratization in turn increases the geographic and topical diversity of Landsat-enabled research. Scientists who start using Landsat data after access is improved tend to focus on previously understudied regions close to their home location and introduce novel research topics. These findings suggest that policies that improve access to valuable scientific data may promote scientific progress, reduce inequality among scientists, and increase the diversity of scientific research.

Synthesis of evidence from the totality of relevant research is essential to inform and improve prevention efforts and policy. Given the large and usually heterogeneous evidence available, reaching a thorough understanding of what works, for whom, and in what contexts, can only be achieved through a systematic and comprehensive synthesis of evidence. Many barriers impede comprehensive evidence synthesis, which leads to uncertainty about the generalizability of intervention effectiveness, including: inaccurate terminology titles/abstracts/keywords (hampering literature search efforts); ambiguous reporting of study methods (resulting in inaccurate assessments of study rigor); and poorly reported participant characteristics, outcomes, and key variables (obstructing the calculation of an overall effect or the examination of effect modifiers). To address these issues and improve the reach of primary studies through their inclusion in evidence syntheses, we provide a set of practical guidelines to help prevention scientists prepare synthesis-ready research. We use a recent mindfulness trial as an empirical example to ground the discussion and demonstrate ways to ensure: (1) primary studies are discoverable; (2) the types of data needed for synthesis are present; and (3) these data are readily synthesizable. We highlight several tools and practices that can aid authors in these efforts, such as creating a repository for each project to host all study-related data files. We also provide step-by-step guidance and software suggestions for standardizing data design and public archiving to facilitate synthesis-ready research.

The 2019 novel coronavirus disease (COVID-19) is a global threat that has negative impacts on individuals’ physical and mental health. Here, we explore if disordered social media use promotes fear of COVID-19, which in turn increases stress and depression in users. The study also explores several risk and protective factors that may affect the relationship between fear of COVID-19 and stress and depression. There were 174 participants that completed an online survey that measured disordered social media use, fear of COVID-19, perceived stress, and depression symptomatology. We found that disordered social media use predicts perceived stress indirectly through fear of COVID-19. Disordered social media use had a direct relationship with depression scores and this relationship is mediated by fear of COVID-19. We also found that the positive relationship between fear of COVID-19 and perceived stress is stronger for older people than younger people. The psychological impact of COVID-19 may be exacerbated by content promoting the fear of COVID-19 that users will be exposed to on social media.

Released in September 2020, the website aims to provide:   entry-level information for those interested in prognosis research methods and good practice ​​ links to frameworks and resources to help you plan, carry out and evaluate prognosis research in healthcare ​ updates on emerging topics, methods and findings in prognosis research ​ links to training courses, summer schools and conferences in prognosis and prediction research  ​ publications, presentations and videos that disseminate prognosis research methods

A comprehensive mapping of paranoia, propaganda, moral panics, misinformation, and extremism

Culture has played a pivotal role in human evolution. Yet, the ability of social scientists to study culture is limited by currently available measurement instruments. Scholars of culture must regularly choose between scalable but sparse survey-based methods or restricted but rich ethnographic methods. Here, we demonstrate that massive online social networks can advance the study of human culture by providing quantitative, scalable, and high-resolution measurement of behaviorally revealed cultural values and preferences. We employ publicly available data across nearly 60,000 topic dimensions drawn from two billion Facebook users across 225 countries and territories. The data capture preferences inferred by Facebook from online behaviours on the platform, behaviors on external websites and apps, and offline behaviours captured by smartphones and other devices. We first validate that cultural distances calculated from this measurement instrument correspond to survey-based and objective measures of cultural differences. We then demonstrate that this measure enables insight into the cultural landscape globally at previously impossible resolution. We analyze the importance of national borders in shaping culture and explore unique cultural markers that identify subnational population groups. The global collection of massive data on human behavior provides a high-dimensional complement to traditional cultural metrics, potentially enabling novel insight into fundamental questions in the social sciences. The measure enables detailed investigation into the countries’ geopolitical stability, social cleavages within both small and large-scale human groups, the integration of migrant populations, and the disaffection of certain population groups from the political process, among myriad other potential future applications.

intuitive EPIDEMIOLOGY: with the goal of developing your intuitions ('gut feelings'), this channel discusses epidemiology and related content (e.g., scientific papers and media articles) in a non-technical and accessible manner.

Doctors, nurses and others are carrying a heavy burden for a nation that isn't doing enough to help them.

The constant mantra is the virus must be suppressed and contained. But how do you do this when people can be infectious without knowing they have it? Where it can be passed on silently because people do not develop symptoms?

The ability to share social network data at the level of individual connections is beneficial to science: not only for reproducing results, but also for researchers who may wish to use it for purposes not foreseen by the data releaser. Sharing such data, however, can lead to serious privacy issues, because individuals could be re-identified, not only based on possible nodes' attributes, but also from the structure of the network around them. The risk associated with re-identification can be measured and it is more serious in some networks than in others. Various optimization algorithms have been proposed to anonymize the network while keeping the number of changes minimal. However, existing algorithms do not provide guarantees on where the changes will be made, making it difficult to quantify their effect on various measures. Using network models and real data, we show that the average degree of networks is a crucial parameter for the severity of re-identification risk from nodes' neighborhoods. Dense networks are more at risk, and, apart from a small band of average degree values, either almost all nodes are re-identifiable or they are all safe. Our results allow researchers to assess the privacy risk based on a small number of network statistics which are available even before the data is collected. As a rule-of-thumb, the privacy risks are high if the average degree is above 10. Guided by these results we propose a simple method based on edge sampling to mitigate the re-identification risk of nodes. Our method can be implemented already at the data collection phase. Its effect on various network measures can be estimated and corrected using sampling theory. These properties are in contrast with previous methods arbitrarily biasing the data. In this sense, our work could help in sharing network data in a statistically tractable way.

We investigate how the initial number of infected individuals affects the behavior of the critical susceptible-infected-recovered process. We analyze the outbreak size distribution, duration of the outbreaks, and the role of fluctuations

Immunization has always been a proxy for wider fears about social control, a history reminds us

Compartmental epidemic models have been used extensively to study the historical spread of infectious diseases and to inform strategies for future control. A critical parameter of any such model is the transmission rate. Temporal variation in the transmission rate has a profound influence on disease spread. For this reason, estimation of time-varying transmission rates is an important step in identifying mechanisms that underlie patterns in observed disease incidence and mortality. Here, we present and test fast methods for reconstructing transmission rates from time series of reported incidence. Using simulated data, we quantify the sensitivity of these methods to parameters of the data-generating process and to mis-specification of input parameters by the user. We show that sensitivity to the user’s estimate of the initial number of susceptible individuals—considered to be a major limitation of similar methods—can be eliminated by an efficient, “peak-to-peak” iterative technique, which we propose. The method of transmission rate estimation that we advocate is extremely fast, for even the longest infectious disease time series that exist. It can be used independently or as a fast way to obtain better starting conditions for computationally expensive methods, such as iterated filtering and generalized profiling.

Local news outlets have struggled to stay open in the more competitive market of digital media. Some have noted that this decline may be due to the ways in which digital platforms direct attention to some news outlets and not others. To test this theory, we collected 12.29 million responses to Google News searches within all US counties for a set of keywords. We compared the number of local outlets reported in the results against the number of national outlets. We find that, unless consumers are searching specifically for topics of local interest, national outlets dominate search results. Features correlated with local supply and demand, such as the number of local outlets and demographics associated with local news consumption, are not related to the likelihood of finding a local news outlet. Our findings imply that platforms may be diverting web traffic and desperately needed advertising dollars away from local news.

Poll shows 74% favour publicly-run system as concerns over contractors like Serco grow.

The recognition and management of the socio-emotional pain facing the COVID-19 pandemic refer to different, but interdependent, clues regarding cognitive and emotional aspects of the pandemic threat, considering the need of social distancing as a prophylactic procedure to avoid spreading the pathogen. The socio-emotional condition at the time of outbreak subsidizes the (re)modulation of interactive neural circuits underlying the risk assessment behaviour at physical, emotional, and social levels. Experiences of social isolation, exclusion or affective loss are generally considered to be some of the most “painful” things that people face. The threats of social disconnection are processed by some of the same neural structures that process basic threats to survival. The lack of social connection can be "painful" due to an overlap in the neural circuitry responsible for both physical and emotional pain related to feelings of social rejection. Indeed, many of us go to great lengths to avoid situations that may engender these experiences. Because of this, this work focusses on times of pandemic, the somatization mentioned above seeks the interconnection and/or interdependence between neural systems related to emotional and cognitive processes, so that the person involved in that aversive social environment becomes aware of himself, the others, and the threatening situation experienced to avoid daily psychological and neuropsychiatric effects. Social distancing during the isolation evokes the formation of social distress, raising the intensity of learned fear that people acquire, consequently enhancing the emotional and social pain.

The COVID-19 pandemic poses many real-world moral dilemmas, which can pit the needs and rights of the many against the needs and rights of the few. We investigated the influence of this contemporary global crisis on moral judgments in older adults, who are at greatest personal risk from the pandemic. We hypothesized that during this pandemic, individuals would give fewer utilitarian responses to hypothetical dilemmas, accompanied by higher levels of confidence and emotion elicitation. Our pre-registered analysis (https://osf.io/g2wtp) involved two waves of data collection, before (2014) and during (2020) the COVID-19 pandemic, regarding three categories of moral dilemmas (personal rights, agent-centered permissions, and special obligations). While utilitarian responses considered across all categories of dilemma did not differ, participants during the 2020 wave gave fewer utilitarian responses to dilemmas involving personal rights; that is, they were less willing to violate the personal rights of others to produce the best overall outcomes.

In the present research, we approached utopian thinking from an individual differences perspective and developed the utopian impulse as a psychological construct, defined as the propensity to have thoughts and engage in actions whose purpose is to transform the current society into a better one in the future by addressing existing global issues.

Choices under conditions of risk often have consequences not just for ourselves, but for others. Yet, it is unclear how the other’s identity (stranger, close friend, etc.) influences risky choices made on their behalf. Here, two groups of undergraduates made a series of risky economic decisions for themselves, for another person, or for both themselves and another person (i.e., shared outcomes); one group of participants made choices involving a same-sex stranger (n = 29), the other made choices involving a same-sex close friend (n = 28). Hierarchical Bayesian Estimation of computations underlying risky decision-making revealed that relative to choosing for themselves, people were more risk averse, more loss averse, and more consistent when choices involved another person. Interestingly, partner identity differentially modulated decision computations. People became risk neutral and more consistent when choosing for friends relative to strangers. In sum, these findings suggest that the complexity of the social world is mirrored in its nuanced consequences for our choices.

Amidst the global COVID-19 pandemic, there is an urgent need for establishing knowledge about risk factors for adverse health outcomes associated with loneliness and social isolation. In this study, we show that self-perceived loneliness coincides with objective measures of social isolation as well as the personality trait neuroticism, and that these comorbidities contribute to differential associations with risk factors including depression, social deprivation, unhealthy lifestyle behaviors, cardiovascular risk, and aging of the brain. The findings contribute to identifying groups of individuals who may be vulnerable to loneliness and associated health problems, and emphasize the need for public-health initiatives addressing socioeconomic conditions as well as social, mental, and physical health to reduce the risk of loneliness and adverse health outcomes in the population.

Confirmed coronavirus disease 2019 (COVID-19) cases have increased in the United States following the relaxation of strong lockdown measures.1 Contact tracing, which entails identifying and monitoring people who have been in close contact with individuals with confirmed diagnoses and encouraging them to self-isolate and quarantine, is recommended as a key component of COVID-19 control strategies.2-4 We used a mathematical model to examine the potential for contact tracing to reduce the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the context of relaxed physical distancing, under different assumptions for case detection, tracing, and quarantine efficacy.

Human behavioral risk-seeking tendencies differ across content domains. How can such behavioral differences be reliably produced by the cognitive system? This article presents an explorative analysis of the reasons for and cognitive mechanisms underlying different risk propensities across 10 evolutionary domains. We investigate three cognitive process models: Tally, Take The First, and Most Relevant. Tally assumes decision-makers use majority rules. Take The First assumes decision makers rely on the first piece of information that comes to mind. Most Relevant assumes decision makers rely on information that is important in their environment. A survey with a total of N = 120 individuals in the United States gathered 1,598 self-reported memory-based attributes of risky situations in 10 evolutionary content domains. The explorative analysis of the cognitive processes underlying the domain differences suggest that the Most Relevant strategy is most closely related to the shifts in risk seeking across content domains, and that Take The First is also related, but the Tally process is not related to domain differences in risk propensities. This means that a cognitive process that relies on the first or frequent pieces of information from the environment may be underlying domain differences in risk taking. (APA PsycInfo Database Record (c) 2018 APA, all rights reserved)

In this spirit, Keystone Symposia has reimagined the scientific conference, leveraging emerging digital media technologies to connect scientists in new ways with our eSymposia series. Through this innovative platform, we will continue to catalyze discovery and accelerate breakthroughs. Despite inherent limitations to virtual interfaces, valuable benefits have also emerged.

The ever-increasing competitiveness in the academic publishing market incentivizes journal editors to pursue higher impact factors. This translates into journals becoming more selective, and, ultimately, into higher publication standards. However, the fixation on higher impact factors leads some journals to artificially boost impact factors through the coordinated effort of a "citation cartel" of journals in addition to self-citations. "Citation cartel" behavior has become increasingly common in recent years, with several instances of cartels being reported. Here, we propose an algorithm---named CIDRE---to detect anomalous groups of journals that exchange citations at excessively high rates when compared against a null model that accounts for scientific communities and journal size. CIDRE detects more than half of the journals suspended by Thomson Reuters due to cartel-like behavior in the year of suspension or in advance. Furthermore, CIDRE detects a large number of additional anomalous groups, which reveal a variety of mechanisms that may help to detect citation cartels at their onset. We describe a number of such examples in detail and discuss the implications of our findings with regard to the current academic climate.

Once the virus escapes into the air inside a building, you have two options: bring in fresh air from outside or remove the virus from the air inside the building.

Although confidence is commonly believed to be an essential element in decision making, it remains unclear what gives rise to one’s sense of confidence. Recent Bayesian theories propose that confidence is computed, in part, from the degree of uncertainty in sensory evidence. Alternatively, observers can use physical properties of the stimulus as a heuristic to confidence. In the current study, we developed ideal observer models for either hypothesis and compared their predictions against human data obtained from psychophysical experiments. Participants reported the orientation of a stimulus, and their confidence in this estimate, under varying levels of internal and external noise. As predicted by the Bayesian model, we found a consistent link between confidence and behavioral variability for a given stimulus orientation. Confidence was higher when orientation estimates were more precise, for both internal and external sources of noise. However, we observed the inverse pattern when comparing between stimulus orientations: although observers gave more precise orientation estimates for cardinal orientations (a phenomenon known as the oblique effect), they were more confident about oblique orientations. We show that these results are well explained by a strategy to confidence that is based on the perceived amount of noise in the stimulus. Altogether, our results suggest that confidence is not always computed from the degree of uncertainty in one’s perceptual evidence, but can instead be based on visual cues that function as simple heuristics to confidence.

Background Recent reports based on conventional SEIR models suggest that the next wave of the COVID-19 pandemic in the UK could overwhelm health services, with fatalities that far exceed the first wave. These models suggest non-pharmaceutical interventions would have limited impact without intermittent national lockdowns and consequent economic and health impacts. We used Bayesian model comparison to revisit these conclusions, when allowing for heterogeneity of exposure, susceptibility, and viral transmission. Methods We used dynamic causal modelling to estimate the parameters of epidemiological models and, crucially, the evidence for alternative models of the same data. We compared SEIR models of immune status that were equipped with latent factors generating data; namely, location, symptom, and testing status. We analysed daily cases and deaths from the US, UK, Brazil, Italy, France, Spain, Mexico, Belgium, Germany, and Canada over the period 25-Jan-20 to 15-Jun-20. These data were used to estimate the composition of each country's population in terms of the proportions of people (i) not exposed to the virus, (ii) not susceptible to infection when exposed, and (iii) not infectious when susceptible to infection. Findings Bayesian model comparison found overwhelming evidence for heterogeneity of exposure, susceptibility, and transmission. Furthermore, both lockdown and the build-up of population immunity contributed to viral transmission in all but one country. Small variations in heterogeneity were sufficient to explain the large differences in mortality rates across countries. The best model of UK data predicts a second surge of fatalities will be much less than the first peak (31 vs. 998 deaths per day. 95% CI: 24-37)--substantially less than conventional model predictions. The size of the second wave depends sensitively upon the loss of immunity and the efficacy of find-test-trace-isolate-support (FTTIS) programmes. Interpretation A dynamic causal model that incorporates heterogeneity of exposure, susceptibility and transmission suggests that the next wave of the SARS-CoV-2 pandemic will be much smaller than conventional models predict, with less economic and health disruption. This heterogeneity means that seroprevalence underestimates effective herd immunity and, crucially, the potential of public health programmes.

As students start a term like no other, higher education is being reinvented for the post-pandemic world