Complex systems thinking is applied to a wide variety of domains, from neuroscience to computer science and economics. The wide variety of implementations has resulted in two key challenges: the progenation of many domain-specific strategies that are seldom revisited or questioned, and the siloing of ideas within a domain due to inconsistency of complex systems language. In this work we offer basic, domain-agnostic language in order to advance towards a more cohesive vocabulary. We use this language to evaluate each step of the complex systems analysis pipeline, beginning with the system and data collected, then moving through different mathematical formalisms for encoding the observed data (i.e. graphs, simplicial complexes, and hypergraphs), and relevant computational methods for each formalism. At each step we consider different types of \emph{dependencies}; these are properties of the system that describe how the existence of one relation among the parts of a system may influence the existence of another relation. We discuss how dependencies may arise and how they may alter interpretation of results or the entirety of the analysis pipeline. We close with two real-world examples using coauthorship data and email communications data that illustrate how the system under study, the dependencies therein, the research question, and choice of mathematical representation influence the results. We hope this work can serve as an opportunity of reflection for experienced complexity scientists, as well as an introductory resource for new researchers.

Predicting an infectious disease can help reduce its impact by advising public health interventions and personal preventive measures. Novel data streams, such as Internet and social media data, have recently been reported to benefit infectious disease prediction. As a case study of dengue in Brazil, we have combined multiple traditional and non-traditional, heterogeneous data streams (satellite imagery, Internet, weather, and clinical surveillance data) across its 27 states on a weekly basis over seven years. For each state, we nowcast dengue based on several time series models, which vary in complexity and inclusion of exogenous data. The top-performing model varies by state, motivating our consideration of ensemble approaches to automatically combine these models for better outcomes at the state level. Model comparisons suggest that predictions often improve with the addition of exogenous data, although similar performance can be attained by including only one exogenous data stream (either weather data or the novel satellite data) rather than combining all of them. Our results demonstrate that Brazil can be nowcasted at the state level with high accuracy and confidence, inform the utility of each individual data stream, and reveal potential geographic contributors to predictive performance. Our work can be extended to other spatial levels of Brazil, vector-borne diseases, and countries, so that the spread of infectious disease can be more effectively curbed.

A major goal of dynamical systems theory is the search for simplified descriptions of the dynamics of a large number of interacting states. For overwhelmingly complex dynamical systems, the derivation of a reduced description on the entire dynamics at once is computationally infeasible. Other complex systems are so expansive that despite the continual onslaught of new data only partial information is available. To address this challenge, we define and optimise for a local quality function severability for measuring the dynamical coherency of a set of states over time. The theoretical underpinnings of severability lie in our local adaptation of the Simon-Ando-Fisher time-scale separation theorem, which formalises the intuition of local wells in the Markov landscape of a dynamical process, or the separation between a microscopic and a macroscopic dynamics. Finally, we demonstrate the practical relevance of severability by applying it to examples drawn from power networks, image segmentation, social networks, metabolic networks, and word association.

The Centers for Disease Control is no longer recommending that employers incentivize their workers to commute by car alone as businesses reopen during the COVID-19 pandemic, but the agency’s revised guidelines still don’t do enough to protect workers from the novel coronavirus — or the myriad public health threats posed by our unsafe transportation network.

On its face, it was a major finding: Antimalarial drugs touted by the White House as possible COVID-19 treatments looked to be not just ineffective, but downright deadly. A study published on 22 May in The Lancet used hospital records procured by a little-known data analytics company called Surgisphere to conclude that coronavirus patients taking chloroquine or hydroxychloroquine were more likely to show an irregular heart rhythm—a known side effect thought to be rare—and were more likely to die in the hospital. Within days, some large randomized trials of the drugs—the type that might prove or disprove the retrospective study’s analysis—screeched to a halt. Solidarity, the World Health Organization’s (WHO’s) megatrial of potential COVID-19 treatments, paused recruitment into its hydroxychloroquine arm, for example.

Our multi-national observational registry study published in The Lancet Medical Journal has been met with both high praise and some skepticism from the scientific community and global institutions. It is essential that the scientific and lay community alike understand the value of this observational study, its place in the evolving research corpus, and the need for ongoing, randomly controlled trials (RCT). It is also vitally important that our scientific colleagues around the world understand the validity of our database, particularly regarding data acquisition, warehousing, analytics, and related reporting processes. We are committed to demonstrating the high standards we hold at Surgisphere®, and the robustness of the work that has been completed.

On its face, it was a major finding: Antimalarial drugs touted by the White House as possible COVID-19 treatments looked to be not just ineffective, but downright deadly. A study published on 22 May in The Lancet used hospital records procured by a little-known data analytics company called Surgisphere to conclude that COVID-19 patients taking chloroquine or hydroxychloroquine were more likely to show an irregular heart rhythm—a known side effect thought to be rare—and more likely to die. Within days, large randomized trials of the drugs screeched to a halt. Solidarity, the World Health Organization's (WHO's) megatrial of potential COVID-19 treatments, paused recruitment into its hydroxychloroquine arm.But just as quickly, the results have begun to unravel—and Surgisphere, which provided patient data for two other high-profile COVID-19 papers, has come under withering online scrutiny from researchers and amateur sleuths. They have pointed out many red flags in the Lancet paper, including the astonishing number of patients and details about patient demographics and dosing that seemed implausible. “It began to stretch and stretch and stretch credulity,” says Nicholas White, a malaria researcher at Mahidol University in Bangkok.

he Lancet, one of the world’s top medical journals, on Thursday retracted an influential study that raised alarms about the safety of the experimental Covid-19 treatments chloroquine and hydroxychloroquine amid scrutiny of the data underlying the paper. Just over an hour later, the New England Journal of Medicine retracted a separate study, focused on blood pressure medications in Covid-19, that relied on data from the same company. The retractions came at the request of the authors of the studies, published last month, who were not directly involved with the data collection and sources, the journals said.

As the use of robotics has ramped up over the last 50 years, the notion that robots are taking human jobs has persisted, whether accurate or not. Much of the public still envisions a future with very little human labor, while other industry experts are skeptical. A new study from the Massachusetts Institute of Technology tackled this topic head-on, seeking to find out how many jobs robots actually replace. The MIT study, covering 1990 to 2017, found that the U.S. added one additional robot for every 1,000 workers, lowering the country’s employment-to-population ratio by approximately 0.2%. The study noted that certain areas of the U.S. were affected far more than others. In simpler terms, the study found that each additional robot added in the manufacturing space replaced about 3.3 workers in the U.S. on average. Additionally, the study found that the rise of workplace robots lowered wages by about 0.4% during the timeframe.

The British Psychological Society has published guidance for psychologists on safely returning to their physical places of work, as lockdown restrictions begin to ease across the United Kingdom.

The Lancet paper that halted global trials of hydroxychloroquine for Covid-19 because of fears of increased deaths has been retracted after a Guardian investigation found inconsistencies in the data.

Public housing tenants were not allowed to modify bathroom pipes until August 2016 Reclassification may have allowed coronavirus to travel 10 storeys down in a Tsing Yi housing estate

About 1 in 10 residents in this study have developed antibodies and approximately 1 in 40 currently asymptomatic individuals are positive for COVID-19 and potentially infectious. 

Metaphors pervade discussions of social issues like climate change, the economy, and crime. We ask how natural language metaphors shape the way people reason about such social issues. In previous work, we showed that describing crime metaphorically as a beast or a virus, led people to generate different solutions to a city’s crime problem. In the current series of studies, instead of asking people to generate a solution on their own, we provided them with a selection of possible solutions and asked them to choose the best ones. We found that metaphors influenced people’s reasoning even when they had a set of options available to compare and select among. These findings suggest that metaphors can influence not just what solution comes to mind first, but also which solution people think is best, even when given the opportunity to explicitly compare alternatives. Further, we tested whether participants were aware of the metaphor. We found that very few participants thought the metaphor played an important part in their decision. Further, participants who had no explicit memory of the metaphor were just as much affected by the metaphor as participants who were able to remember the metaphorical frame. These findings suggest that metaphors can act covertly in reasoning. Finally, we examined the role of political affiliation on reasoning about crime. The results confirm our previous findings that Republicans are more likely to generate enforcement and punishment solutions for dealing with crime, and are less swayed by metaphor than are Democrats or Independents.

I’m inspired by a West Midlands charity, but our community’s most vulnerable members are being hit the hardest

Covid-19 has put the science of rapid review centre stage. It is only just over six months ago that the world began to grapple with an entirely new disease whose natural history, prevention, treatment and wider management were completely unknown. Since then, tens of thousands of scientific papers and preprints have been published, and a new industry has emerged to summarise and synthesise these primary studies as quickly as possible. 

Survey data from Facebook users on self-reported COVID-19-related symptoms, conducted in partnership with the University of Maryland and Carnegie Mellon University. Aggregated data are available publicly; non-aggregated data are available upon request to eligible academic and nonprofit institution researchers.

Surgisphere, whose employees appear to include a sci-fi writer and adult content model, provided database behind Lancet and New England Journal of Medicine hydroxychloroquine studies

Health ministry reports 3,000 new cases in 24 hours after lockdown was eased in April

Number of suspected victims of modern slavery falls for first time in four years

In the northern region of La Rioja, one medieval town has suffered a particularly deadly outbreak. And in such a tight-knit community, suspicion and recrimination can spread as fast as the virus.

Given the rapidly changing nature of the coronavirus disease 2019 (COVID-19) pandemic, real-time monitoring of COVID-19 cases and deaths has been widely embraced.1 The pandemic has also been accompanied by an “infodemic,” an overabundance of information and misinformation.2 Public response to the pandemic and infodemic is important, but undermeasured.3 Real-time analysis of public response could lead to earlier recognition of changing public priorities, fluctuations in wellness, and uptake of public health measures, all of which carry implications for individual- and population-level health.3 To test this hypothesis, we measured daily changes in the frequency of topics of discussion across 94,467 COVID-19-related comments on an online public forum in March, 2020.

The Lancet presents a Series of five papers about research. In the first report Iain Chalmers et al discuss how decisions about which research to fund should be based on issues relevant to users of research. Next, John Ioannidis et al consider improvements in the appropriateness of research design, methods, and analysis. Rustam Al-Shahi Salman et al then turn to issues of efficient research regulation and management. Next, An-Wen Chan et al examine the role of fully accessible research information. Finally, Paul Glasziou et al discuss the importance of unbiased and usable research reports. These papers set out some of the most pressing issues, recommend how to increase value and reduce waste in biomedical research, and propose metrics for stakeholders to monitor the implementation of these recommendations.

Watch Watch the full video of our June 1 Conference

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, poses a tremendous threat to human health.1Chan JFW Yuan S Kok KH et al.A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster.Lancet. 2020; 395: 514-523Summary Full Text Full Text PDF PubMed Scopus (663) Google Scholar,  2Huang C Wang Y Li X et al.Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China.Lancet. 2020; 395: 497-506Summary Full Text Full Text PDF PubMed Scopus (1926) Google Scholar,  3Wu JT Leung K Leung GM Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study.Lancet. 2020; 395: 689-697Summary Full Text Full Text PDF PubMed Scopus (254) Google Scholar There were more than 4 million confirmed cases and 300 000 reported deaths worldwide as of May 11, 2020. There have been warnings of a major coronavirus pandemic since the SARS outbreak in 2002–04, and in 2020 that threat has been realised. Now we need to consider the challenges ahead; the current prevalence, the incidence of asymptomatic infections, and the true mortality remains unclear. We also need to ascertain the probable effectiveness of current control measures so future strategies can be prioritised accordingly.At the end of 2019, the COVID-19 outbreak was first reported in the city of Wuhan, China, and has since spread worldwide.4Tian H Liu Y Li Y et al.An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China.Science. 2020; 368: 638-642Crossref PubMed Scopus (18) Google Scholar To contain the spread of the disease, a cordon sanitaire was imposed on Wuhan city on Jan 23, 2020, and travel restrictions were subsequently imposed on other cities across Hubei province the next day.5Kraemer MUG Yang C-H Gutierrez B et al.The effect of human mobility and control measures on the COVID-19 epidemic in China.Science. 2020; 368: 493-497Crossref PubMed Scopus (21) Google Scholar After 61 days of lockdown in Hubei province, the province reopened again on March 25, 2020, and after 76 days of lockdown in Wuhan, the city reopened again on April 8, 2020. The screening of individuals in these areas provides essential information on how immunity, and potentially herd immunity, is shaped in the community that has so far had the longest chain of community transmission but also some of the strongest physical distancing measures.During early-2020, many studies attempted to estimate the reporting rate (or ascertainment rate) of COVID-19 in Wuhan.6Li R Pei S Chen B et al.Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2).Science. 2020; 368: 489-493Crossref PubMed Scopus (107) Google Scholar,  7Pan A Liu L Wang C et al.Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China.JAMA. 2020; (published online April 10.)DOI: 10.1001/jama.2020.6130Crossref Scopus (18) Google Scholar,  8Wu JT Leung K Bushman M et al.Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China.Nat Med. 2020; 26: 506-510Crossref PubMed Scopus (38) Google Scholar Seroprevalence surveys of the general population are key to understanding reporting rates, the underlying number of infections, the build-up of immunity, and to reconstruct chains of transmission of SARS-CoV-2. In The Lancet Microbe, Kelvin To and colleagues9To KK-W Cheng VC-C Cai J-P et al.Seroprevalence of SARS-CoV-2 in Hong Kong and in residents evacuated from Hubei province, China: a multicohort study.Lancet Microbe. 2020; (published online June 3.)https://doi.org/10.1016/S2666-5247(20)30053-7Google Scholar present the results of a seroepidemiological survey of SARS-CoV-2 in the general population in Hong Kong and returnees evacuated from Hubei, China. The investigators enrolled 1938 individuals before and after the COVID-19 pandemic between 2018 and 2020, and 452 asymptomatic Hubei returnees in March, 2020. The study showed that as an emergent virus, SARS-CoV-2 had a seroprevalence of 3·8% among Hubei returnees (17 of 452 returnees); very far from any plausible level of herd immunity.Although the study assessed a small sample—452 of approximately 60 million people in Hubei province—it provides an essential baseline for public health authorities when evaluating the effect of current and future interventions. Judging by patterns of circulation of endemic coronaviruses (HCoV-NL63, HCoV-HKU1, HCoV-229E, and HCoV-OC4),10Killerby ME Biggs HM Haynes A et al.Human coronavirus circulation in the United States 2014–2017.J Clin Virol. 2018; 101: 52-56Crossref PubMed Scopus (0) Google Scholar it is probable that broader immunity might build up in the years ahead. However, To and colleagues' study shows that the current level of immunity is far below the herd immunity threshold and will not appreciably slow future spread, so testing, screening, and contact tracing from symptomatic and asymptomatic infections are still key to stopping further infections.We declare no competing interests.

JASP, Jeffrey’s Amazing Statistics Program (official webpage: https://jasp-stats.org/), version 0.12.2, is an open-source, free statistical software package available on Windows, MacOS, and Linux platforms. It was developed by a group of quantitative methodologists who are interested in improving statistical testing and analysis methods used in the fields of psychological sciences. It implements various analysis methods, particularly those in Bayesian analysis and data science. The implemented methods include t-tests, ANOVA, regression analysis, factor analysis, machine learning, meta-analysis, network analysis, and SEM. JASP also includes a data editor for visual inspection and pre-processing. It supports data importing and exporting from/to various data sources. The statistical analysis program R constitutes the basis of JASP, but for users who are not familiar with programming, JASP is featured with a graphical user interface (GUI) so that the users can select analysis modules and modify options conveniently. To support users in academia, especially those in psychology, JASP provides functionalities for visualizing analysis results in tables and figures according to the American Psychological Association (APA) convention and presents options for modification once they are created.

The Article by Nan Yu and colleagues1Yu N Li W Kang Q et al.Clinical features and obstetric and neonatal outcomes of pregnant patients with COVID-19 in Wuhan, China: a retrospective, single-centre, descriptive study.Lancet Infect Dis. 2020; 20: 559-564Summary Full Text Full Text PDF PubMed Scopus (20) Google Scholar provides timely information to inform policies concerning essential pregnant workers. In past epidemics (eg, H1N1 influenza, Middle East respiratory syndrome, and severe acute respiratory syndrome), pregnant women and their offspring have been at increased risk of morbidity and mortality.2Louie JK Acosta M Jamieson DJ Honein MA California Pandemic Working GroupSevere 2009 H1N1 influenza in pregnant and postpartum women in California.N Engl J Med. 2010; 362: 27-35Crossref PubMed Scopus (489) Google Scholar By contrast, the maternal, fetal, and neonatal outcomes reported by Yu and colleagues were surprisingly good. When considering this study, it is important to note that all of the women were full-term (≥37 weeks) and underwent caesarean section within 3 days of presentation. Prompt delivery allowed for experimental treatment in all women; all patients received antiviral treatment, including ganciclovir, oseltamivir, interferon, and Arbidol (ie, umifenovir) tablets. Traditional Chinese medicine, such as Jinye Baidu granules or Lianhuaqingwen capsules, or both, was also administered to four of seven women, and five of seven received the steroid methylprednisolone. Because the safety of these interventions during pregnancy is not established, infection in the first, second, or early third trimester, without immediate delivery, might not lead to similar favourable outcomes. For example, in a case series on nine pregnant women with a larger range of gestational ages and severe cardiopulmonary disease attributed to COVID-19, seven of nine died.3Hantoushzadeh S Shamshirsaz AA Aleyasin A et al.Maternal death due to COVID-19 disease.Am J Obstet Gynecol. 2020; (published online April 28.)DOI:10.1016/j.ajog.2020.04.030Summary Full Text Full Text PDF Google Scholar Although the worse outcomes of this non-systematic series do not establish risk, they do highlight that low risk should not be assumed in the absence of multicentre databases to study pregnancy-related outcomes.

The present study aimed to investigate the predictors of work stress in elementary and upper-secondary school teachers and school counsellors in the initial period of online education in schools during the COVID-19 pandemic. A total of 964 school professionals (90.7% teachers; 9.3% school counsellors) participated in the study. The results indicated that school professionals who reported higher ICT self-efficacy, had more positive attitudes toward distance education and perceived higher level of supervisor support experienced less stress. In addition, the participants that reported taking care of their own preschool or younger school children during the schools' closure reported higher levels of stress.

On March 16 2020, President Trump introduced strict social distancing guidelines for the United States, in an effort to stem the spread of the COVID-19 pandemic. This had an immediate major effect on the job market, with millions of Americans forced to find alternative ways to make a living from home. Here, we investigate the possibility that this policy also changed the pool of MTurk workers available to take part in academic studies – either by influencing which existing MTurk workers participate, or by causing an influx of new workers. Specifically, we look at 10,510 responses gathered in 16 studies run between February 25, 2020, and May 14, 2020, examining the distribution of gender, age, ethnicity, political preferences, and analytic cognitive style. We find important changes on all measures following the imposition of nationwide social distancing: participants are more non-white, more Republican, less experienced with MTurk, and less reflective (as measured by the Cognitive Reflection Test). Most of these differences are explained by an influx of new participants who are demographically different from previous participants

Question  If commercial insurers retrospectively deny coverage for emergency department (ED) visits based on diagnoses determined to be nonemergent, what visits will be denied coverage?Findings  This cross-sectional study found that 1 insurer’s list of nonemergent diagnoses would classify 15.7% of commercially insured adult ED visits for possible coverage denial. However, these visits shared the same presenting symptoms as 87.9% of ED visits, of which 65.1% received emergency-level services.Meaning  A retrospective diagnosis-based policy is not associated with accurate identification of unnecessary ED visits and could put many commercially insured patients at risk of coverage denial.

The coronavirus disease 2019 (Covid-19) outbreak has dramatically altered people’s social lives due to strict distancing policies. Increased loneliness has been publicly discussed as a harmful psychological side effect of these policies. However, empirical evidence was lacking. This large scale daily diary study assessed daily loneliness in N = 4,850 German adults from March 16, 2020 until April 12, 2020. Daily loneliness increased during the first two weeks of the Covid 19 lockdown and decreased thereafter. We identified subgroups that are at a higher risk for changes in daily loneliness during the Covid-19 pandemic (i.e., older adults, parents). It is important to evaluate if and how established knowledge about psychological functioning applies to extraordinary times and events such as the Covid-19 pandemic.

The pandemic shows how hard it will be to decarbonise—and creates an opportunity

You are invited to complete this form and contribute to a project led and funded by the National Institute for Health Research (NIHR) Mental Health Translational Research Collaboration (MH-TRC). The MH-TRC is a UK-wide network of 14 centres of excellence in mental health research that facilitates collaboration. It is led by chair Professor Matthew Hotopf and deputy chair Professor John Geddes. This website is hosting the survey on behalf of the MH-TRC. 

Global health scholars have criticised the Emergency Committee process as lacking transparency, using “irrelevant considerations, undue influence and political interference”,5Eccleston-Turner M Kamradt-Scott A Transparency in IHR emergency committee decision making: the case for reform.BMJ Glob Hlth. 2019; 4e001618PubMed Google Scholar and delaying declaration when International Health Regulations criteria have been met.

Bei der epidemiologischen Abklärung geht es darum, darzustellen, wie sich ein Krankheitsausbruch innerhalb der Bevölkerung verbreitet: Dafür versucht man, Quellen der Infektion bzw. Übertragungsketten der Fälle durch persönliche Befragungen von erkrankten bzw. positiv getesteten Personen (= Fällen) zu identifizieren. Wenn man weiß, wie sich die Krankheit in der Bevölkerung verbreitet, können Maßnahmen gesetzt werden, die am wahrscheinlichsten dazu beitragen, die Verbreitung einzudämmen oder zu verlangsamen

To argue that predictions are “overrated” is simply bizarre

When children are little, they like to pose dilemmas as “would you rather” questions that involve difficult trade-offs. Would you rather fight an elephant-sized duck or five human-sized rhinos? Would you rather have a runny nose for a month or dry eyes? The options are not only both undesirable, but also incomparable. These questions are how we might think of some of the dilemmas created by the COVID-19 pandemic, which presents us with difficult trade-offs in equity, economics, public health, and civil liberties.

The government’s disease-fighting agency is conflating viral and antibody tests, compromising a few crucial metrics that governors depend on to reopen their economies. Pennsylvania, Georgia, Texas, and other states are doing the same.

Researchers say that if the UK had imposed lockdown seven days earlier, its death toll would be on a par with 8,000 in Germany

As a call to action in the field, we highlight that the COVID-19 pandemic is an abrupt and chronic stressor to children and families of disadvantaged and marginalized backgrounds. Critically, this global crisis has a disproportionate impact on the physical, mental and behavioral health of these vulnerable members of society, due to pre-existing disadvantages such as economic hardship, educational inequities, risks of maltreatment and community violence. A lack of access to mental health support further deprives children and their caregivers of the resources needed to cope with magnified adversities during the pandemic. We believe these inequalities are not the mere result of the pandemic, but enduring issues in society that are detrimental to the wellbeing of the most vulnerable. We advocate for long-term strategies to change societal structures that oppress marginalized children and families, and believe the pandemic reflects a pressing need to invest in interventions.

In general, data visualization, especially advanced data visualization, has been viewed by practitioners from various disciplines as a powerful tool with the capacity to highlight the most important elements or key issues to improve understanding of the provided information. This chapter reviews the research on traditional static data visualization as well as advanced computer‐mediated interactive data visualization that has been conducted in the area of health and risk communication. Like traditional data visualizations, there are numerous types of interactive data visualization that have been adopted for presenting various types of health‐ and risk‐related information to the general public. The chapter discusses some of the theoretical frameworks that have been adopted in research on data visualization for health and risk communication. In health and risk communication research, a popular theoretical lens used to explain and predict the effectiveness and/or ineffectiveness of fear appeals is the extended parallel process model.

“Human challenge trials” may be able to speed up vaccine testing and rollout — if we clear some hurdles to make them work.

The UK is trialling its smartphone app to assist with contact tracing. The government is full of enthusiasm and the Isle of Wight is the guinea pig. But for all the fanfare and faith in its potential, is the right kind of tracing being done? And are we missing the human touch?

CMU Researchers Say Sophisticated, Orchestrated Bot Campaigns Aim To Sow Divide

The COVID-19 crisis has challenged all sectors of society, including science. The crisis demands an all-out scientific response if it is to be mastered with minimal damage. This means that we, as a community of scientists, need to think about how we can adapt to the moment in order to be maximally beneficial. How can we quickly and reliably deliver an evidence base for the many, diverse questions that behavioral science can inform?

A Swedish study found that just 7.3 percent of Stockholmers developed COVID-19 antibodies by late April, which could fuel concern that a decision not to lock down Sweden against the pandemic may bring little herd immunity in the near future.

Let’s again start with the WHY: we are developing a Corona-Warn-App to minimize time from a person being tested positive (index case) to being informed about a positive test result. That way, the person tested positive is hopefully not infecting anyone anymore. We do that by digitalizing the process from doctor to lab and back to the patient minimize time to inform citizens who were close to the index case for a certain amount time maximize the reach of the notification to citizens an index case was close to (e.g. on the train, in a restaurant, or similar) but does not personally know

What I do want to do: cast this episode as just one entry in an increasingly long ledger of high-profile and dramatically undercooked scientific papers (and it really is dealer’s choice for which one you pick at this point) that have made a good few people question the role of preprints in scientific life — a position which, as someone who wrote an article literally called Why I Love Preprints, I find totally understandable and justified.Because this is now a serious problem.

News of the city’s death has been greatly exaggerated. Instead, it might be about to get a lot younger.

More than 1,200 California pastors say they will resume in-person services this month in defiance of Governor Gavin Newsom’s stay-at-home order to slow the spread of coronavirus.

To make clinical and biological observations within a timeframe that is likely to benefit patients during disease outbreaks, coordination of global research must match the speed of spread of novel pathogens. Time is short. Circumstances call for international collaboration to understand, treat, and prevent coronavirus disease 2019 (COVID-19).

Houston is one of several cities in the South that could see spikes in COVID-19 cases over the next four weeks as restrictions are eased, according to new research that uses cellphone data to track how well people are social distancing. The updated projection, from PolicyLab at Children’s Hospital of Philadelphia, found that traffic to non-essential businesses has jumped especially in Texas and Florida, which have moved aggressively to reopen. In Harris County, the model predicts the outbreak will grow from about 200 new cases per day to more than 2,000 over the next month.

I find a gulf in perceptions between experts and nonexperts. Many Americans believe that we are now emerging from the pandemic and that, as President Trump says, we can see light at the end of the tunnel. Yet many epidemiologists, while acknowledging how little they know, are deeply apprehensive about a big second wave this fall, more brutal than anything we’ve endured so far.That mix of humility and apprehensiveness seems the best guide as we devise policy to survive a plague. Hope for the best while preparing for the worst.

The COVID-19 pandemic has vividly highlighted how much society depends upon essential workers. Praise for the heroic work being done by health-care workers to save lives worldwide in dangerous, exhausting conditions is everywhere. But those same workers are often left unprotected by governments and systems that have failed to supply them with enough personal protective equipment (PPE), supplies, and resources to do their jobs. In April alone, there were an estimated 27 COVID-19-related health worker deaths in the USA, 106 in the UK, and 180 in Russia, with tens of thousands of infections. The actual numbers are probably much higher.But essential work extends beyond health care. Although some people have been able to shift their jobs to their homes, millions of workers have jobs that cannot be done at home—not only custodial staff and orderlies in hospitals, but also teachers and child-care workers, grocery clerks and supermarket workers, delivery people, factory and farm workers, and restaurant staff, often without adequate PPE. These people leave their homes to help maintain a semblance of normality for others, at great risk to themselves and their families.

Aggression in online social networks has been studied up to now, mostly with several machine learning methods which detect such behavior in a static context. However, the way aggression diffuses in the network has received little attention as it embeds modeling challenges. In fact, modeling how aggression propagates from one user to another, is an important research topic since it can enable effective aggression monitoring, especially in media platforms which up to now apply simplistic user blocking techniques. In this paper, we focus on how to model aggression propagation on Twitter, since it is a popular microblogging platform at which aggression had several onsets. We propose various methods building on two well-known diffusion models, Independent Cascade (IC) and Linear Threshold (LT), to study the aggression evolution in the social network. We experimentally investigate how well each method can model aggression propagation using real Twitter data, while varying parameters, such as users selection for model seeding, weigh users' edges, users' activation timing, etc. Based on the proposed approach, the best performing strategies are the ones to select seed users with a degree-based approach, weigh user edges based on overlaps of their social circles, and activate users while considering their aggression levels. We further employ the best performing models to predict which ordinary real users could become aggressive (and vice versa) in the future, and achieve up to AUC=0.89 in this prediction task. Finally, we investigate methods for minimizing aggression, by launching competitive cascades to "inform" and "heal" aggressors. We show that IC and LT models can be used in aggression minimization, thus providing less intrusive alternatives to the blocking techniques currently employed by popular online social network platforms.

The social environment, both in early life and adulthood, is one of the strongest predictors of morbidity and mortality risk in humans. Evidence from long-term studies of other social mammals indicates that this relationship is similar across many species. In addition, experimental studies show that social interactions can causally alter animal physiology, disease risk, and life span itself. These findings highlight the importance of the social environment to health and mortality as well as Darwinian fitness—outcomes of interest to social scientists and biologists alike. They thus emphasize the utility of cross-species analysis for understanding the predictors of, and mechanisms underlying, social gradients in health.

The coronavirus disease 2019 (COVID-19) pandemic has highlighted the need for different types of diagnostics, comparative validation of new tests, faster approval by federal agencies, and rapid production of test kits to meet global demands. In this Perspective, we discuss the utility and challenges of current diagnostics for COVID-19.

What the death of a small business means to the community around it.

Over the last couple of weeks, some major companies have signaled that remote work is here to stay. The heads of three of New York City's largest commercial tenants — JPMorgan Chase, Barclays and Morgan Stanley — have each said it's highly unlikely that all their employees will return to their Manhattan skyscrapers. It's not just banks. Google has axed deals to buy up 2 million square feet of urban office space. Jack Dorsey, Twitter's CEO, told his employees they will be allowed to work remotely forever. With so many high-paid jobs untethered from their urban offices, we've been wondering what this all means for the future of cities. So we called up Harvard University professor Ed Glaeser, the leading scholar of urban economics. In 2011, he published a great book with a title that pretty much sums up decades of research: Triumph of the City: How Our Greatest Invention Makes Us Richer, Smarter, Greener, Healthier, and Happier. Some of his subtitle's adjectives feel very untrue at the moment, and we wanted to know if he still thinks cities will triumph. Glaeser remains a champion of cities, but he says it's possible they're in for a long period of trouble. He remembers the New York City he grew up in during the 1970s. Back then, manufacturers left, poverty got worse, crime and drugs pushed families into the suburbs, property values plummeted and the city almost declared bankruptcy. That dark period for New York and other cities, he says, "should remind us that urban success is not foreordained."

In this article, we present a tool and a method for measuring the psychological and cultural distance between societies and creating a distance scale with any population as the point of comparison. Because psychological data are dominated by samples drawn from Western, educated, industrialized, rich, and democratic (WEIRD) nations, and overwhelmingly, the United States, we focused on distance from the United States. We also present distance from China, the country with the largest population and second largest economy, which is a common cultural comparison. We applied the fixation index (FST), a meaningful statistic in evolutionary theory, to the World Values Survey of cultural beliefs and behaviors. As the extreme WEIRDness of the literature begins to dissolve, our tool will become more useful for designing, planning, and justifying a wide range of comparative psychological projects. Our code and accompanying online application allow for comparisons between any two countries. Analyses of regional diversity reveal the relative homogeneity of the United States. Cultural distance predicts various psychological outcomes.

The state reported Monday that the death toll from the coronavirus outbreak in Massachusetts had risen by 189 and that the number of cases had climbed by 3,840. The large numbers came as state officials announced they had begun including probable as well as confirmed cases in their tallies, noting that not all of the newly-reported cases are recent. The probable cases came from a review of data dating back to March 1.The new inclusion of probable data pushed the state’s death tally past 7,000 and the total number of cases past 100,000.In terms of confirmed numbers, the state reported 48 new fatalities and 326 new cases.

As lockdown rules ease in the UK but distancing guidance remains in place, how can we use group norms to make distancing easier for people at mass gatherings? John Drury, Stephen Reicher and Nick Hopkins have some advice.

Network of U.S. Senators and words used in their official statements following the acquittal vote in the Senate impeachment trial.

Countries have sought to stop the spread of coronavirus disease 2019 (COVID-19) by severely restricting travel and in-person commercial activities. Here, we analyse the supply-chain effects of a set of idealized lockdown scenarios, using the latest global trade modelling framework. We find that supply-chain losses that are related to initial COVID-19 lockdowns are largely dependent on the number of countries imposing restrictions and that losses are more sensitive to the duration of a lockdown than its strictness. However, a longer containment that can eradicate the disease imposes a smaller loss than shorter ones. Earlier, stricter and shorter lockdowns can minimize overall losses. A ‘go-slow’ approach to lifting restrictions may reduce overall damages if it avoids the need for further lockdowns. Regardless of the strategy, the complexity of global supply chains will magnify losses beyond the direct effects of COVID-19. Thus, pandemic control is a public good that requires collective efforts and support to lower-capacity countries.

The devastating effects of the COVID-19 pandemic go far beyond public health; with many industries on hold and unemployment increasing worldwide, the global economy is approaching the deepest recession in living memory. In the USA, where health insurance is largely provided by employers and more than 30 million people have filed for unemployment in the past 2 months, such a recession could cause an unprecedented surge in uninsured or underinsured people. Indeed, an analysis published on May 4, 2020, has estimated that if unemployment in the USA reaches 20%, 25–43 million people could lose their health insurance. For patients with cancer, for whom care is already expensive and long lasting, this could be a fatal blow.

Purpose: This paper aims to 1) identify strategies used by successful teleworkers to create and maintain boundaries between work and home, and 2) determine how these strategies relate to employee preferences for segmentation or integration of work and home. Design/methodology/approach: Forty in-depth, face-to-face interviews were conducted with employees working from home either occasionally (occasional teleworkers), between 20-50% of the workweek (partial teleworkers), or the majority of the time (full teleworkers). Findings: Teleworkers use physical, temporal, behavioural and communicative strategies to recreate boundaries similar to those found in office environments. While teleworkers can generally develop strategies that align boundaries to their preferences for segmentation or integration, employees with greater job autonomy and control are better able to do so. Research limitations: A limitation of this research is its potential lack of generalizability to teleworkers in organizations with “always-on” cultures, who may experience greater pressure to allow work to permeate the home boundary. Practical implications: These findings can encourage organizations to proactively assess employee preferences for boundary permeability before entering a teleworking arrangement. The boundary management tactics identified can be used to provide teleworkers struggling to establish comfortable boundaries with tangible ideas to regulate interactions between home and work. Originality/value: This research makes a significant contribution to practitioner literature by applying a boundary management framework to the practice of teleworking, which is being adopted by organizations with increasing frequency.

The era of peak globalisation is over. For those of us not on the front line, clearing the mind and thinking how to live in an altered world is the task at hand.

As the NHS and HSE can’t deliver our Stress Control classes in the community just now, Dr Jim White will, instead, live-stream the classes, free-of-charge, starting on Monday 11th May. You can watch the sessions either in the afternoon or evening. Click here to get all the dates. To find out more about Stress Control click here All you need to successfully complete this class is to watch each of the six sessions, read the booklets and use the relaxation and mindfulness. You can find the sessions on YouTube. Click the button below or search for ‘Stress Control 2020’ to access our YouTube channel where the classes will be available to view only at the scheduled times. If you click the Subscribe button on our YouTube page, you should be kept up to date with the latest videos when logged in to YouTube

Google, Apple and researchers partner to build more secure and effective tools, but poor adoption could blunt efficacy.

Led by the Silicon Valley tech giants, more and more companies are extending their timelines for remote work — and some are weighing letting employees work from home forever.

Government policies during the COVID-19 pandemic have drastically altered patterns of energy demand around the world. Many international borders were closed and populations were confined to their homes, which reduced transport and changed consumption patterns. Here we compile government policies and activity data to estimate the decrease in CO2 emissions during forced confinements. Daily global CO2 emissions decreased by –17% (–11 to –25% for ±1σ) by early April 2020 compared with the mean 2019 levels, just under half from changes in surface transport. At their peak, emissions in individual countries decreased by –26% on average. The impact on 2020 annual emissions depends on the duration of the confinement, with a low estimate of –4% (–2 to –7%) if prepandemic conditions return by mid-June, and a high estimate of –7% (–3 to –13%) if some restrictions remain worldwide until the end of 2020. Government actions and economic incentives postcrisis will likely influence the global CO2 emissions path for decades.

Simulators that can rapidly test trillions of options would accelerate the slow and costly process of human clinical trials.

BackgroundIn biometric practice, researchers often apply a large number of different methods in a "trial-and-error" strategy to get as much as possible out of their data and, due to publication pressure or pressure from the consulting customer, present only the most favorable results. This strategy may induce a substantial optimistic bias in prediction error estimation, which is quantitatively assessed in the present manuscript. The focus of our work is on class prediction based on high-dimensional data (e.g. microarray data), since such analyses are particularly exposed to this kind of bias.MethodsIn our study we consider a total of 124 variants of classifiers (possibly including variable selection or tuning steps) within a cross-validation evaluation scheme. The classifiers are applied to original and modified real microarray data sets, some of which are obtained by randomly permuting the class labels to mimic non-informative predictors while preserving their correlation structure.ResultsWe assess the minimal misclassification rate over the different variants of classifiers in order to quantify the bias arising when the optimal classifier is selected a posteriori in a data-driven manner. The bias resulting from the parameter tuning (including gene selection parameters as a special case) and the bias resulting from the choice of the classification method are examined both separately and jointly.ConclusionsThe median minimal error rate over the investigated classifiers was as low as 31% and 41% based on permuted uninformative predictors from studies on colon cancer and prostate cancer, respectively. We conclude that the strategy to present only the optimal result is not acceptable because it yields a substantial bias in error rate estimation, and suggest alternative approaches for properly reporting classification accuracy.

Sweden’s highly contested response to Covid-19 left much of the economy open. Even so, the country is now headed for its worst recession since World War II.

Mehra MR, Desai SS, Ruschitzka F, Patel AN. Hydroxychloroquine or chloroquine with or without a macrolide for treatment of COVID-19: a multinational registry analysis. Lancet 2020; published online May 22. https://doi.org/10.1016/S0140-6736(20)31180-6—In this Article, in the first paragraph of the Results section, the numbers of participants from Asia and Australia should have been 8101 (8·4%) and 63 (0·1%), respectively. One hospital self-designated as belonging to the Australasia continental designation should have been assigned to the Asian continental designation. The appendix has also been corrected. An incorrect appendix table S3 was included, originally derived from a propensity score matched and weighted table developed during a preliminary analysis. The unadjusted raw summary data are now included. There have been no changes to the findings of the paper. These corrections have been made to the online version as of May 29, 2020, and will be made to the printed version.

“Is it safe to re-open schools?”  “How can it be safe to re-open car show rooms when it’s not safe for me to see my family?”  Questions like these have been on the front of several newspapers, asked repeatedly at the daily press briefings, and no doubt chewed over on many more Facebook pages and WhatsApp groups around the country. The answers from politicians and scientists alike always seem to be unsatisfactory and fail to bring people together. The reason for this lies not only with the answers given, but the fact that people are asking the wrong questions—for three fundamental reasons:

Scott Galloway predicts a handful of elite cyborg universities will soon monopolize higher education.

Erarbeitet im Rahmen eines Projektes der Digitalstadt Darmstadt GmbH, Digital für alle und dem Profilbereich Internet und Digitalisierung der TU Darmstadt.

Objective To examine longitudinal changes in the prevalence of mental health problems before and during the COVID-19 crisis in a large-scale population-based study and to identify population subgroups that are psychologically vulnerable during the pandemic. Design and setting Longitudinal observational population study using data from the 2017 – 2019 and April 2020 waves of Understanding Society, the United Kingdom Household Longitudinal Study (UKHLS), a nationally representative sample of adults in the United Kingdom. Participants 12,090 men and women with a mean age of 49.9 (range 18 – 92) living in private households in the United Kingdom were drawn from the representative wave 9 (2017 – 2019) sample of the UKHLS and followed up between the 24th and 30th of April 2020. Main outcome measure Mental health problems were measured using the General Health Questionnaire 12 (GHQ-12), a validated and widely used measure of common mental health symptoms in population studies. The GHQ-12 score cut-off (score of 3 or more) for “psychiatric caseness” (i.e. likely to present with psychiatric disorder) was used to estimate prevalence of mental health problems. Results The percentage of participants classified as experiencing mental health problems increased from 23.3% in 2017-2019 to 36.8% in April 2020. In a multivariate mixed effects logistic regression model all population subgroups examined showed statistically significant increases in mental health problems. Increases were most pronounced among young adults, females, and those with a higher level of education. The rise in mental health difficulties was 8.6 percentage points (95% CI: 4.1 to 13.2) greater among those aged 18-34 compared to those aged 50 – 64, 6.9 points (95% CI: 4.0 to 9.8) greater in females compared to males and 4 points (95% CI: 1.2, 6.9) greater amongst those with a university degree compared to others. Additional analyses of the full UKHLS dataset from 2009-2020 showed that the substantial increase in mental health problems observed in April 2020 was unlikely to be due to seasonality effects or year-to-year variation. Conclusion This study contributes to a rapidly growing evidence base suggesting that mental health problems may have risen substantially during the COVID-19 pandemic.

Overconfident people should be surprised that they are so often wrong. Are they? Three studies examined the relationship between confidence and surprise in order to shed light on the psychology of overprecision in judgment. Participants reported ex-ante confidence in their beliefs, and after receiving accuracy feedback, they then reported ex-post surprise. Results show that more ex-ante confidence produces less ex-post surprise for correct answers; this relationship reverses for incorrect answers. However, this sensible pattern only holds for some measures of confidence; it fails for confidence-interval measures. The results can help explain the robust durability of overprecision in judgment.

ne of the remarkable achievements of the past hundred years has been the reduction of the global toll of death from infectious disease. The combination of applied biological science, improved living and working conditions, and standards of living, together with the benefits of planned parenthood, have transformed the health landscape for millions of people, not least in the developed world. Unfortunately, this led to the belief that these developments had led to the disappearance of infectious diseases as major public health issues with a resulting rundown of public health systems especially in the decades following the Second World War. The stark message of the current global pandemic of COVID-19 is that we can never afford to lower our guard; that nature has many more tricks up its sleeve, especially in the form of novel forms of infection, especially those emerging where the delicate ecological balance of populations and their habitats is disrupted by poverty, urbanization, and the incursion of people into the territory of other species with their own unique commensal organisms.

Sometimes interesting statistical findings are produced by a small number of “lucky” data points within the tested sample. To address this issue, researchers and reviewers are encouraged to investigate outliers and influential data points. Here, we present StatBreak, an easy-to-apply method, based on a genetic algorithm, that identifies the observations that most strongly contributed to a finding (e.g., effect size, model fit, p value, Bayes factor). Within a given sample, StatBreak searches for the largest subsample in which a previously observed pattern is not present or is reduced below a specifiable threshold. Thus, it answers the following question: “Which (and how few) ‘lucky’ cases would need to be excluded from the sample for the data-based conclusion to change?” StatBreak consists of a simple R function and flags the luckiest data points for any form of statistical analysis. Here, we demonstrate the effectiveness of the method with simulated and real data across a range of study designs and analyses. Additionally, we describe StatBreak’s R function and explain how researchers and reviewers can apply the method to the data they are working with.

Predicting behavior of other people is vital for successful social interactions. We tested whether a stress-induced cortisol increase affects healthy young men’s prediction of another individual’s behavior. Forty-two participants were randomly assigned to a stress or a control condition. Afterwards they participated in a modified false-belief task that not only tests false-belief understanding but also the tendency to predict another person’s future behavior based on his former behavior. Subjective ratings and salivary cortisol concentrations revealed a successful stress induction. Stress did not affect participants’ attribution of false beliefs but it increased the probability to predict that a protagonist would act according to his former behavior. Recognizing that stress fosters the interpretation of others’ behavior following their former behavior and not their current goals extends previous research showing that stress fosters our own habitual behavior.

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 and is spread person-to-person through close contact. We aimed to investigate the effects of physical distance, face masks, and eye protection on virus transmission in health-care and non-health-care (eg, community) settings. Methods We did a systematic review and meta-analysis to investigate the optimum distance for avoiding person-to-person virus transmission and to assess the use of face masks and eye protection to prevent transmission of viruses. We obtained data for SARS-CoV-2 and the betacoronaviruses that cause severe acute respiratory syndrome, and Middle East respiratory syndrome from 21 standard WHO-specific and COVID-19-specific sources. We searched these data sources from database inception to May 3, 2020, with no restriction by language, for comparative studies and for contextual factors of acceptability, feasibility, resource use, and equity. We screened records, extracted data, and assessed risk of bias in duplicate. We did frequentist and Bayesian meta-analyses and random-effects meta-regressions. We rated the certainty of evidence according to Cochrane methods and the GRADE approach. This study is registered with PROSPERO, CRD42020177047. Findings Our search identified 172 observational studies across 16 countries and six continents, with no randomised controlled trials and 44 relevant comparative studies in health-care and non-health-care settings (n=25697 patients). Transmission of viruses was lower with physical distancing of 1 m or more, compared with a distance of less than 1 m (n=10736, pooled adjusted odds ratio [aOR] 0·18, 95% CI 0·09 to 0·38; risk difference [RD] –10·2%, 95% CI –11·5 to –7·5; moderate certainty); protection was increased as distance was lengthened (change in relative risk [RR] 2·02 per m; pinteraction=0·041; moderate certainty). Face mask use could result in a large reduction in risk of infection (n=2647; aOR 0·15, 95% CI 0·07 to 0·34, RD –14·3%, –15·9 to –10·7; low certainty), with stronger associations with N95 or similar respirators compared with disposable surgical masks or similar (eg, reusable 12–16-layer cotton masks; pinteraction=0·090; posterior probability >95%, low certainty). Eye protection also was associated with less infection (n=3713; aOR 0·22, 95% CI 0·12 to 0·39, RD –10·6%, 95% CI –12·5 to –7·7; low certainty). Unadjusted studies and subgroup and sensitivity analyses showed similar findings.Interpretation The findings of this systematic review and meta-analysis support physical distancing of 1 m or more and provide quantitative estimates for models and contact tracing to inform policy. Optimum use of face masks, respirators, and eye protection in public and health-care settings should be informed by these findings and contextual factors. Robust randomised trials are needed to better inform the evidence for these interventions, but this systematic appraisal of currently best available evidence might inform interim guidance

As scientists around the world return to work, they’re encountering new safety rules and awkward restrictions — and sometimes writing the protocols themselves.

On this week's podcast: How the coronavirus could transform urban life. The coronavirus has run rampant around the world’s cities, bringing them to a complete standstill. The joys of city life have been upturned as restaurants, theaters, and workplaces have all become potential vectors for transmission of the virus.  This week’s episode looks at how cities could be transformed by the pandemic. Will urban residents flee to the suburbs, or will cities persist as they have through past epidemics? Do the world’s metropolises have a rare opportunity to reinvent themselves for a more equitable, sustainable future? Don’t Touch Your Face hosts Amy Mackinnon and James Palmer are joined by Sara Carr, an assistant professor at Northeastern University’s School of Architecture, and Richard Florida, a professor at the University of Toronto’s School of Cities and Rotman School of Management. 

This N=173,426 social science dataset was collected through the collaborative COVIDiSTRESS Global Survey – an open science effort to improve understandings of the human experiences of the 2020 COVID-19 pandemic between 30th March and 30th May, 2020. The dataset allows a cross-cultural study of psychological and behavioural responses to the Coronavirus pandemic and associated government measures like cancellation of public functions and stay at home orders implemented in many countries. The dataset contains demographic background variables as well as measures of perceived stress (PSS-10), availability of social provisions (SPS-10), trust in various authorities, trust in governmental measures to contain the virus (OECD trust), personality traits (BFF-15), information behaviours, agreement with the level of government intervention, and compliance with preventive measures, along with a rich pool of exploratory variables and written experiences. A global consortium from 44 countries worked together to build and translate a survey with variables of shared interests, and recruited participants in 49 languages and dialects. Raw plus cleaned the data and dynamic visualizations are available.

What’s needed to make a campus safe now is at odds with the experiences people crave, argues Deborah J. Cohan, and institutions are making decisions about the fall based on magical thinking.

Intervention research is often time- and resource-intensive, with numerous participants involved over extended periods of time. In order to maximize the value of intervention studies, multiple outcome measures are often included, either to ensure a diverse set of outcomes is being assessed or to refine assessments of specific outcomes. Here, we advocate for combining assessments, rather than relying on individual measures assessed separately, to better evaluate the effectiveness of interventions. Specifically, we argue that by pooling information from individual measures into a single outcome, composite scores can provide finer estimates of the underlying theoretical construct of interest, while retaining important properties more sophisticated methods often forego, such as transparency and interpretability. We describe different methods to compute, evaluate, and use composites, depending on the goals, design, and data. To promote usability, we also provide a preregistration template that includes examples in the context of psychological interventions, with supporting R code. Finally, we make a number of recommendations to help ensure that intervention studies are designed in a way that maximizes discoveries. A Shiny app and detailed R code accompany this paper, and are available at: https://osf.io/u96em/.

Let’s be blunt. A huge chunk of the UK government’s coronavirus strategy has never seemed to make any sense. There just hasn’t seemed to be a whole lot of logic to it. In many instances contradictions have appeared obvious. And try as we might, there have been some things we’ve never quite been able to rationalise. But here at FT Alphaville we are firm believers in the concept of Chesterton’s fence. This is the principle conjured up by social commentator and philosopher G.K. Chesterton, which says that before critiquing a certain piece of regulation or arguing for its removal, it’s important to understand why the rule was instated in the first place.

A total of 2,618,862 participants reported their potential symptoms of COVID-19 on a smartphone-based app. Among the 18,401 who had undergone a SARS-CoV-2 test, the pro-portion of participants who reported loss of smell and taste was higher in those with a positive test result (4,668 of 7,178 individuals; 65.03%) than in those with a negative test result (2,436 of 11,223 participants; 21.71%) (odds ratio = 6.74; 95% confidence interval = 6.31–7.21). A model combining symptoms to predict probable infection was applied to the data from all app users who reported symptoms (805,753) and predicted that 140,312 (17.42%) participants are likely to have COVID-19

Will temperature checks of employees make workplaces safe?No, not completely. They can help reduce the risk of COVID-19 infections but shouldn’t be the only safety measure employers take.

The purpose of this article is to develop an epistemology of scientific models in scientific research practices, and to show that disciplinary perspectives have crucial role in such an epistemology. A transcendental (Kantian) approach is taken, aimed at explanations of the kinds of questions relevant to the intended epistemology, such as “How is it possible that models provide knowledge about aspects of reality?” The approach is also pragmatic in the sense that the questions and explanations must be adequate and relevant to concrete scientific practice. First it is explained why the idea of models as representations in terms of similarity or isomorphism between a model and its target is too limited as a basis for this epistemology. An important finding is that the target-phenomenon is usually not something that can be observed in a straightforward manner, but requires both characterization in terms of measurable variables and subsumption under (scientific) concepts.The loss of this basis leads to a number of issues, such as: how can models be interpreted as representations if models also include conceptually meaningful linguistic content; how can researchers identify non-observable real-world target-phenomena that are then represented in the model; how do models enable inferential reasoning in performing epistemic tasks by researchers; and, how to justify scientific models. My proposal is to deal with these issues by analyzing how models are constructed, rather than by looking at ready-made models. Based on this analysis, I claim that the identification of phenomena and the construction of scientific models is guided and also confined by the disciplinary perspective within which researchers in a scientific discipline have learned to work. I propose a Kuhnian framework by which the disciplinary perspective can be systematically articulated. Finally, I argue that harmful forms of subjectivism, due to the loss of the belief that models objectively represent aspects of reality, can be overcome by making the disciplinary perspective(s) in a research project explicit, thereby enabling its critical assessment, for which the proposed Kuhnian framework provides a tool.

Virtual meetings are becoming the norm under COVID-19 and winning over many researchers: part 3 in a series on science after the pandemic.

Everything you always wanted to know about higher-order interactions^ (^but were afraid to ask)

Background and ObjectivesSystematic reviews (SRs) are time and resource intensive, requiring approximately 1 year from protocol registration to submission for publication. Our aim was to describe the process, facilitators, and barriers to completing the first 2-week full SR.Study Design and SettingWe systematically reviewed evidence of the impact of increased fluid intake, on urinary tract infection (UTI) recurrence, in individuals at risk for UTIs. The review was conducted by experienced systematic reviewers with complementary skills (two researcher clinicians, an information specialist, and an epidemiologist), using Systematic Review Automation tools, and blocked off time for the duration of the project. The outcomes were time to complete the SR, time to complete individual SR tasks, facilitators and barriers to progress, and peer reviewer feedback on the SR manuscript. Times to completion were analyzed quantitatively (minutes and calendar days); facilitators and barriers were mapped onto the Theoretical Domains Framework; and peer reviewer feedback was analyzed quantitatively and narratively.ResultsThe SR was completed in 61 person-hours (9 workdays; 12 calendar days); accepted version of the manuscript required 71 person-hours. Individual SR tasks ranged from 16 person-minutes (deduplication of search results) to 461 person-minutes (data extraction). The least time-consuming SR tasks were obtaining full-texts, searches, citation analysis, data synthesis, and deduplication. The most time-consuming tasks were data extraction, write-up, abstract screening, full-text screening, and risk of bias. Facilitators and barriers mapped onto the following domains: knowledge; skills; memory, attention, and decision process; environmental context and resources; and technology and infrastructure. Two sets of peer reviewer feedback were received on the manuscript: the first included 34 comments requesting changes, 17 changes were made, requiring 173 person-minutes; the second requested 13 changes, and eight were made, requiring 121 person-minutes.ConclusionA small and experienced systematic reviewer team using Systematic Review Automation tools who have protected time to focus solely on the SR can complete a moderately sized SR in 2 weeks.

The White House’s inability to track the disease as it spread across the nation crippled the government’s response and led to the worst disaster this country has faced in nearly a century

Alex Andreou‘s forensic analysis shows how Boris Johnson’s administration underestimated the speed of the Coronavirus pandemic and have been flying blind ever since

Evidence indicates markedly higher mortality risk from COVID-19 among Black, Asian and Minority Ethnic (BAME) groups, but deaths are not consistent across BAME groups. Similarly, adverse outcomes are seen for BAME patients in intensive care units and amongst medical staff and Health and Care Workers. The exact reasons for this increased risk and vulnerability from COVID-19 in BAME populations are not known. There may be a number of contributing factors in the general population such as overrepresentation of BAME populations in lower socio-economic groups, multi-family and multi-generational households, co-morbidity exposure risks, and disproportionate employment in lower band key worker roles. For Health and Care workers, there are increased health and care setting exposure risks.

According to expectancy-value models of achievement motivation, a core component of increasing student motivation is utility value. Utility value refers to the importance that a task has in one’s future goals. Utility value interventions provide an opportunity for students to make explicit connections between course content and their own lives. A large body of literature suggests that utility value interventions are effective for a wide range of students (e.g., both adolescent and adult learners) in a variety of courses (e.g., introductory psychology, introductory biology, and physics). This review provides (1) an overview of an expectancy value model of achievement motivation, (2) a comprehensive review of the experimental studies of utility value interventions in psychology, (3) concrete pedagogical recommendations based on the evidence from over thirty studies of the utility value intervention, and (4) suggestions for future research directions. After reading this review, college-level psychology instructors should be able to decide whether the utility value intervention is appropriate for their own course and, if so, implement the intervention effectively.

The current study explores various individual difference factors known to predict epistemically suspect beliefs across Western and Eastern cultures. While Western individuals scoring higher on measures of analytic thinking endorsed less epistemically suspect beliefs, this association was not observed in our Japanese samples, suggesting that the often observed negative association between analytic thinking and epistemically suspect beliefs may be exclusive to Western samples. Additionally, we demonstrate that a tendency to think holistically (specifically with regards to causality) was positively associated with the endorsement of epistemically suspect beliefs, both within Western and Eastern samples.

A common dilemma in regulation is determining how much trust authorities can place in people’s self-reports, especially in regulatory contexts where the incentive to cheat is very high. In such contexts, regulators, who are typically risk averse, do not readily confer trust, resulting worldwide in excessive requirements when applying for permits, licenses, and the like. Studies in behavioral ethics have suggested that asking people to ex-ante pledge to behave ethically can reduce their level of dishonesty and noncompliance. However, pledges might also backfire by allowing more people to cheat with no real sanctions. Additionally, pledges’ effects have only been studied in one-shot decision making, and they may only have a short-term effect that could decay in the long run, leading to an overall erosion of trust. We explored the interaction of pledges with sanctions and the decay of their effects on people’s honesty by manipulating whether pledges were accompanied by sanctions (fines) and testing their impact on sequential, repeated ethical decisions. We found that pledges considerably and consistently reduced dishonesty, and this effect was not crowded out by the presence of fines. Furthermore, pledges seem to exert an effect on most people, including those who are relatively less inclined to follow rules and norms. We conclude that pledges could be an effective tool for the behavioral regulation of dishonesty, reduce the regulatory burden, and build a more trusting relationship between government and the public, even in areas where incentives and opportunities to cheat are high.

In this paper a method is proposed to determine whether the result from an original study is corroborated in a replication study. The paper is illustrated using data from the reproducibility project psychology by the Open Science Collaboration. This method emphasizes the need to determine what one wants to replicate: the hypotheses as formulated in the introduction of the original paper, or hypotheses derived from the research results presented in the original paper. The Bayes factor will be used to determine whether the hypotheses evaluated in/resulting from the original study are corroborated by the replication study. Our method to assess the successfulness of replication will better fit the needs and desires of researchers in fields that use replication studies.

Modern media report news remarkably fast, often before the information is confirmed. This general tendency is even more pronounced in times of an increasing demand for information, such as in the case of pressing natural phenomena or the pandemic spreading of diseases. Yet, even if early reports correctly identify their content as suspicions (rather than facts), recipients may not adequately consider the preliminary nature of such information. Theories on language processing suggest that understanding a suspicion requires its reconstruction as a factual assertion first—which can later be erroneously remembered. This would lead to a bias to remember and treat suspicions as if they were factual, rather than falling for the reverse mistake. In five experiments, however, we demonstrate the opposite pattern. Participants read news headlines with explanations for distinct events either in form of a fact or a suspicion (as still being under investigation). Both kinds of framings increased the participants’ belief in the correctness of the respective explanations to an equal extent (relative to receiving no explanation). Importantly, however, this effect was not mainly driven by a neglect of uncertainty cues. In contrast, three memory experiments (recognition and cued recall) revealed a reverse distortion: a bias to falsely remember and treat a presented “fact” as if it were merely a suspicion. These surprising results stress the importance of developing new theoretical accounts on the processing of (un-)certainty cues which take into account their broader context, such as source credibility and the presence of uncertainty in other unrelated headlines.

Unlike OECD countries, India imposed a pre-emptive national lockdown when the total case count was still low.

The COVID-19 pandemic presents a significant challenge to wellbeing for people around the world. Here, we examine which individual and societal factors can predict the extent to which individuals suffer or thrive during the COVID-19 outbreak, with survey data collected from 26,684 participants in 51 countries from 17 April to 15 May 2020. We show that wellbeing is linked to an individual’s recent experiences of specific momentary positive and negative emotions, including love, calm, determination, and loneliness. Higher socioeconomic status was associated with better wellbeing. The present study provides a rich map of emotional experiences and wellbeing around the world during the COVID-19 outbreak, and points to calm, connection, and control as central to our wellbeing at this time of collective crisis.

UK researchers believe blood markers may help them identify those most at risk

Main points Within this bulletin, we refer to the number of coronavirus (COVID-19) infections within the community population; community in this instance refers to private households, and it excludes those in hospitals, care homes or other institutional settings. At any given time between 27 April and 10 May 2020, it is estimated that an average of 0.27% of the community population had COVID-19 (95% confidence interval: 0.17% to 0.41%). It is estimated that an average of 148,000 people in England had COVID-19 during this time (95% confidence interval: 94,000 to 222,000). For individuals working in patient-facing healthcare or resident-facing social care roles, 1.33% tested positive for COVID-19 (95% confidence interval: 0.39% to 3.28%); of those reporting not working in these roles, 0.22% tested positive for COVID-19 (95% confidence interval: 0.13% to 0.35%) There is no evidence of differences in the proportions testing positive between the age categories 2 to 19, 20 to 49, 50 to 69 and 70 years and over.

Data on viral load, as estimated by real-time RT-PCR threshold cycle values from 3,712COVID-19 patients were analysed to examine the relationship between patient age andSARS-CoV-2 viral load. Analysis of variance of viral loads in patients of different age categoriesfound no significant difference between any pair of age categories including children. Inparticular, these data indicate that viral loads in the very young do not differ significantly fromthose of adults. Based on these results, we have to caution against an unlimited re-opening ofschools and kindergartens in the present situation. Children may be as infectious as adults.

Danish transition out of lockdown has become the model for Boris Johnson’s government as it tries to convince teachers to return

Intense non-pharmaceutical interventions were put in place in China to stop transmission of the novel coronavirus disease (COVID-19). As transmission intensifies in other countries, the interplay between age, contact patterns, social distancing, susceptibility to infection, and COVID-19 dynamics remains unclear. To answer these questions, we analyze contact surveys data for Wuhan and Shanghai before and during the outbreak and contact tracing information from Hunan Province. Daily contacts were reduced 7-8-fold during the COVID-19 social distancing period, with most interactions restricted to the household. We find that children 0-14 years are less susceptible to SARS-CoV-2 infection than adults 15-64 years of age (odds ratio 0.34, 95%CI 0.24-0.49), while in contrast, individuals over 65 years are more susceptible to infection (odds ratio 1.47, 95%CI: 1.12-1.92). Based on these data, we build a transmission model to study the impact of social distancing and school closure on transmission. We find that social distancing alone, as implemented in China during the outbreak, is sufficient to control COVID-19. While proactive school closures cannot interrupt transmission on their own, they can reduce peak incidence by 40-60% and delay the epidemic.

Some studies show pupils are less likely to become ill if infected, while others show they are as infectious as adults

Doctors say teachers in England ‘absolutely right’ to prioritise testing over 1 June return

Michael Gove has insisted England's schools are safe to reopen, but acknowledged that "you can never eliminate risk".

In this report, we present initial evidence on how children are spending their time during the lockdown, with a focus on home learning activities and the home learning resources available in different families. This evidence is based on a new survey, specially designed by researchers at IFS and the Institute of Education (IoE). The survey was completed online by over 4,000 parents of children aged 4–15 between Wednesday 29 April and Tuesday 12 May 2020.

The COVID-19 pandemic has impacted many individuals, including university students typically at a developmental stage of emerging adulthood, and facing disruptions to their education. University students may be particularly vulnerable to the mental health sequelae of the current COVID-19-related lockdown in Bangladesh. The study aimed to assess the prevalence of depression, anxiety, and stress and associated factors among university students in Bangladesh early in the COVID-19 outbreak. An internet-based survey was conducted from April 11 to April 24, 2020, involving 3,122 Bangladeshi university students aged 18 to 29 years (59.5% males; mean age 21.4±2 years). After providing informed consent, participants completed an internet-based survey assessing socio-demographic and personal lifestyle-related measures. The DASS-21 scale assessed depression, anxiety, and stress symptomatology. Binary logistic regression analyses were conducted. Prevalence estimates of depression, anxiety, and stress were 62.9%, 63.6%, and 58.6%, respectively. These rates tended to be higher than in previous surveys in the student population, particularly for the dimensions of stress and depression. Also, the pattern of associations with risk and demographic variables had shifted compared to previous work, suggesting situational impact on these. Depression, anxiety, and stress were prevalent among university students during the COVID-19 pandemic situation in April 2020 in Bangladesh. Although a causal relationship cannot be established, the findings suggest that monitoring and support programs may be useful to help students cope with the impact of the COVID-19 outbreak.

The 2020 global COVID-19 pandemic has led to a marked increase in positive discussion of Universal Basic Income (UBI) in political and media circles. However, we do not know whether there has been a corresponding increase in support for the policy in the public at large, or why. Here, we present two studies carried out in April and May 2020 in UK and US samples. In study 1 (n=802), we find that people express much stronger support for a UBI policy for the times of the pandemic and its aftermath than for normal times. This is largely explained by the increased importance they attach to a system that is simple and efficient to administer, and that reduces stress and anxiety in society. In study 2 (n=400), we pit UBI against an equally-generous but targeted social transfer system. We find that, for pandemic times, support shifts towards UBI. This is partially explained by a number of perceived advantages, such as simplicity of administration and suitability for a changing world. Our results illustrate how a changing social and economic situation can bring about marked shifts in policy preferences, through changes in citizen’s perceptions of what is currently important.

If anything prepared me for life in lockdown during COVID-19, it was researching my book Convent Autobiography: Early Modern English Nuns in Exile. I knew from this work that people can live satisfying and complex lives within restricted circumstances, that routines and a sense of shared purpose are important – and that there’s room to develop a deeper understanding of ourselves as individuals and as members of society, once we’ve stripped away so much of what once occupied and shaped our former lives.The women who established or joined the English convents on the Continent and in America between the 1530s and 1800s took huge personal risks. Leaving home to join a convent was illegal and could result in fines or imprisonment, yet nearly 4,000 women did so in order to take vows of poverty, chastity and perpetuity, meaning they promised to stay in their convents for the rest of their lives.Most convents required new nuns to ceremonially ‘die to the world’, meaning they would radically reduce the amount of contact they had with friends and family outside their cloister to focus on their relationship with God and their new spiritual community. Winefrid Thimelby, who served as Prioress of St Monica’s Augustinian Convent in Louvain from 1668-1690, beautifully articulates what separation and absence from loved ones meant to her in a letter to her sister Katherine back in England:

Clinical manifestations of coronavirus disease 2019 (COVID-19) are rare or absent in children and adolescents;1Lee P-I Hu Y-L Chen P-Y Huang Y-C Hsueh P-R Are children less susceptible to COVID-19?.J Microbiol Immunol Infect. 2020; (published online Jan 1.)DOI:10.1016/j.jmii.2020.02.011Crossref Scopus (14) Google Scholar,  2Pavone P Giallongo A La Rocca G Ceccarelli M Nunnari G Recent COVID-19 outbreak—effect in childhood.Infectious Diseases & Tropical Medicine. 2020; 6: e594Google Scholar hence, early clinical detection is fundamental to prevent further spreading. We report three young patients presenting with chilblain-like lesions who were diagnosed with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Two of them were asymptomatic and potentially contagious. Skin lesions, such as erythematous rashes, urticaria, and chicken pox-like vesicles, were reported in 18 (20·4%) of 88 patients with COVID-19 in a previous study.3Recalcati S Cutaneous manifestations in COVID-19: a first perspective.J Eur Acad Dermatol Venereol. 2020; (published online March 26.)DOI:10.1111/jdv.16387Crossref Scopus (11) Google Scholar These symptoms developed at the onset of SARS-CoV-2 infection or during hospital stay and did not correlate with disease severity.3Recalcati S Cutaneous manifestations in COVID-19: a first perspective.J Eur Acad Dermatol Venereol. 2020; (published online March 26.)DOI:10.1111/jdv.16387Crossref Scopus (11) Google Scholar In our cases, lesions involved the acral sites, especially the dorsum of the digits of the feet, beginning as erythematous-violaceous patches that slowly evolved to purpuric lesions and then to blisters and ulceronecrotic lesions, with final complete return to normal. Burning and itching were also present with some of the lesions. Informed consent was obtained from the parents of patients 1 and 2 and from patient 3 himself.

In top journals, more papers fail than pass replication tests and papers failing replications spread as widely as replicating papers. This dynamic raises research costs by over 20bn annually, jeopardizes the literature, and exposes the need for new methods for predicting replicability. Using 96 studies that underwent rigorous manual replication, we developed an artificial intelligence (AI) model that predicts a paper’s replicability. We then tested the model on 317 diverse out-of-sample studies that span disciplines, methods, and topics. We find that AI predicts replicability better than statistics and individual reviewers and as accurately as prediction markets, the gold standard of replicability methods. Further, AI generalizes to out-of-sample data at AUC levels up to 0.78. Finally, tests indicate that the AI model does not show biases common to human reviewers. We discuss how AI can address replication problems at scale in ways that current methods cannot and can advance research by combining human and machine intelligence.

Replicability tests of scientific papers show that the majority of papers fail replication. Moreover, failed papers circulate through the literature as quickly as replicating papers. This dynamic weakens the literature, raises research costs, and demonstrates the need for new approaches for estimating a study’s replicability. Here, we trained an artificial intelligence model to estimate a paper’s replicability using ground truth data on studies that had passed or failed manual replication tests, and then tested the model’s generalizability on an extensive set of out-of-sample studies. The model predicts replicability better than the base rate of reviewers and comparably as well as prediction markets, the best present-day method for predicting replicability. In out-of-sample tests on manually replicated papers from diverse disciplines and methods, the model had strong accuracy levels of 0.65 to 0.78. Exploring the reasons behind the model’s predictions, we found no evidence for bias based on topics, journals, disciplines, base rates of failure, persuasion words, or novelty words like “remarkable” or “unexpected.” We did find that the model’s accuracy is higher when trained on a paper’s text rather than its reported statistics and that n-grams, higher order word combinations that humans have difficulty processing, correlate with replication. We discuss how combining human and machine intelligence can raise confidence in research, provide research self-assessment techniques, and create methods that are scalable and efficient enough to review the ever-growing numbers of publications—a task that entails extensive human resources to accomplish with prediction markets and manual replication alone.