The Pandemic Shuttered My Town’s Only Coffee Shop

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."

What Is The Future Of Cities?

10.1177/0956797620916782

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.

Beyond Western, Educated, Industrial, Rich, and Democratic (WEIRD) Psychology: Measuring and Mapping Scales of Cultural and Psychological Distance

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.

Mass. sees 3,840 new coronavirus cases, 189 new deaths as officials begin reporting ‘probable’ 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.

The psychology of physical distancing

10.1073/pnas.1607151113

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

Textnets: automated text analysis with networks

10.1038/s41562-020-0896-8

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.

Global supply-chain effects of COVID-19 control measures

10.1016/S1470-2045(20)30286-2

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.

COVID-19 and the US health insurance conundrum

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.

What history can tell us about infectious diseases

10.1177/2515245920917950

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.

StatBreak: Identifying “Lucky” Data Points Through Genetic Algorithms

10.1016/ S0140-6736(20)31142-9

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

Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis

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

Return to the lab: scientists face shiftwork, masks and distancing as coronavirus lockdowns ease

10.5281/zenodo.3746024

Scenario analyses for COVID-19 outbreak in the United Kingdom

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.

Messy, Beloved Community

10.1038/s41591-020-0916-2

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

Real-time tracking of self-reported symptoms to predict potential 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.

Will temperature checks of employees make workplaces safe?

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

How scientific conferences will survive the coronavirus shock

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

Networks beyond pairwise interactions: structure and dynamics

10.1016/j.jclinepi.2020.01.008

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.

A full systematic review was completed in 2 weeks using automation tools: a case study

The Scientific Advisory Group for Emergencies (SAGE) provides scientific and technical advice to support government decision makers during emergencies.

Scientific Advisory Group for Emergencies (SAGE): Coronavirus (COVID-19) response

and please consider joining the initiative, by joining the reddit community forum, or volunteering in other ways http://scibeh.org 7/7

behav. sciences will still be central in all of this (if anything, their role looks set to broaden), but the balance of content in the @SciBeh feed will likely change. That also means adapting our sources Please help by tagging @SciBeh in any relevant material you find! 6/7

in a context that already saw rising levels of instability pre-pandemic, and a flood of mis- and dis-information and polarisation fuelling means of information exchange 5/7

and divergent views on what our future should look like, all to be negotiated with fragile democratic legitimacy (pandemic response options featured in no election manifesto, and political decisions will be made after limited discussion at warp speed) 4/7

All of this change will take place in a societal and political context of increasingly divergent (or perceived to be divergent) individual interests (high risk vs. low risk, young vs. old, rich vs. poor, crisis winners vs. losers, now immune vs. 'haven't had it yet') 3/7

what will only continue to grow over the next months is the demand for science guiding the restructure of key aspects of society (how we work, travel, education) short, mid and long term, and how we manage economic fallout and economic transformation 2/7

A thought and a plea from @SciBeh: as the pandemic unfolds, we will see shifting in what aspects of the beh. sciences are most relevant to crisis response - the "first wave" emphasised risk communication, behaviour change, and mental health - 1/7

Problem in using lag indicators: 21 days on average from infection to death- and countries seem to track 'progress' based on deaths. So by the time the data is worrying & increase in deaths is exponential, it's already too late.

Then ease measures while testing widely & w/ good data systems that alert public whether it is red/amber/green in their area. Need clusters of cases identified rapidly & broken up before tips over into sustained community transmission. If it tips, hard to avoid another lockdown.

My suggestion: bring down daily new cases to a low level, get test/trace/isolate in place and core infrastructure build up, get regular testing going for essential workers/teachers/students, monitor borders for imported cases, & move to mandatory masks in shops/public transport.

Looking at the estimates for daily new cases in England (8K/day), the openings of shops/schools on Monday, watching carefully what's happening in East Asia & combining this with what we know so far about this virus --> feels like mistakes are being repeated from early March.

#PerfectlyConfident takes both the #1 and #2 spots on Amazon's "Hot new releases"??

Pauly Saal, a Michelin-starred restaurant in Berlin, was one of the first restaurants in Europe to restart operations last week. How did its chefs, waiters — and diners — cope on its opening night?

In a German Restaurant, the Sommelier Lifts His Mask to Smell the Wine

2005.14356

Centrality, which quantifies the "importance" of individual nodes, is among the most essential concepts in modern network theory. Most prominent centrality measures can be expressed as an aggregation of influence flows between pairs of nodes. As there are many ways in which influence can be defined, many different centrality measures are in use. Parametrized centralities allow further flexibility and utility by tuning the centrality calculation to the regime most appropriate for a given network. Here, we identify two categories of centrality parameters. Reach parameters control the attenuation of influence flows between distant nodes. Grasp parameters control the centrality's potential to send influence flows along multiple, often nongeodesic paths. Combining these categories with Borgatti's centrality types [S. P. Borgatti, Social Networks 27, 55-71 (2005)], we arrive at a novel classification system for parametrized centralities. Using this classification, we identify the notable absence of any centrality measures that are radial, reach parametrized, and based on acyclic, conservative flows of influence. We therefore introduce the ground-current centrality, which is a measure of precisely this type. Because of its unique position in the taxonomy, the ground-current centrality has significant advantages over similar centralities. We demonstrate that, compared to other conserved-flow centralities, it has a simpler mathematical description. Compared to other reach centralities, it robustly preserves an intuitive rank ordering across a wide range of network architectures. We also show that it produces a consistent distribution of centrality values among the nodes, neither trivially equally spread (delocalization), nor overly focused on a few nodes (localization). Other reach centralities exhibit both of these behaviors on regular networks and hub networks, respectively.

A Current-Flow Centrality With Adjustable Reach

#Italy remains one of the worst outbreaks & one of the best & most consistent responses to lockdown/NPI measures. 0.6% positive rate; STILL testing at rate of greater than 1/1000 each day. The US is NOT currently on this path. (some regions are). 33K fatalities.

2005.14564

10.1088/2632-072X/ab8a61

Ranking algorithms are pervasive in our increasingly digitized societies, with important real-world applications including recommender systems, search engines, and influencer marketing practices. From a network science perspective, network-based ranking algorithms solve fundamental problems related to the identification of vital nodes for the stability and dynamics of a complex system. Despite the ubiquitous and successful applications of these algorithms, we argue that our understanding of their performance and their applications to real-world problems face three fundamental challenges: (i) Rankings might be biased by various factors; (2) their effectiveness might be limited to specific problems; and (3) agents' decisions driven by rankings might result in potentially vicious feedback mechanisms and unhealthy systemic consequences. Methods rooted in network science and agent-based modeling can help us to understand and overcome these challenges.

Network-based ranking in social systems: three challenges

Random phenomena are not obligated to follow one of the few dozen distributions that humans have given names to.

We should be able to explain good faith third parties how science works and why we do what we do.In Germany we just had an open science flare up. A famous virologists (Prof. Christian Drosten) published a preprint and colleagues gave feedback on it, mostly how to improve the statistical analysis and as far as I can judge this only made the conclusion stronger. Our Daily Mail (Bild Zeitung) spun that into a series of stories about Drosten doing shady science and one former public health official and professor was willing to help them by calling for a retraction, while the key finding stood firm and all that was needed were some revisions.There was close to a popular uprising against the Bild Zeitung. Science kept Germany safe and we would not let the Bild Zeitung drag us to the USA or UK. You can see the burning buildings and looted Target Store under the hashtags. #TeamScience and #TeamDrostenIt was perfectly possible to explain to good faith third parties that preprints were preliminary, that peer review and disagreements belong to science, that feedback is normal (one of the reviewers is now an author) and that no work of science is perfect, but that it was good enough to come to the carefully formulated conclusion, which was only a small part of the puzzle. I am sure for nearly everyone this was a bizarre world they did not know, normally peer review in closed. Surely they did not understand how it works in the short time this flare up happened, but they trusted science and the scientists from many fields who told them all was fine.Even if this could be abused by bad faith actors, I think it was good to publish this study as a preprint, to have people see the peer review in the open. That is good science, especially in these times were we cannot afford to wait too long, and we should do so.

Complex systems such as ecological communities and neuron networks are essential parts of our everyday lives. These systems are composed of units which interact through intricate networks. The ability to predict sudden changes in the dynamics of these networks, known as critical transitions, from data is important to avert disastrous consequences of major disruptions. Predicting such changes is a major challenge as it requires forecasting the behavior for parameter ranges for which no data on the system are available. We address this issue for networks with weak individual interactions and chaotic local dynamics. We do this by building a model network, termed an effective network, consisting of the underlying local dynamics and a statistical description of their interactions. We show that behavior of such networks can be decomposed in terms of an emergent deterministic component and a fluctuation term. Traditionally, such fluctuations are filtered out. However, as we show, they are key to accessing the interaction structure. We illustrate this approach on synthetic time series of realistic neuronal interaction networks of the cat cerebral cortex and on experimental multivariate data of optoelectronic oscillators. We reconstruct the community structure by analyzing the stochastic fluctuations generated by the network and predict critical transitions for coupling parameters outside the observed range.

10.1103/PhysRevX.10.021047

Revealing Dynamics, Communities, and Criticality from Data

10.1103/PhysRevE.101.062302

Network data sets are often constructed by some kind of thresholding procedure. The resulting networks frequently possess properties such as heavy-tailed degree distributions, clustering, large connected components, and short average shortest path lengths. These properties are considered typical of complex networks and appear in many contexts, prompting consideration of their universality. Here we introduce a simple model for correlated relational data and study the network ensemble obtained by thresholding it. We find that some, but not all, of the properties associated with complex networks can be seen after thresholding the correlated data, even though the underlying data are not “complex.” In particular, we observe heavy-tailed degree distributions, a large numbers of triangles, and short path lengths, while we do not observe nonvanishing clustering or community structure.

Thresholding normally distributed data creates complex networks

Comprehensive study supports government’s current physical distancing guidance

Risk of infection could double if 2-metre rule reduced, study finds

As the UK stared down the barrel of a coronavirus epidemic in early March, the biggest fear was that hospitals would be swamped and incapacitated by a tsunami of patients. It happened in Wuhan and northern Italy. The NHS largely pulled through, but there were still times hospitals became overwhelmed. One of those was when a London hospital became suddenly engulfed with victims.

Coronavirus: The London hospital hit by a 'tidal wave' of patients

Lockdown break-ups, job losses and urgent relocations are thought to have led to a surge in the rental sector.

Lockdown break-ups and job changes boost rentals

We should all be pretty familiar with the narrative by now. An arrogant, exceptionalist British government was until mid-March pursuing a reckless strategy of herd immunity that would have callously allowed a huge number of old and vulnerable people to die and the health system to be overwhelmed. Then came a “bombshell” from Imperial College London: a “doomsday report” predicting there would be 500,000 deaths if we were to carry on down that road, prompting a sudden government U-turn, and ultimately the decision to lock the country down. Gone was the Machiavellian Dominic Cummings plan of “letting old people die”; in was STAY AT HOME; PROTECT THE NHS; SAVE LIVES. (The notion that it was Cummings who was pushing for the herd immunity idea has since been disputed, while the notion that Cummings was into the staying-at-home idea has also since been, er, disputed.)But then, after all that, it turned out that the computer code Imperial had relied on to predict the future in that March 16 paper (“Report 9”) was outdated, full of bugs, and based on flimsy, unscientific assumptions. The code was simply totally unreliable. All academic epidemiology should be defunded immediately. The lockdown, surely, could no longer be justified. As the Telegraph put it in their headline on May 16, this could be “the most devastating software mistake of all time”! It could “supersede the failed Venus space probe” in terms of economic cost and lives lost!The question is: is any of this true? Did the modelling, as the Daily Mail put it last Saturday, “single-handedly (trigger) a dramatic change in the government’s handling of the outbreak”? If the code is so bad, does that render the modelling useless? And would shoddy modelling remove the justification for the lockdowns in place across much of the globe anyway?

Is the “science” behind the lockdown any good?

Science advice to Government needs to be direct and given without fear or favour

We are learning a lot, including how to do it better next time

The neuroscientist who advises Independent Sage on Covid-19 discusses the predictive power of his mathematical modelling and the risk of a second wave

Covid-19 expert Karl Friston: 'Germany may have more immunological “dark matter”'

Yesterday was the saddest day in the history of @Twitter

Without effective treatment or vaccine, social measures remain at the heart of the world’s response to the COVID-19 pandemic. With this, behaviour change remains one of the top three scientific priorities for the coming months, according to the Lancet, and the behavioural sciences are implicated throughout the complex task of bringing societies out of lockdown. Providing a suitable evidence base for these high-stakes policy decisions means drawing together research across presently, at best, loosely interconnected sub-fields and disciplines, formulating and conducting new research, distilling findings into formats digestible by policymakers, journalists, and the public, and providing expert guidance to decision-making bodies. What feels like years ago, we wrote a paper to prompt debate on how the behavioural sciences could reconfigure to rise to the challenge by finding new ways of knowledge creation, integration, and dissemination. A new model of “proper science without the drag” that accelerates knowledge production without sacrificing quality is needed. Constructive, critical input at all stages of the research process, from study idea, through design, to data analysis is the obvious way for improving research quality while cutting down on time. The pressing need for such input has already become apparent: early voices warned about the adverse impact of fast research under pressure, and this can now be seen in poor quality studies, needless reduplication of effort, and irresponsible amplification of problematic results through media pick-up.

Bringing together behavioural scientists for crisis knowledge management