Passengers face fines from 15 June for flouting new rule to stop spread of coronavirus

Face coverings to be made compulsory on public transport in England

Wearing face coverings will be compulsory on public transport in England from 15 June, the transport secretary has said.

Coronavirus: Face coverings to be mandatory on public transport

The world is six months into the COVID-19 pandemic, the global response to which is unprecedented in human history. But just as the pandemic has tightened borders, closed workplaces, and isolated people in their homes, scientific borders have been flung open and barriers torn down. Despite academic institutes and laboratories being shuttered worldwide, the urgency and pace of the pandemic has spawned a new era of scientific collaboration, open discourse, and efficiency. These collaborative efforts have included substantial input from rheumatologists—an eventuality that was mostly unanticipated in the early months of 2020.

10.1016/S2665-9913(20)30102-8

COVID-19: a time to reflect

Nearly 600 front-line health care workers appear to have died of COVID-19, according to Lost on the Frontline, a project launched by The Guardian and KHN that aims to count, verify and memorialize every health care worker who dies during the pandemic.

Exclusive: Nearly 600 — And Counting — US Health Workers Have Died Of COVID-19

Government accused of totalitarianism and censorship after Bolsonaro orders end to publication of numbers

Brazil stops releasing Covid-19 death toll and wipes data from official site

2006.03435

Urban mobility increasingly relies on multimodality, combining the use of bicycle paths, streets, and rail networks. These different modes of transportation are well described by multiplex networks. Here we propose the overlap census method which extracts a multimodal profile from a city's multiplex transportation network. We apply this method to 15 cities, identify clusters of cities with similar profiles, and link this feature to the level of sustainable mobility of each cluster. Our work highlights the importance of evaluating all the transportation systems of a city together to adequately identify and compare its potential for sustainable, multimodal mobility.

Extracting the multimodal fingerprint of urban transportation networks

2006.03207

Hierarchies permeate the structure of real networks, whose nodes can be ranked according to different features. However, networks are far from tree-like structures and the detection of hierarchical ordering remains a challenge, hindered by the small-world property and the presence of a large number of cycles, in particular clustering. Here, we use geometric representations of undirected networks to achieve an enriched interpretation of hierarchy that integrates features defining popularity of nodes and similarity between them, such that the more similar a node is to a less popular neighbor the higher the hierarchical load of the relationship. The geometric approach allows us to measure the local contribution of nodes and links to the hierarchy within a unified framework. Additionally, we propose a link filtering method, the similarity filter, able to extract hierarchical backbones containing the links that represent statistically significant deviations with respect to the maximum entropy null model for geometric heterogeneous networks. We applied our geometric approach to the detection of similarity backbones of real networks in different domains and found that the backbones preserve local topological features at all scales. Interestingly, we also found that similarity backbones favor cooperation in evolutionary dynamics modelling social dilemmas.

Geometric detection of hierarchical backbones in real networks

The coronavirus 2019–2020 pandemic (COVID-19) poses unprecedented challenges for governments and societies around the world (1). Nonpharmaceutical interventions have proven to be critical for delaying and containing the COVID-19 pandemic (2–6). These include testing and tracing, bans on large gatherings, nonessential business and school and university closures, international and domestic mobility restrictions and physical isolation, and total lockdowns of regions and countries. Decision-making and evaluation or such interventions during all stages of the pandemic life cycle require specific, reliable, and timely data not only about infections but also about human behavior, especially mobility and physical copresence. We argue that mobile phone data, when used properly and carefully, represents a critical arsenal of tools for supporting public health actions across early-, middle-, and late-stage phases of the COVID-19 pandemic.

10.1126/sciadv.abc0764

Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle

For those pointing to countries like S.Korea, Singapore, China, Taiwan, Australia, Germany, Denmark to say there will be no second wave in the UK or US- take a deeper look at their incredible public health response & infrastructure. None sat back & said ‘Let’s see what happens.’

Ignoring advice and lessons from Europe and Asia on mass testing and isolation, health experts insist that our approach is the answer

Britain goes it alone over coronavirus. We can only hope the gamble pays off

Most of the research thus far on treatment in #COVID19 seem to be targeting the wrong timeframe of disease...the point is to start treatment prior to hyperinflammation...would like to see more studies target earlier timeframe of disease #COVID19FOAM

We read with interest the recent article by Feeney and colleagues on ‘utilizing (sic) patient advocates in Parkinson's disease’.1 We acknowledge that sound methods for patient engagement need to be developed and evaluated. This can be especially relevant for Parkinson's disease (PD), a field we three know well from many years’ experience as active patient advocates living with PD. However, these methods need to be based on relevant premises. The article focuses on drug development and, for that reason alone, falls short of reflecting the full experience of PD upon which patient engagement needs to be constructed. Patient engagement, as defined by patients, reflects a wider scope of thought and experience than when defined by people who are not themselves patients.2 Before we start developing methods, we need a cultural and ideological shift across the field towards an acceptance that involving patients in therapeutic development is self‐evident.

10.1111/hex.13087

Patient advocates respond to ‘Utilizing Patient Advocates…’ by Feeney et al

Many more will die unless we fix structural problems that have blighted Britain’s response, say scientists

UK experts call for coronavirus inquiry to prevent deadly second wave

The vast majority of manufacturers continued to operate through the COVID-19 crisis (67.1%) or temporarily halted only part of their operations (31.6%). Among large manufacturers, 50.8% are completely operational, while roughly 73% of small and medium-sized firms state the same.

Weak domestic demand was the top primary business challenge for manufacturers in the second quarter (83.1%), supplanting the inability to attract and retain talent (41%), which had been the top concern for 10 consecutive quarters.

Weak domestic demand was the top primary business challenge for manufacturers in the second quarter (83.1%), supplanting the inability to attract and retain talent (41%), which had been the top concern for 10 consecutive quarters.

Daily deaths higher than anticipated --> past 3 days have been 412, 377, 324 compared to last week's 363, 338, 351. 74 daily deaths on 23rd March start of lockdown. Need to watch carefully for second peak --> by the time deaths start taking off, too late given 21 day lag.

tl;dr: Many scientists write code that is crappy stylistically, but which is nevertheless scientifically correct (following rigorous checking/validation of outputs etc). Professional commercial software developers are well-qualified to review code style, but most don’t have a clue about checking scientific validity or what counts as good scientific practice. Criticisms of the Imperial Covid-Sim model from some of the latter are overstated at best. Update (2020-06-02): The CODECHECK project has independently reproduced the results of one of the key reports (“Report 9”) that was based on the Imperial code, addressing some of the objections raised in the spurious “reviews” that are the subject of this article.

Why you can ignore reviews of scientific code by commercial software developers

The media vilification of the government adviser is about far more than social distancing

It's not just Neil Ferguson – scientists are being attacked for telling the truth

10.23668/psycharchives.3019

The outbreak of the COVID-19 pandemic has prompted the German government and the 16 German federal states to announce a variety of public health measures in order to suppress the spread of the coronavirus. These non-pharmaceutical measures intended to curb transmission rates by increasing social distancing (i.e., diminishing interpersonal contacts) which restricts a range of individual behaviors. These measures span moderate recommendations such as physical distancing, up to the closures of shops and bans of gatherings and demonstrations. The implementation of these measures are not only a research goal for themselves but have implications for behavioral research conducted in this time (e.g., in form of potential confounder biases). Hence, longitudinal data that represent the measures can be a fruitful data source. The presented data set contains data on 14 governmental measures across the 16 German federal states. In comparison to existing datasets, the dataset at hand is a fine-grained daily time series tracking the effective calendar date, introduction, extension, or phase-out of each respective measure. Based on self-regulation theory, measures were coded whether they did not restrict, partially restricted or fully restricted the respective behavioral pattern. The time frame comprises March 08, 2020 until May 15, 2020. The project is an open-source, ongoing project with planned continued updates in regular (approximately monthly) intervals. This release note presents the background, dataset structure and coding rules of the dataset.

The ZPID lockdown measures dataset for Germany

The current Covid-19 crisis has demonstrated the importance of a robust research sector. Not only have researchers been critical in the immediate response from scientific and public health perspectives, but the embedded social, cultural, economic, environmental and political knowledge of researchers will be vital as we begin to the long process of recovering from the pandemic. Yet ironically, it is the next generation of research leaders, often referred to as ‘early career researchers’, that are particularly exposed by the current situation.

Could the next generation of researchers be lost in the aftermath of Covid-19?

10.1093/acrefore/9780190228637.013.1250

Political communicators have long used framing as a tactic to try to influence the opinions and political decisions of others. Frames capture an essence of a political issue or controversy, typically the essence that best furthers a communicator’s political goals. Framing has also received much attention by scholars; indeed, the framing literature is vast. In the domain of political decision making, one useful distinction is between two types of frames: emphasis frames and equivalence frames. Emphasis frames present an issue by highlighting certain relevant features of the issue while ignoring others. Equivalence frames present an issue or choice in different yet logically equivalent ways. Characterizing the issue of social welfare as a drain on the government budget versus a helping hand for poor people is emphasis framing. Describing the labor force as 95% employed versus 5% unemployed is equivalency framing. These frames differ not only by their content but also by the effects on opinions and judgements that result from frame exposure as well as the psychological processes that account for the effects. For neither emphasis nor equivalence frames, however, are framing effects inevitable. Features of the environment, such as the presence of competing frames, or individual characteristics, such as political predispositions, condition whether exposure to a specific frame will influence the decisions and opinions of the public.

Framing and Political Decision Making: An Overview

Lack of evidence is not an excuse for lack of action. Some countries threw everything & the kitchen sink at controlling this virus & protecting their populations. Others spent weeks debating, discussing, producing reports & selling spin, all to get to the perfect evidence-base.

Once this pandemic is over, we should thank NHS staff in real terms, argues Louis Lederman

Louis Lederman: NHS workers need protecting too

David Spiegelhalter said this morning, “Peer review has just disappeared from scientific analysis.” This is complete and utter nonsense. Our editors across 19 Lancet journals do nothing else but peer review. We intensively review all COVID-19 research papers. You know this David.

2006.03023

The current crisis, at the time of writing, has had a profound impact on the financial world, introducing the need for creative approaches to revitalising the economy at the micro level as well as the macro level. In this informal analysis and design proposal, we describe how infrastructure for digital assets can serve as a useful monetary and fiscal policy tool and an enabler of existing tools in the future, particularly during crises, while aligning the trajectory of financial technology innovation toward a brighter future. We propose an approach to digital currency that would allow people without banking relationships to transact electronically and privately, including both internet purchases and point-of-sale purchases that are required to be cashless. We also propose an approach to digital currency that would allow for more efficient and transparent clearing and settlement, implementation of monetary and fiscal policy, and management of systemic risk. The digital currency could be implemented as central bank digital currency (CBDC), or it could be issued by the government and collateralised by public funds or Treasury assets. Our proposed architecture allows both manifestations and would be operated by banks and other money services businesses, operating within a framework overseen by government regulators. We argue that now is the time for action to undertake development of such a system, not only because of the current crisis but also in anticipation of future crises resulting from geopolitical risks, the continued globalisation of the digital economy, and the changing value and risks that technology brings.

Digital Currency and the Economic Crisis: Helping States Respond

2006.02870

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.

The why, how, and when of representations for complex systems

2006.02483

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.

Time Series Methods and Ensemble Models to Nowcast Dengue at the State Level in Brazil

2006.02972

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.

Severability of mesoscale components and local time scales in dynamical networks

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.

CDC Revise Awful COVID-19 Commuting Recommendations, But They’re Still Not Great

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.

A mysterious company’s coronavirus papers in top medical journals may be unraveling

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.

Response to Widespread Reaction to Recent Lancet Article on Hydroxychloroquine

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.

The pandemic's first major research scandal erupts

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.

Lancet, New England Journal retract Covid-19 studies, including one that raised safety concerns about malaria drugs

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.

MIT Study Examines How Many Jobs Robots Really Replace

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.

BPS issues new guidance for psychologists on safety considerations when returning to the workplace

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.

Using cellphone data, national study predicts huge June spike in Houston coronavirus cases

10.1016/S0140-6736(20)31200-9

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.

The plight of essential workers during the COVID-19 pandemic

2005.10646

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.

On the Aggression Diffusion Modeling and Minimization in Online Social Networks

10.1126/science.aax9553

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.

Social determinants of health and survival in humans and other animals

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