Healthcare leaders and managers must take proactive steps to protect the psychological wellbeing of their staff during and after the coronavirus outbreak, according to BPS advice.

As strong measures are being put in place to slow down the spread of COVID-19, many are looking at how technology and data could help. With many countries using mobile phone location data to analyze the effectiveness of social distancing measures and help predict the potential geographic spread of the disease, the focus has now shifted to whether mobile phones could also help warn users if they have been exposed to an infected person.

CEPR has launched a new online peer-reviewed review to disseminate emerging scholarly work on the Covid-19 epidemic. Very quickly after the onset of the epidemic a large number of policy papers have been written by economic scholars, many of which have appeared on VoxEU. This has been enormously helpful to improve our understanding of policy options. The next step requires more formal investigations, based on explicit theory and/or empirical evidence. This is what Covid Economics: Vetted and Real-Time Papers aims to provide.

A resource list from the AAAS Science and Human Rights Coalition Secretariat and Coalition member organizations to help confront the urgent challenges at the intersections of science, technology and human rights raised by the novel coronavirus pandemic. 

Daniel Calovi, a postdoc at the Max-Planck-Institute for Animal Behaviour at the department "Collectve Behaviour" of Iain Couzin, is a co-founder of the project “crowdfight covid19”. The website, a service for COVID-19 researchers, tries to bring together people who are offering help with people looking for help. Here Daniel Calovi talks about the main concept, motivation and overall goal of the initiative. The cofounders are Sara Arganda Carreras (Universidad Rey Juan Carlos, Madrid, Spain) and Alfonso Pérez Escudero, (Center for Integrative Biology, CNRS and Université Paul Sabatier, Toulouse, France).

The world is currently witnessing a public health crisis that is unprecedented in our lifetimes: the global COVID-19 pandemic. At the Psychological Science Accelerator (PSA), we are deeply concerned about the many impacts of this outbreak, but we are also optimistic about behavioral science’s potential to mitigate these impacts. With our network of more than 500 labs from over 70 countries, we believe that – with your help – the PSA can play a crucial role in this process. This can only occur if our member labs, and new labs who would like to join us, contribute to project administration and local data collection.

The biggest problem in finding a cure or vaccine isn’t money. It’s lack of coordination. We need a better way of identifying the global research gaps for this 21st century Manhattan Project of health

The Stream Graph shows the evolution of interest in different news topics:

“Fake news,” broadly defined as false or misleading information masquerading as legitimate news, is frequently asserted to be pervasive online with serious consequences for democracy. Using a unique multimode dataset that comprises a nationally representative sample of mobile, desktop, and television consumption, we refute this conventional wisdom on three levels. First, news consumption of any sort is heavily outweighed by other forms of media consumption, comprising at most 14.2% of Americans’ daily media diets. Second, to the extent that Americans do consume news, it is overwhelmingly from television, which accounts for roughly five times as much as news consumption as online. Third, fake news comprises only 0.15% of Americans’ daily media diet. Our results suggest that the origins of public misinformedness and polarization are more likely to lie in the content of ordinary news or the avoidance of news altogether as they are in overt fakery.

Engagements with government agencies, healthcare organizations and academic institutions around the world including in Arkansas, California, Georgia, New York, Texas, Czech Republic, Greece, Poland, Spain and UK

I am looking for insights into how to help the public wear and use face masks effectively and understand the underlying rationales.

Abstract OBJECTIVE: To examine the effectiveness of eye protection, face masks, or person distancing on interrupting or reducing the spread of respiratory viruses. DESIGN: Update of a Cochrane review that included a meta-analysis of observational studies during the SARS outbreak of 2003. DATA SOURCES: Eligible trials from the previous review; search of Cochrane Central Register of Controlled Trials, PubMed, Embase and CINAHL from October 2010 up to 1 April 2020; and forward and backward citation analysis. DATA SELECTION: Randomised and cluster-randomised trials of people of any age, testing the use of eye protection, face masks, or person distancing against standard practice, or a similar physical barrier. Outcomes included any acute respiratory illness and its related consequences. DATA EXTRACTION AND ANALYSIS: Six authors independently assessed risk of bias using the Cochrane tool and extracted data. We used a generalised inverse variance method for pooling using a random-effects model and reported results with risk ratios and 95% Confidence Intervals (CI). RESULTS: We included 15 randomised trials investigating the effect of masks (14 trials) in healthcare workers and the general population and of quarantine (1 trial). We found no trials testing eye protection. Compared to no masks there was no reduction of influenza-like illness (ILI) cases (Risk Ratio 0.93, 95%CI 0.83 to 1.05) or influenza (Risk Ratio 0.84, 95%CI 0.61-1.17) for masks in the general population, nor in healthcare workers (Risk Ratio 0.37, 95%CI 0.05 to 2.50). There was no difference between surgical masks and N95 respirators: for ILI (Risk Ratio 0.83, 95%CI 0.63 to 1.08), for influenza (Risk Ratio 1.02, 95%CI 0.73 to 1.43). Harms were poorly reported and limited to discomfort with lower compliance. The only trial testing quarantining workers with household ILI contacts found a reduction in ILI cases, but increased risk of quarantined workers contracting influenza. All trials were conducted during seasonal ILI activity. CONCLUSIONS: Most included trials had poor design, reporting and sparse events. There was insufficient evidence to provide a recommendation on the use of facial barriers without other measures. We found insufficient evidence for a difference between surgical masks and N95 respirators and limited evidence to support effectiveness of quarantine. Based on observational evidence from the previous SARS epidemic included in the previous version of our Cochrane review we recommend the use of masks combined with other measures.

There can be considerable mathematical and statistical dif-ferences between the following two items:1) (univariate) binary predictions, bets and "beliefs" (ex-pressed as a specific "event" will happen/will not hap-pen) and, on the other,2) real-world continuous payoffs (that is, numerical benefitsor harm from an event).Way too often, the decision science and economics literatureuses one as a proxy for another. Some results, say overestima-tion of tailprobability, by humans can be stated in one result1and unwarranted conclusions that people overestimatetail riskhave been chronically made since.23In this paper we show the mischaracterization as made inthe decision-science literature and presents the effect of theirconflation. We also examine the differences under thin andfat tails –for under Gaussian distributions the effect can bemarginal, which may have lulled the psychology literature intothe conflation.The net effects are:1)Spuriousness of many psychological results:This af-fects risk management claims, particularly the research resultsto the effect that humans overestimate the risks of rare events.Many perceived "biases" are shown to be just mischaracteri-zations by psychologists. We quantify such conflations with ametric for "pseudo-overestimation".2)Being a "good forecaster" in binary space doesn’tlead to having a good actual performance:The reverseis also true, and the effect is exacerbated under nonlinearities.A binary forecasting record is likely to be a reverse indicatorunder some classes of distributions or deeper uncertainty.

3)Machine Learning:Some nonlinear payoff functions,while not lending themselves to verbalistic expressions and"forecasts", can be well captured by ML or expressed in optioncontracts.4)Fattailedness:The difference is exaggerated when thevariable under consideration lies in the power law classes ofprobability distributions.5)Model error:Binary forecasts are not particularly proneto model error; real world payoffs are.The paper is organized as follows. We first present thedifference in statistical properties thanks to precise mathe-matical definitions of the two types in section II. The textis structured with (numbered) "definitions","comments", and"examples". Section III presents the differences in the contextof Gaussian-like and fat tailed environments (that is, the classof distributions dominated by remove events), a separationbased on the presence or absence of a characteristic scale. Sec-tion IV develops the mathematics of spurious overestimation,comparing the properties of payoffs under thin tails (section A)and Fat Tails (section B), discusses the conflation and presentsthe impact of model error (section D). Section V appliesto the calibration in psychological experiments. Section VIpresents a catalogue of scoring metrics. Section VII shows theloss functions of machine learning and how they fit nonlinearpayoffs in practical applications.The appendix shows the mathematical derivations and exactdistribution of the various payoffs, along with an exact explicitfunctions for the Brier score helpful for other applicationssuch as significance testing and sample sufficiency (new tothe literature).

Update (April 2, 2020): Through a contribution from Research Manitoba, Research Nova Scotia, and Alberta Innovates, CIHR was able to fund an additional three grants, bringing the total number of funded grants to 99 and a total investment of $54.2M. To continue to contribute to global efforts to address the COVID-19 outbreak, the Government of Canada is investing an additional $25.8M in research. This investment is a portion of the $275M in funding for research on medical countermeasures against COVID-19 announced by the Prime Minister on March 11, 2020.  This investment will support 49 researchers across the country whose teams will focus on developing and implementing measures to rapidly detect, manage, and reduce the transmission of COVID-19. This additional funding builds on the $27M investment announced on March 6, and brings the Government’s total investment in coronavirus research to date to $52.6M to support 96 research teams from across the country.  The Government of Canada provided the funding ($26.8M) for the first wave of COVID-19 research projects through the CIHR, the Natural Sciences and Engineering Research Council of Canada (NSERC), the Social Sciences and Humanities Research Council (SSHRC), the Canada Research Coordinating Committee (CRCC) through the New Frontiers in Research Fund (NFRF), the International Development Research Centre (IDRC), and Genome Canada (GC).

The COVID-19 pandemic is now more than a crisis in public health; it may seriously challenge many different aspects of our society. The Nuffield Foundation’s purpose is to understand and advance social well-being in the domains of Education, Welfare and Justice. Although we do not fund health research, we are interested in the fast-emerging questions relating to the wider social significance of this public health emergency. We are a responsive funder, and we imagine that social scientists and others are already thinking about these dramatic developments in relation to their own research interests. Though the terrain is outside our normal funding remit, we want to listen to our research community and your suggestions of what sorts of research projects could be instigated in the coming weeks to capture the unfolding potential crisis.

To contain the Coronavirus disease (COVID-19) pandemic, one of the non-pharmacological epidemic control measures in response to the COVID-19 outbreak is reducing the transmission rate of SARS-COV-2 in the population through (physical) social distancing. An interactive web-based mapping platform that provides timely quantitative information on how people in different counties and states reacted to the social distancing guidelines was developed with the support of the National Science Foundation (NSF). It integrates geographic information systems (GIS) and daily updated human mobility statistical patterns derived from large-scale anonymized and aggregated smartphone location big data at the county-level in the United States, and aims to increase risk awareness of the public, support governmental decision-making, and help enhance community responses to the COVID-19 outbreak.

While ancient scientists often had patrons to fund their work, peer review of proposals for the allocation of resources is a foundation of modern science. A very common method is that proposals are evaluated by a small panel of experts (due to logistics and funding limitations) nominated by the grant-giving institutions. The expert panel process introduces several issues - most notably: 1) biases introduced in the selection of the panel. 2) experts have to read a very large number of proposals. Distributed Peer Review promises to alleviate several of the described problems by distributing the task of reviewing among the proposers. Each proposer is given a limited number of proposals to review and rank. We present the result of an experiment running a machine-learning enhanced distributed peer review process for allocation of telescope time at the European Southern Observatory. In this work, we show that the distributed peer review is statistically the same as a `traditional' panel, that our machine learning algorithm can predict expertise of reviewers with a high success rate, and we find that seniority and reviewer expertise have an influence on review quality. The general experience has been overwhelmingly praised from the participating community (using an anonymous feedback mechanism).

We present a timely and novel methodology that combines disease estimates from mechanistic models with digital traces, via interpretable machine-learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real-time. Specifically, our method is able to produce stable and accurate forecasts 2 days ahead of current time, and uses as inputs (a) official health reports from Chinese Center Disease for Control and Prevention (China CDC), (b) COVID-19-related internet search activity from Baidu, (c) news media activity reported by Media Cloud, and (d) daily forecasts of COVID-19 activity from GLEAM, an agent-based mechanistic model. Our machine-learning methodology uses a clustering technique that enables the exploitation of geo-spatial synchronicities of COVID-19 activity across Chinese provinces, and a data augmentation technique to deal with the small number of historical disease activity observations, characteristic of emerging outbreaks. Our model's predictive power outperforms a collection of baseline models in 27 out of the 32 Chinese provinces, and could be easily extended to other geographies currently affected by the COVID-19 outbreak to help decision makers.

Our aim is to assess the effects of the COVID-19 restriction measures on the mobility patterns of people in the UK. These measures are strong public health policies which came into place as a consequence of the COVID-19 pandemic and its potential impact on the British population and on the NHS. To do so, we analyse changes in the average levels of mobility of anonymous mobile phone users across the country at different time periods, which include the periods when the restriction measures are in place and enforced by authorities. Summary of main initial findings In early March, before restriction measures were enforced, mobility levels decreased by about 10% compared to their normal levels before the pandemic. In the middle of March, after people were encouraged to work from home and reduce their travelling, mobility levels dropped by about 50% compared to before the pandemic. From March 24th onwards the UK entered a state of lockdown, with only essential travelling allowed. This led to a reduction of about 70% in the mobility levels. Mobility levels have dropped consistently in all areas across the UK after the lockdown measurements. These results present our initial analysis of the restriction measures and their effect on mobility across the UK. This might be of interest to epidemiologist who can use this to estimate contact matrices, and to public health policy makers who have to assess the impact of their policies on the British population.

With most schools closed nationwide because of the coronavirus pandemic, a national poll of young people ages 13 to 17 suggests distance learning has been far from a universal substitute. The poll of 849 teenagers, by Common Sense Media, conducted with SurveyMonkey, found that as schools across the country transition to some form of online learning, 41% of teenagers overall, including 47% of public school students, say they haven't attended a single online or virtual class. This broad lack of engagement with online learning could be due to many factors.

Steven and others recovering from substance use disorders are especially vulnerable to COVID-19, medical and treatment experts say. They may be unable to get necessary prescriptions and treatments vital to their recovery. They're already more at risk for homelessness, and if they get the disease that means they may be more likely to need hospitalization or be more prone to severe symptoms.

What is happening here? Preprints related to the COVID-19 pandemic are regularly being posted on PsyArXiv. The high level of interest in these studies, paired with the speed with which they are being produced, calls for the research community to actively review these preprints. This tracking sheet includes all preprints posted to PsyArXiv. The preprints are retrieved by running the following search in Twitter: (covid OR covid-19 OR coronavirus OR corona OR pandemic OR nCoV OR infection OR quarantine OR "social distancing" OR outbreak OR SARS OR virus) (from:psyarxivbot), which is augmented by a manual search directly in PsyArXiv. The list will be updated daily. At this time only PsyArXiv is included in the tracker, but additional preprint servers could be added if there is sufficient interest. Beyond the scientific and public need for vetting preprints at this time, one motivation for this project is that it serves as an early prototype for a community-oriented overlay journal. This collection may also be of interest to meta-scientists who want to examine how psychology responded to the pandemic.

The global pandemic is brutally exposing how reliant the EU – and the rest of the world – is on high quality research, collaboration and data sharing. These lessons must be applied in Horizon Europe

Belgian researchers chose to bypass all standard science publishing protocols to publish research that has been overhyped and isn't well understood.

Objective To review and critically appraise published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at risk of being admitted to hospital for covid-19 pneumonia.Design Rapid systematic review and critical appraisal.Data sources PubMed and Embase through Ovid, Arxiv, medRxiv, and bioRxiv up to 24 March 2020.Study selection Studies that developed or validated a multivariable covid-19 related prediction model.Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).Results 2696 titles were screened, and 27 studies describing 31 prediction models were included. Three models were identified for predicting hospital admission from pneumonia and other events (as proxy outcomes for covid-19 pneumonia) in the general population; 18 diagnostic models for detecting covid-19 infection (13 were machine learning based on computed tomography scans); and 10 prognostic models for predicting mortality risk, progression to severe disease, or length of hospital stay. Only one study used patient data from outside of China. The most reported predictors of presence of covid-19 in patients with suspected disease included age, body temperature, and signs and symptoms. The most reported predictors of severe prognosis in patients with covid-19 included age, sex, features derived from computed tomography scans, C reactive protein, lactic dehydrogenase, and lymphocyte count. C index estimates ranged from 0.73 to 0.81 in prediction models for the general population (reported for all three models), from 0.81 to more than 0.99 in diagnostic models (reported for 13 of the 18 models), and from 0.85 to 0.98 in prognostic models (reported for six of the 10 models). All studies were rated at high risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, and high risk of model overfitting. Reporting quality varied substantially between studies. Most reports did not include a description of the study population or intended use of the models, and calibration of predictions was rarely assessed.Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that proposed models are poorly reported, at high risk of bias, and their reported performance is probably optimistic. Immediate sharing of well documented individual participant data from covid-19 studies is needed for collaborative efforts to develop more rigorous prediction models and validate existing ones. The predictors identified in included studies could be considered as candidate predictors for new models. Methodological guidance should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, studies should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.

Apple Inc. and Google unveiled a rare partnership to add technology to their smartphone platforms that will alert users if they have come into contact with a person with Covid-19. People must opt in to the system, but it has the potential to monitor about a third of the world’s population.

The goal is to identify the best means of encouraging individuals to change the way they live. Young people may need more convincing

What follows is my personal perspective, as an individual with some real world experience in epidemic modeling during previous pandemics and shouldn’t reflect on any group or institution I might be affiliated with.

How epidemiologists rushed to model the coronavirus pandemic.

Feeling overwhelmed by a lockdown and the need to suddenly adopt e-learning? Keep connected and compassionate, says clinical psychologist Desiree Dickerson.

Therapists weigh in with advice on how to establish new ground rules when your living space suddenly becomes your workspace.

The most difficult thing for an epidemiological model to predict: human behavior.

Beginning in late 2019, the coronavirus disease (COVID-19) has now been deemed a pandemic by the World Health Organization. There have been hundreds of thousands of confirmed cases and more than tens of thousands of deaths. Many people are adhering to self-isolation and quarantine instructions while facing xenophobia, stigma, and more. There are collective, felt experiences of fear and anxiety, inadequate supplies of food and other essentials, confusion from unclear information, and financial loss. Others are enduring a life-threatening course of the disease by themselves or experiencing the loss of loved ones around them. Health care professionals are working around the clock despite high-risk exposure for themselves. These stressful and potentially traumatic events are likely to be related to negative short-term and long-term mental health outcomes, such as anxiety, depression, posttraumatic stress, confusion, and anger. Around the globe, countries are challenged to deal with these psychological and social consequences of the COVID-19 pandemic. This special section aims to serve as a platform for researchers, practitioners, and policy makers in various countries to exchange experiences, challenges, successes, and lessons learned during the COVID-19 pandemic Authors are encouraged to submit commentaries on the following five questions: How is the situation in your country regarding the COVID-19 pandemic? How do you think the pandemic is affecting the population from a mental health perspective? How do people respond to the situation in your country? What is helpful and what is less helpful in dealing with the situation? How is health care currently organized? Commentaries should not exceed five double-spaced pages, excluding references, tables, or figures, if any. Cover page and abstract are not required and will not be reviewed. Aim for approximately 500-1,000 words in the main body of the text. Submit commentaries to the journal's manuscript submission portalzsburwzauc by Thursday, April 30, 2020.  Email the Special Section Editors if you have questions.

First tutorial of the Net-COVID online series: Understanding and Exploring Network Epidemiology in the Time of Coronavirus. Lecture by Laurent Laurent Hebert-Dufresne from the University of Vermont. See go.umd.edu/net-covid for more information about the online series.

Patients are under lockdown and health workers are at risk of infection. Paul Webster reports on how telemedicine is being embraced like never before.In the face of a surge in cases of coronavirus disease 2019 (COVID-19), physicians and health systems worldwide are racing to adopt virtualised treatment approaches that obviate the need for physical meetings between patients and health providers. But many doctors are watching warily.

In the past few weeks, many academic research groups (including our own) have been working on providing information about mobility in many countries. This is an incredible effort, carried out during extraordinary times and by really talented people. There are many methodologies (using different mobility metrics: radius of gyration, number of visited places), datasets (gps points, data detail records), and analysis (trips, activity) going around, so maybe trying to keep a centralized repository of them, with some commentary, would be worthwhile both to keep updated and personal edification, and also as a service to the community. We have done something similar for mobile stream data research per country.

Global responses to the coronavirus disease 2019 (COVID-19) pandemic are converging with pervasive, existing sexual and reproductive health and justice inequities to disproportionately impact the health, wellbeing, and economic stability of women, girls, and vulnerable populations. People whose human rights are least protected are likely to experience unique difficulties from COVID-19.1McGinn T Reproductive health of war-affected populations: what do we know?.Int Fam Plan Perspects. 2000; 26: 174-180Google Scholar Women, girls, and marginalised groups are likely to carry a heavier burden of what will be the devastating downstream economic and social consequences of this pandemic.2Wenham C Smith J Morgan R COVID-19: the gendered impacts of the outbreak.Lancet. 2020; 395: 846-848Google Scholar A sexual and reproductive health and justice framework—one that centres human rights, acknowledges intersecting injustices, recognises power structures, and unites across identities—is essential for monitoring and addressing the inequitable gender, health, and social effects of COVID-19.

“This disease is unlike anything I have seen before. If you end up on ICU, you are potentially in real trouble. I have never seen anything like it before.” These words were written by one intensive care physician working at a London teaching hospital. As deaths accumulate, the early message that severe acute respiratory syndrome coronavirus 2 causes mostly a mild illness has been shown to be dangerously false. One in five patients develop complications and are at grave risk. A further misunderstanding concerns age. An impression was given that only older people are at risk of serious illness. But the average age of non-survivors is under 70 years. Two-thirds of those admitted to hospital in China were younger than 60 years. The complexity of illness in these often quite young patients is challenging to comprehend. Patients are not commonly dying, for example, from hypoxaemia. The cause of death is often cardiovascular, with high-sensitivity cardiac troponin I being a more reliable marker for mortality. Thromboembolic disease, hypercytokinaemia, secondary sepsis, hypovolaemia, and renal complications are a toxic combination of problems for intensivists to manage. The number of patients admitted to intensive care units has been doubling every 2 days. Deaths are so frequent that hospitals have created emergency mortuary space, often in car parks, moving bodies at night to avoid media scrutiny. Intensive care teams are doing truly remarkable work. But it is a huge physical and mental struggle. Here is one physician, writing from the front line. You can feel the anguish in her words. “We are therapeutically bereft (phrase borrowed from a colleague), and I am concerned that the push to do something, anything—which I fully share as I am on the wards with these patients too and it feels desperate—is resulting in suggestions of repurposed drugs too rapidly and without a cool look at plausibility or risks.” The focus of the political debate about coronavirus disease 2019 (COVID-19) has so far been almost exclusively about the public health dimensions of this pandemic. But at the bedside there is another story, one that has so far been largely hidden—a story of terrible suffering, distress, and utter bewilderment.

SARS-CoV-2 does not discriminate, but without careful consideration, the global response to the COVID-19 pandemic might. Demographic data from small studies are already informing political decisions and clinical research strategies. Women and men are affected by COVID-19, but biology and gender norms are shaping the disease burden. The success of the global response—the ability of both women and men to survive and recover from the pandemic's effects—will depend on the quality of evidence informing the response and the extent to which data represent sex and gender differences.

Emergency efforts are underway to find optimum medical products to prevent infection and diagnose and treat patients during the coronavirus disease 2019 (COVID-19) pandemic. Production and supply chains for COVID-19 candidate drugs (such as chloroquine and hydroxychloroquine), and for many other essential medical products, are being impaired by this crisis.1Guerin PJ Singh-Phulgenda S Strub-Wourgaft N The consequence of COVID-19 on the global supply of medical products: why Indian generics matter for the world.F1000Res. 2020; (published online April 1.)DOI:10.12688/f1000research.23057.1Google Scholar Supply chains for vital drugs for other diseases (such as systemic lupus erythematosus) are being disrupted because they are being repurposed to use against COVID-19, without adequate supporting evidence.Without preparation for the quality assurance of diagnostic tests, drugs, and vaccines, the world risks a parallel pandemic of substandard and falsified products. Interventions are needed globally to ensure access to safe, quality assured, and effective medical products on which the world's population will depend.

The coronavirus disease 2019 (COVID-19) pandemic is not only stretching health systems to their limits, it is rapidly becoming a threat to the entire global economy, on a scale much greater than the 2007–08 financial crisis. Policymakers from high-income countries have been quick to respond, pledging unprecedented amounts of support to citizens and businesses. The EU announced a “no limits” commitment to protect European economies by purchasing sovereign and corporate debt, while the US congress has agreed a US$2 trillion stimulus bill.Such measures are not, however, open to low-income and middle-income countries (LMICs), which will face the brunt of the COVID-19 burden. Emerging markets were among the first from which investors fled and have so far withdrawn more than $83 billion from them, the largest capital flow ever recorded. This limits the credit available to governments and businesses, pushes down commodity prices and real economic activity, and ultimately reduces health-system budgets at a time when capacity urgently needs to expand.

This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations - to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process.

The outbreak of coronavirus disease 2019 (COVID-19), which began in Wuhan, China, in late 2019, has spread to 203 countries as of March 30, 2020, and has been officially declared a global pandemic.1WHORolling updates on coronavirus disease (COVID-19): WHO characterizes COVID-19 as a pandemic.https://www.who.int/emergencies/diseases/novel-coronavirus-2019/events-as-they-happenDate accessed: March 30, 2020Google Scholar With unprecedented public health interventions, local transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) appears now to have been contained in China. Multiple countries are now experiencing the first wave of the COVID-19 epidemic; thus, gaining an understanding of how these interventions prevented the transmission of SARS-CoV-2 in China is urgent.

The novel coronavirus disease 2019 (COVID-19) has swept across 210 countries and territories with over 1·2 million cases and 67 594 deaths reported by April 6, 2020. Most countries have implemented social distancing measures to curb the spread of infection and minimise the impact of the virus.188 countries have implemented country-wide school closures, but a modelling study by Ferguson and colleagues concluded that in the UK, school closures alone will reduce COVID-19 deaths by only 2–4%. Most evidence for school closures has come from influenza outbreaks such as the 2009 H1N1 influenza pandemic in which children were disproportionately affected. During that time, the US closed 700 schools but the response was local and only for a couple of weeks. To tackle COVID-19, Chinese schools have been closed for more than 2 months, and many countries have closed their schools and colleges indefinitely.

In an attempt to control the 2019 coronavirus disease (COVID-19) pandemic, governments across the world have implemented distancing measures during the search for medical countermeasures, resulting in millions of people being isolated for long periods. Alcohol misuse is one of the leading causes of preventable mortality, contributing annually to about 3 million deaths worldwide.1WHOGlobal status report on alcohol and health 2018. World Health Organization, Geneva2019https://www.who.int/substance_abuse/publications/global_alcohol_report/en/Date accessed: April 1, 2020Google Scholar In some individuals, long term, excessive alcohol misuse might escalate into an alcohol use disorder. The potential public health effects of long-term isolation on alcohol use and misuse are unknown.

We identified seasonal human coronaviruses, influenza viruses and rhinoviruses in exhaled breath and coughs of children and adults with acute respiratory illness. Surgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets. Our results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.

Members of the CERN community have shown ingenuity and generosity in their contribution to the struggle against the COVID-19 pandemic. The “CERN against COVID-19” taskforce, which was established at the end of March to identify and support these initiatives, has already received hundreds of messages suggesting ideas ranging from producing sanitizer gel to designing and building sophisticated medical equipment. Indeed, CERN and its community can make use of important resources such as the Worldwide LHC Computing Grid, mechanical workshops, sophisticated design and prototyping facilities, advanced technologies and expertise ranging from science and engineering to industrialisation.

Previous crises have shown how an economic crash has dire consequences for public health. But in the COVID-19 pandemic, the world is entering uncharted territory. The world’s leaders must prepare to preserve health.

For the past two years, our expert panelists have been informing the public about the extent to which economists agree or disagree on important public policy issues.

The global coronavirus lockdown is making it hard for Mexican drug cartels to operate. With borders shut and limited air traffic, cartels are turning on each other. Sandra Weiss reports from Mexico City.

We Americans are now experiencing the tragic consequences of our slow, uncoordinated response to the coronavirus pandemic. While this experience will surely help us respond better to future health crises, it’s important we apply the hard lessons learned to even greater disasters. In particular, there are many parallels between the coronavirus pandemic and the climate change crisis. We need to recognize that we’re making the same mistakes with climate change and correct them before it’s too late. Below are some of these key blunders.

Social mixing patterns are crucial in driving transmission of infectious diseases and informing public health interventions to contain their spread. Age-specific social mixing is often inferred from surveys of self-recorded contacts which by design often have a very limited number of participants. In addition, such surveys are rare, so public health interventions are often evaluated by considering only one such study. Here we report detailed population contact patterns for United Kingdom based self-reported contact data from over 36,000 volunteers that participated in the massive citizen science project BBC Pandemic. The amount of data collected allows us generate fine-scale age-specific population contact matrices by context (home, work, school, other) and type (conversational or physical) of contact that took place. These matrices are highly relevant for informing prevention and control of new outbreaks, and evaluating strategies that reduce the amount of mixing in the population (such as school closures, social distancing, or working from home). In addition, they finally provide the possibility to use multiple sources of social mixing data to evaluate the uncertainty that stems from social mixing when designing public health interventions.

Objective: Establishing a social contact data sharing initiative and an interactive tool to assess mitigation strategies for COVID-19. Results: We organized data sharing of published social contact surveys via online repositories and formatting guidelines. We analyzed this social contact data in terms of weighted social contact matrices, next generation matrices, relative incidence and R0. We incorporated location-specific isolation measures (e.g. school closure or telework) and capture their effect on transmission dynamics. All methods have been implemented in an online application based on R Shiny and applied to COVID-19 with age-specific susceptibility and infectiousness. Using our online tool with the available social contact data, we illustrate that social distancing could have a considerable impact on reducing transmission for COVID-19. The effect itself depends on assumptions made about disease-specific characteristics and the choice of intervention(s).

I think with all the excitement about using technology for contact tracing, as the product lead for TraceTogether, I feel compelled to call out the following section of the policy brief and white paper that we published to accompany the open-sourcing of the BlueTrace protocol and OpenTrace codebase.If you ask me whether any Bluetooth contact tracing system deployed or under development, anywhere in the world, is ready to replace manual contact tracing, I will say without qualification that the answer is, No. Not now and, even with the benefit of AI/ML and — God forbid — blockchain 😂 (throw whatever buzzword you want), not for the foreseeable future.There are critical factors (like ventilation — see below; update: or singing!) that a purely automated system will not have access to. You cannot “big data” your way out of a “no data” situation. Period.Any attempt to believe otherwise, is an exercise in hubris, and technology triumphalism. There are lives at stake. False positives and false negatives have real-life (and death) consequences. We use TraceTogether to supplement contact tracing — not replace it.

Coronavirus disease 2019 (COVID-19) is an acute respiratory tract infection that emerged in late 20191,2. Initial outbreaks in China involved 13.8% cases with severe, and 6.1% with critical courses3. This severe presentation corresponds to the usage of a virus receptor that is expressed predominantly in the lung2,4. By causing an early onset of severe symptoms, this same receptor tropism is thought to have determined pathogenicity, but also aided the control, of severe acute respiratory syndrome (SARS) in 20035. However, there are reports of COVID-19 cases with mild upper respiratory tract symptoms, suggesting the potential for pre- or oligosymptomatic transmission6–8. There is an urgent need for information on body site-specific virus replication, immunity, and infectivity. Here we provide a detailed virological analysis of nine cases, providing proof of active virus replication in upper respiratory tract tissues. Pharyngeal virus shedding was very high during the first week of symptoms (peak at 7.11 × 108 RNA copies per throat swab, day 4). Infectious virus was readily isolated from throat- and lung-derived samples, but not from stool samples, in spite of high virus RNA concentration. Blood and urine never yielded virus. Active replication in the throat was confirmed by viral replicative RNA intermediates in throat samples. Sequence-distinct virus populations were consistently detected in throat and lung samples from the same patient, proving independent replication. Shedding of viral RNA from sputum outlasted the end of symptoms. Seroconversion occurred after 7 days in 50% of patients (14 days in all), but was not followed by a rapid decline in viral load. COVID-19 can present as a mild upper respiratory tract illness. Active virus replication in the upper respiratory tract puts the prospects of COVID-19 containment in perspective.

We’ve put together some useful tips, advice and links to articles that you might find helpful in dealing with the effects of the coronavirus pandemic.

Do you want to learn more about educational and child psychology? Our introductory Youtube playlist gives you a taster of the diverse nature of educational and child psychology.

The British Psychological Society’s Division of Educational and Child Psychology (DECP) has published tips for schools, parents and carers dealing with the unprecedented school closures.

The Division of Clinical Psychology’s Faculty for Children, Young People and their Families (CYPF) has published tips for talking to children about illness, in light of the ongoing Covid-19 pandemic.

Registration deadline: 12pm on 12th May The BPS Division of Health Psychology is delighted to facilitate a webinar providing an ‘Introduction to Open Science in Health Psychology’.The webinar will be co-facilitated by three world leading experts in the field of Open Science, and aims to provide an introduction to open science and its uses and implications for research.It focuses on providing an opportunity for attendees to increase their understanding of the applications of Open Science, particularly in relation to the fields of Psychology and Health Psychology.Where possible Webinars will be recorded to allow attendees access after the live session has taken place.Please note that there is no guarantee every event will be recorded, therefore we strongly advise those registered for the event to attend the live session.ObjectivesProvide an accessible opportunity for CPD, with a particular focus on people working in Health Psychology at all stages of their careersIncrease understanding of what Open Science is and its application for research, particularly in Health PsychologyIncrease understanding of the opportunities and challenges of Open Science for qualitative methodsLearning OutcomeAt this end of this webinar, you should have increased understanding of:What Open Science is and why it is important for research.The importance of Open Science for the profession of psychology, particularly Health Psychology.The opportunities and challenges of Open Science for research using qualitative methods

Opinion dynamics have attracted the interest of researchers from different fields. Local interactions among individuals create interesting dynamics for the system as a whole. Such dynamics are important from a variety of perspectives. Group decision making, successful marketing and constructing networks (in which consensus can be reached or prevented) are a few examples of existing or potential applications. The invention of the Internet has made the opinion fusion faster, unilateral, and at a whole different scale. Spread of fake news, propaganda, and election interferences have made it clear there is an essential need to know more about these dynamics. The emergence of new ideas in the field has accelerated over the last few years. In the first quarter of 2020, at least 50 research papers have emerged, either peer-reviewed and published or on pre-print outlets such as arXiv. In this paper, we summarize these ground-breaking ideas and their fascinating extensions, and introduce newly developed concepts.

Since the first case of novel coronavirus disease 2019 (COVID-19) was diagnosed in December 2019, it has swept across the world and galvanized global action. This has brought unprecedented efforts to institute the practice of physical distancing (called in most cases “social distancing”) in countries all over the world, resulting in changes in national behavioral patterns and shutdowns of usual day-to-day functioning. While these steps may be critical to mitigate the spread of this disease, they will undoubtedly have consequences for mental health and well-being in both the short and long term. These consequences are of sufficient importance that immediate efforts focused on prevention and direct intervention are needed to address the impact of the outbreak on individual and population level mental health.

Question  Was there an association of public health interventions with improved control of the COVID-19 outbreak in Wuhan, China?Findings  In this cohort study that included 32 583 patients with laboratory-confirmed COVID-19 in Wuhan from December 8, 2019, through March 8, 2020, the institution of interventions including cordons sanitaire, traffic restriction, social distancing, home quarantine, centralized quarantine, and universal symptom survey was temporally associated with reduced effective reproduction number of SARS-CoV-2 (secondary transmission) and the number of confirmed cases per day across age groups, sex, and geographic regions.Meaning  A series of multifaceted public health interventions was temporally associated with improved control of the COVID-19 outbreak in Wuhan and may inform public health policy in other countries and regions.

The spread of COVID-19 is posing an unprecedented threat to health systems worldwide[1]. The fast propagation of the disease combined with the existence of covert contagions by asymptomatic individuals make the controlling of this disease particularly challenging. The key parameter to track the progression of the epidemics is the effective reproduction number R, defined as the number of secondary infections generated by an infected individual[2]. The suppression of the epidemics is directly related to this value, and is attained when R<1.Here, we find an analytical expression for R as a function of mobility restrictions and confinement measures, using an epidemic model tailored for COVID-19. This expression for R is an extremely useful tool to design containment policies that are able to suppress the epidemics. We applied our epidemic model for the case of Spain, successfully forecasting both the observed incidence in each region and the overload of the health system. The expression for R allowed us to determine the precise reduction of mobility kappa_0 needed to bend the curve of epidemic incidence, which turned out to be kappa_0 ≈ 0.7. This value, for the case of Spain, translates to a total lockdown with the exception of the mobility associated to essential services, a policy that was finally enforced on March 28.

Make no mistake: we need science right now. There’s a global problem whose solution lies squarely in the domain of building empirical knowledge about the natural world. This comes with something of a gold rush, both for money and for profile.But the historical precedent for bad scientific work wasting millions of dollars and hours is strong. The specific precedents for work that met an immediate need and looked cool, but ended up being revealed as desperately overhyped, are also substantial. And right now, instead of bad science just slowing down progress, it can fly straight to the heart of global policymaking on the run. This makes it not some milquetoast term we’d normally use, like “problematic”, but rather dangerous.I hope this doesn’t happen, but it already happens when the world is spinning on its normal axis. Right now, twisting out of control, it has the potential to both happen more and stack bodies.

The aim of this tutorial is to provide an introduction to data manipulation in R, primarily using tools from the tidyverse. Given that lots of people are currently moving their data collection procedures online, we will use an output file from Gorilla as an example. However, the tools should readily apply regardless of your data collection software.1 In this first part of the tutorial, we will cover the basics of extracting the relevant data from your output files. In Part 2, we will cover some extra tips and tricks for monitoring sample size during online data collection, scaling up the tools to more complex datasets, and re-organising your data flexibly.

n this tutorial, we will introduce multilevel correlations (or hierarchical / random-effects correlations) and how to compute them using the new correlations package from the easystats suite.

Decentralized Privacy-Preserving Proximity Tracing

The CoMuNe Lab's infodemic data set (250+ millions tweets) used to feed the COVID-19 Infodemic Observatory (https://covid19obs.fbk.eu/) is quickly growing on a daily basis. Raw tweets cannot be shared, by Twitter policy, and our list of tweet IDs would require a huge computing efforts in terms of hydration (about 32 days required, to date) and storage (about 2 Terabytes, to date), clearly not affordable by all researchers worldwide that may lack the adequate computational infrastructure. However, to favor the fight against #COVID19 infodemic, we decided to call for collaborations: we will collect your proposals and accurately evaluate them, to select up to 10 projects to join with our processed data or with data to be processed ad hoc for your requirements. Note that this is not a hackathon.Please, submit wisely and provide us with all the materials you think will support your application. We will accept request on a rolling basis, until the 10 projects will be selected. Selection criteria: 1) soundness, 2) effectiveness, 3) readiness (i.e., priority will be given to projects already started and proposing to be finalized by integrating our data within the next 3-6 months). Larger teams are not necessarily favored, but single-proponent projects are discouraged.

The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the phase 2 of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively, voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates - if and when they want, for specific aims - with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.

Research has shown that we touch our faces far more often than we may realize and this creates a major path for the spread of the disease.  Behavioral scientists have made recommendations based on empirical evidence on how to reduce the number of times you touch your face. More information about this project is here and here. We have created an infographic with these tips. And with help from our many friends and colleagues around the world (see below), we are working on translating the information to as many languages as possible. Behavioral changes are critically needed to reduce the spread of coronavirus. This is a start so please share the information!

Right now, all eyes are on an urgent science issue and its effects on our society. While everyone has a role to play in reducing the spread of the virus, scientists have unique opportunities to respond to the crisis in impactful ways. For example, some scientists might volunteer their lab or data analysis skills. Regardless of discipline, researchers may find opportunities to pivot their research agenda to address timely and relevant questions. And others might serve as voices for science to amplify evidence, reduce the spread of misinformation, and advocate for science-informed approaches to managing the pandemic. This online panel discussion will include researchers from different disciplines who will share ideas for scientists looking to respond in the current moment.

APA Publishing is grateful for your leadership and is committed to providing you with the resources you need to carry out your work. This free collection includes relevant psychological research published across the APA Journals portfolio. We will update this collection on an ongoing basis.

Five strategies for helping patients during the COVID-19 public health crisis.

A regular workout was shown to ease depression and lift moods.

The sudden, short-lived feeling of anxiety, shortness of breath and disabling fear can be confused with symptoms of coronavirus. Here’s what to do about it.

During a crisis, the people who cope best are those who help others.

It is urgent to understand the future of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) transmission. We used estimates of seasonality, immunity, and cross-immunity for betacoronaviruses OC43 and HKU1 from time series data from the USA to inform a model of SARS-CoV-2 transmission. We projected that recurrent wintertime outbreaks of SARS-CoV-2 will probably occur after the initial, most severe pandemic wave. Absent other interventions, a key metric for the success of social distancing is whether critical care capacities are exceeded. To avoid this, prolonged or intermittent social distancing may be necessary into 2022. Additional interventions, including expanded critical care capacity and an effective therapeutic, would improve the success of intermittent distancing and hasten the acquisition of herd immunity. Longitudinal serological studies are urgently needed to determine the extent and duration of immunity to SARS-CoV-2. Even in the event of apparent elimination, SARS-CoV-2 surveillance should be maintained since a resurgence in contagion could be possible as late as 2024.

The coronavirus disease 2019 (COVID-19) pandemic—and the social distancing measures that many countries have implemented—have caused disruptions to daily routines. As of April 8, 2020, schools have been suspended nationwide in 188 countries, according to UNESCO. Over 90% of enrolled learners (1·5 million young people) worldwide are now out of education. The UNESCO Director-General Audrey Azoulay warned that “the global scale and speed of the current educational disruption is unparalleled”.For children and adolescents with mental health needs, such closures mean a lack of access to the resources they usually have through schools. In a survey by the mental health charity YoungMinds, which included 2111 participants up to age 25 years with a mental illness history in the UK, 83% said the pandemic had made their conditions worse. 26% said they were unable to access mental health support; peer support groups and face-to-face services have been cancelled, and support by phone or online can be challenging for some young people.

The HIV pandemic provides lessons for the response to the novel coronavirus disease 2019 (COVID-19) pandemic: no vaccine is available for either and there are no licensed pharmaceuticals for COVID-19, just as there was not for HIV infection in the early years. Population behaviour will determine the pandemic trajectory of COVID-19,1Anderson RM Heesterbeek H Klinkenberg D Hollingsworth TD How will country-based mitigation measures influence the course of the COVID-19 epidemic?.Lancet. 2020; 395: 931-934Summary Full Text Full Text PDF PubMed Scopus (3) Google Scholar just as it did for HIV.

Rapid development of coronavirus disease 2019 (COVID-19) into a pandemic has called for people to acquire and apply health information, and adapt their behaviour at a fast pace.1Zarocostas J How to fight an infodemic.Lancet. 2020; 395: 676Google Scholar Health communication intended to educate people about the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and how to avoid getting or spreading the infection has become widely available. Most valuable information is created in an easy-to-understand manner that offers simple and practical solutions, such as washing hands, maintaining physical distance2Prem K Liu Y Russell TW et al.The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study.Lancet Public Health. 2020; (published online March 25.)https://doi.org/10.1016/S2468-2667(20)30073-6Google Scholar, and where to find information about the latest recommendations, and advice. Unfortunately, there is also complex, contradictory, and false information.1Zarocostas J How to fight an infodemic.Lancet. 2020; 395: 676Google Scholar Similarly, individuals are considered able to acquire, understand, and use this information in a sound and ethical manner—ie, to be health literate.

The COVID-19 outbreak, caused by the newly described viral pathogen SARS-CoV-2, is having a devastating effect on the scientific community. China has been battling the virus for months and provides a bellwether for the impact we are starting to experience on global research. Throughout that country, experimental facilities were essentially shut down. With travel restrictions imposed to prevent further spread of the coronavirus, laboratory members were unable to leave their hometowns. While students and postdocs from some areas are now gradually returning to their research projects, personnel maximums have been put in place restricting the number of people who can work simultaneously in any one location. Those returning undergo mandatory two-week quarantines in a contracted hotel before rejoining their laboratories. The slow-down in productivity is impacting not only the rate, but also the reporting of science. Editorially, at Cell we have seen delays in timelines for manuscript submissions and resubmissions.

COVID-19 is far more than just a data science issue—it’s a massive public health problem that has resulted in many deaths and is throwing a harsh light onto how we structure our society when it comes to important things like the availability and affordability of healthcare, worker’s rights, and even freedom of movement.But as a data scientist, I do think it’s important to look at the situation through a data science perspective. We’ve all seen curves on Twitter—exponential, flattened, and otherwise—plotted in Excel and been reassured by them or scared by them or wondered whether we could trust them. That’s a data science question, and there are many similar ones that I want to address here, in the hopes that what I write will inspire others to think about the data and feel more empowered about what to do in this situation.

Over the past few weeks, we have seen a surge in need for access to research expertise as the UK Parliament engages with the COVID-19 outbreak.  In this rapidly evolving situation, Parliament needs quick access to researchers who can provide expert insights relating to both Coronavirus and the wider situation.

Accordingly, Cell Systems is responding to the world around us and changing our editorial practice during the COVID-19 pandemic. We are committed to keeping the wheels of the scientific endeavor turning. Experimental labs may lie quiet, but that doesn’t mean that the thinking, analysis, and writing that are critical to discovery and understanding can’t go on. Even more importantly, we are committed to supporting public health measures designed to keep people home unless their work is vital to society or immediately important to coordinated efforts to fight COVID-19.

Countries that have managed to “flatten the curve” without rigorous stay-at-home policies (e.g., Singapore), have employed tracking via smartphone apps to mitigate the impact of COVID-19. Tracking allows government agencies to observe who you have been in contact with and when this contact occurred, thereby rapidly implementing appropriate measures to reduce the spread of COVID-19. The effectiveness of collocation tracking relies on the willingness of the population to support such measures. Gaining the social license – broad community acceptance beyond formal legal requirements – for collocation tracking requires the perceived public health benefits to outweigh concerns of personal privacy, security, and any potential risk of harm.

Aim To estimate the percentage of symptomatic COVID-19 cases reported in different countries using case fatality ratio estimates based on data from the ECDC, correcting for delays between confirmation-and-death.

Metaphors pervade discussions of abstract concepts and complex issues: ideas are ‘light bulbs’, crime is a ‘virus’, and cancer is an ‘enemy’ in a ‘war’.At a process level, metaphors, like analogies, involve structure mapping, in which relational structure from the source domain is leveraged for thinking about the target domain.Metaphors influence how people think about the topics they describe by shaping how people attend to, remember, and process information.The effects of metaphor on reasoning are not simply the result of lexical priming.Metaphors can covertly influence how people think. That is, people are not always aware that they have been influenced by a metaphor.

Using Brunswik’s (1952) lens model framework, Hammond (1965) proposed interpersonal conflict theory to explainthe nature, source, and resolution of disagreement or “cognitive conflict” between parties performing judgment tasks. Anearly review by Brehmer (1976) highlighted the potential of this approach in, for example, understanding the structureof cognitive conflicts, and the effect of task and person variables on judgment policy change and conflict resolution.However, our bibliographic and content reviews from 1976 to the present day demonstrate that research on cognitiveconflict using the lens model has declined sharply, while research on “task conflict” has grown dramatically. Therehas also been a shift to less theoretical precision and methodological rigor. We discuss possible reasons for thesedevelopments, and suggest ways in which lens model research on cognitive conflict can be revitalized by borrowingfrom recent theoretical and methodological advances in the field of judgment and decision making.

In the United States, interventions that cost less than $100,000 per [quality-adjusted life year] gained are often considered “cost effective,” although the precise number is somewhat controversial. . . . The Italian National Health Institute pegged the median age of death from COVID-19 in Italy at 80.5. This is consistent with early data from the United States. . . . The average 80-year old in the United States has a life expectancy of about 9 years, suggesting that on average, a death averted will “buy” 9 extra years of life. In QALY-estimations, this number needs to be adjusted for the “quality of the years”. In Italy, 99% of deaths had an underlying pathology that needs to be incorporated in QALY adjustments. If we use diabetes as a reasonable proxy for the many chronic diseases, we would adjust the 9 years down to 7.8 years or QALYs. In other words: the average loss per person of quality-adjusted life years is 7.8. . . . According to a CDC scenario analysis, the expected range of deaths is from 200,000 to 1.7 million people. This implies the pandemic, if unchecked, will lead to a loss of between 1.56 million and 13.26 million QALYs.

Neurons in the visual cortex sharpen their orientation tuning as humans learn aversive contingencies. A stimulus orientation (CS+) that reliably predicts an aversive noise (unconditioned stimulus: US) is selectively enhanced in lower-tier visual cortex, while similar unpaired orientations (CS−) are inhibited. Here, we examine in male volunteers how sharpened visual processing is affected by fear extinction learning (where no US is presented), and how fear and extinction memory undergo consolidation one day after the original learning episode. Using steady-state visually evoked potentials from electroencephalography in a fear generalization task, we found that extinction learning prompted rapid changes in orientation tuning: Both conditioned visuocortical and skin conductance responses to the CS+ were strongly reduced. Next-day re-testing (delayed recall) revealed a brief but precise return-of-tuning to the CS+ in visual cortex accompanied by a brief, more generalized return-of-fear in skin conductance. Explorative analyses also showed persistent tuning to the threat cue in higher visual areas, 24 h after successful extinction, outlasting peripheral responding. Together, experience-based changes in the sensitivity of visual neurons show response patterns consistent with memory consolidation and spontaneous recovery, the hallmarks of long-term neural plasticity.

Posttraumatic stress disorder (PTSD) may develop when mechanisms for making accurate distinctions about threat relevance have gone awry. Generalization across conceptually related objects has been hypothesized based on clinical observation in PTSD, but the neural mechanisms remain unexplored. Recent trauma-exposed military veterans (n = 46) were grouped into PTSD (n = 23) and non-PTSD (n = 23). Participants learned to generalize fear across conceptual categories (animals or tools) of semantically related items that were partially reinforced by shock during functional magnetic resonance imaging. Conditioned fear learning was quantified by shock expectancy and skin conductance response (SCR). Relative to veteran controls, PTSD subjects exhibited a stronger neural response associated with fear generalization to the reinforced object category in the striatum, anterior cingulate cortex, amygdala, occipitotemporal cortex, and insula (Z > 2.3; p < 0.05; whole-brain corrected). Based on SCR, both groups generalized the shock contingency to the reinforced conceptual category, but learning was not significantly different between groups. We found that PTSD was associated with an enhanced neural response in fronto-limbic, midline, and occipitotemporal regions to a learned representation of threat that is based on previously established conceptual knowledge of the relationship between basic-level exemplars within a semantic category. Behaviorally, veterans with PTSD were somewhat slower to differentiate threat and safety categories as compared with trauma-exposed veteran controls owing in part to an initial overgeneralized behavioral response to the safe category. These results have implications for understanding how fear spreads across semantically related concepts in PTSD.

The catastrophic outbreak of Severe Acute Respiratory Syndrome - Coronavirus (SARS-CoV-2) also known as COVID-2019 has brought the worldwide threat to the living society. The whole world is putting incredible efforts to fight against the spread of this deadly disease in terms of infrastructure, finance, data sources, protective gears, life-risk treatments and several other resources. The artificial intelligence researchers are focusing their expertise knowledge to develop mathematical models for analyzing this epidemic situation using nationwide shared data. To contribute towards the well-being of living society, this article proposes to utilize the machine learning and deep learning models with the aim for understanding its everyday exponential behaviour along with the prediction of future reachability of the COVID-2019 across the nations by utilizing the real-time information from the Johns Hopkins dashboard.

The SARS-CoV-2 virus, emerged from Wuhan, China is spreading all over the world in an unprecedented manner, causing millions of infections and thousands of deaths. However, the spread of the disease across countries and regions are not even. Why some countries and regions are more affected than some other countries and regions? We employ simple statistical methods to investigate any linkage between the severity of the disease and the environmental, economic and social factors of countries. The estimation results indicate that the number of confirmed cases of Coronavirus infection is higher in countries with lower yearly average temperatures, higher economic openness, and stronger political democracy. However, findings of this analysis should be interpreted carefully keeping in mind the fact that statistical relations do not necessarily imply causation. Only clinical experiments with medical expertise can confirm how the virus behaves in the environment.

Within psychology, the term habit refers to a process whereby contexts prompt action automatically, through activation of mental context–action associations learned through prior performances. Habitual behavior is regulated by an impulsive process, and so can be elicited with minimal cognitive effort, awareness, control, or intention. When an initially goal-directed behavior becomes habitual, action initiation transfers from conscious motivational processes to context-cued impulse-driven mechanisms. Regulation of action becomes detached from motivational or volitional control. Upon encountering the associated context, the urge to enact the habitual behavior is spontaneously triggered and alternative behavioral responses become less cognitively accessible.By virtue of its cue-dependent automatic nature, theory proposes that habit strength will predict the likelihood of enactment of habitual behavior, and that strong habitual tendencies will tend to dominate over motivational tendencies. Support for these effects has been found for many health-related behaviors, such as healthy eating, physical activity, and medication adherence. This has stimulated interest in habit formation as a behavior change mechanism: It has been argued that adding habit formation components into behavior change interventions should shield new behaviors against motivational lapses, making them more sustainable in the long-term. Interventions based on the habit-formation model differ from non-habit-based interventions in that they include elements that promote reliable context-dependent repetition of the target behavior, with the aim of establishing learned context–action associations that manifest in automatically cued behavioral responses. Interventions may also seek to harness these processes to displace an existing “bad” habit with a “good” habit.Research around the application of habit formation to health behavior change interventions is reviewed, drawn from two sources: extant theory and evidence regarding how habit forms, and previous interventions that have used habit formation principles and techniques to change behavior. Behavior change techniques that may facilitate movement through discrete phases in the habit formation trajectory are highlighted, and techniques that have been used in previous interventions are explored based on a habit formation framework. Although these interventions have mostly shown promising effects on behavior, the unique impact on behavior of habit-focused components and the longevity of such effects are not yet known. As an intervention strategy, habit formation has been shown to be acceptable to intervention recipients, who report that through repetition, behaviors gradually become routinized. Whether habit formation interventions truly offer a route to long-lasting behavior change, however, remains unclear.

To investigate the process of habit formation in everyday life, 96 volunteers chose an eating, drinking or activity behaviour to carry out daily in the same context (for example ‘after breakfast’) for 12 weeks. They completed the self‐report habit index (SRHI) each day and recorded whether they carried out the behaviour. The majority (82) of participants provided sufficient data for analysis, and increases in automaticity (calculated with a sub‐set of SRHI items) were examined over the study period. Nonlinear regressions fitted an asymptotic curve to each individual's automaticity scores over the 84 days. The model fitted for 62 individuals, of whom 39 showed a good fit. Performing the behaviour more consistently was associated with better model fit. The time it took participants to reach 95% of their asymptote of automaticity ranged from 18 to 254 days; indicating considerable variation in how long it takes people to reach their limit of automaticity and highlighting that it can take a very long time. Missing one opportunity to perform the behaviour did not materially affect the habit formation process. With repetition of a behaviour in a consistent context, automaticity increases following an asymptotic curve which can be modelled at the individual level.

The UK government is advising people to stay home and only go out if they need to fetch food or medicine, to go to work if it's essential or to exercise.Even when you leave your home, you need to practise social distancing and keep at least 2m (6ft) away from other people to protect yourself from catching coronavirus. But what does it actually look like? The BBC's Laura Foster demonstrates.

The coronavirus outbreak is sweeping the globe with outbreaks reported on every continent except Antarctica as of March 2020. Data scientists are uniquely and diversely skilled in ways that can be highly effective in minimizing, combatting, and recovering from the impacts of the COVID-19 outbreak. In this Opinion, the basics of biodefense as well as specific opportunities for the data science community to contribute are discussed.

The global epidemic pattern has dynamically changed from the first stage of a single epidemic center (China) in January and February to the second stage of multiple epidemic centers (Italy, Iran, and South Korea) in March. Towardsthe end of Marchhowever, the world beganexperiencing anextremely increasing number ofcases with an estimated 50,000 cases confirmed globally per day.Tocombat thispandemic, different strategies needto be tailored and implementedin countries with different situations.

Hell has frozen over. The world is in the grip of a pandemic that has closed down society, shuttered your lab, and threatens to cause millions of deaths and untold economic misery. You’re confined to your apartment, labs that have been converted into testing sites have all the volunteers they need, alcohol supplies have dwindled, and you’re discovering just how desperately you love experimental science. If someone lined up every complaint you’d ever made about boring techniques, failed experiments, and your idiot advisor and wrote each one on a large, separate piece of paper, you’d happily eat them all if it would let you back into lab to do your now beloved experiments and get on with your quest for scientific knowledge. But even this extreme feat of mastication won’t let you back into lab, so what should you do? Learn Python, write a fellowship proposal, read all those papers that you’ve always been meaning to digest? These are good ideas, but I claim to have a better one, which is to become a better experimentalist from the comfort of your very own couch plus everyone’s favorite new medium, Zoom.

In the United States, black people are being admitted to hospital and dying in disproportionate numbers from the covid-19 pandemic. The Trump administration acknowledged the issue after a Washington Post analysis found that black majority counties had three times the coronavirus infection rate and almost six times the death rate of white majority counties.1The excess deaths among African-Americans “are shining a very bright light on some of the real weaknesses and foibles in our society,” said Anthony Fauci, director of the National Institute of Allergy and Infectious Diseases, adding that at least part of the problem was due to a higher burden of underlying medical conditions such as diabetes, hypertension, obesity, and asthma among African-Americans. “There’s nothing we can do about it right now except to try and give them the best possible care to avoid complications,” he said.The true scale of the disparity is unknown because so few states and counties include racial data in their reporting. Hundreds of doctors joined Democratic lawmakers this week in calling for more detailed information.

It’s not just the Nightingale hospitals—clinically led reorganisation is transforming how trusts are working, finds Jacqui ThorntonAs the NHS Nightingale hospitals attract widespread publicity,1 clinically led innovation is quietly—and quickly—transforming practice in acute trusts to cope with covid-19.Across the UK, the pace of change has been “breathtaking,” says Keith Girling, medical director at Nottingham University Hospitals NHS Trust. It’s not just the huge increase in intensive care capacity, there is also the reconfiguration of wards to accommodate more patients and redeployment of staff within those areas.Medical teams are working in completely different ways, with consultant led and delivered care provided around the clock; rotas have been rewritten wholesale; and areas of trusts that are quieter, such as clinical genetics and genitourinary medicine, are lending trainees and equipment to be used in imaginative ways, with consultants picking up the baseline.At the same time, IT proposals that before the outbreak were expected to take months have been accelerated and have come to fruition in days, and new clinical pathways have developed at record speed. This has happened at district general and large teaching hospitals alike. And, crucially, says David Oliver, consultant in geriatrics and acute general medicine at the Royal Berkshire NHS Foundation Trust and a columnist for The BMJ, much of this work was going on well before guidance from central bodies.

Like many countries, India has entered a nationwide shut-in to protect its 1.3 billion citizens from coronavirus. But communications blackouts in the conflicted region of Jammu and Kashmir make lockdown doubly frightening, confusing, and dangerous, writes Puja Changoiwala

At a sober press briefing in the White House last week, members of President Trump's coronavirus task force unveiled data supporting the need to continue the national effort to limit the spread of the virus.Even while maintaining policies aimed at limiting person-to-person contact, the administration projected between 100,000 and 240,000 Americans would die of covid-19, the disease caused by the virus. One slide, using data from the Institute for Health Metrics and Evaluation at the University of Washington, showed a predicted peak in the daily death toll from the disease arriving in the middle of April.Anthony S. Fauci, a key member of the task force, made an important point about those projections the day before.AD“Models are as good as the assumptions you put into them, and as we get more data, then you put it in and that might change,” he said. That point was soon reinforced, with the IHME estimate shifting upward soon after the White House briefing.Late Tuesday night, however, the IHME estimate shifted in the other direction. While the model last week projected nearly 94,000 deaths by late summer, its new estimate puts the toll by August at 60,400 — a decline of 26 percent from the model’s previous estimate.

The COVID-19 crisis has brought unprecedented challenges for both people and society. We’re also seeing the power of human creativity and collaboration. One example is the Coronavirus Tech Handbook, the world's largest crowdsourced resource library of tech, tools and data relating to COVID-19 and supported by new grant funding from Nesta.

This is an exercice extracted from a paper in French (translated in English by Stephen Muecke) in AOC-Media A little exercise to make sure things don’t restart after the lock out just as they were before* If you wish to share your auto description: here is a platform: Proposed by @BrunoLatourAIME following arguments proposed in Down to Earth Politics in the New Climatic Regime (Polity, 2018). Let us take advantage of the forced suspension of most activities to take stock of those we would like to see discontinued and those, on the contrary, that we would like to see developed. I suggest that readers try to answer this short questionnaire for themselves. It will be especially useful as it will be based on a personal experience that has been directly lived. This exercise is not a question of expressing an opinion but of describing your situation and may be investigating. It is only later, if one were to give oneself the means of compiling the answers of many respondents and then composing the landscape created by their intersections, that one could find a form of political expression - but this time embodied and situated in a concrete world.

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has placed unprecedented strain on health-care services worldwide, leading to more than 100 000 deaths worldwide, as of April 15, 2020.1WHOCoronavirus disease (COVID-19) situation report – 84.https://www.who.int/emergencies/diseases/novel-coronavirus-2019Date: April 13, 2020Date accessed: April 14, 2020Google ScholarMost testing for SARS-CoV-2 aims to identify current infection by molecular detection of the SARS-CoV-2 antigen; this involves a RT-PCR of viral RNA in fluid, typically obtained from the nasopharynx or oropharynx.2Beeching NJ Fletcher TE Beadsworth MBJ Covid-19: testing times.BMJ. 2020; 369m1403Google ScholarThe global approach to SARS-CoV-2 testing has been non-uniform. In South Korea, testing has been extensive, with emphasis on identifying individuals with respiratory illness, and tracing and testing any contacts. Other countries (eg, Spain) initially limited testing to individuals with severe symptoms or those at high risk of developing them.Here we outline the case for mass testing of both symptomatic and asymptomatic health-care workers (HCWs) to: (1) mitigate workforce depletion by unnecessary quarantine; (2) reduce spread in atypical, mild, or asymptomatic cases; and (3) protect the health-care workforce.

This short discussion paper sets out a framework for new operating models to guide work and thinking about the changing role and function of local government.

BackgroundIn the face of rapidly changing data, a range of case fatality ratio estimates for coronavirus disease 2019 (COVID-19) have been produced that differ substantially in magnitude. We aimed to provide robust estimates, accounting for censoring and ascertainment biases.MethodsWe collected individual-case data for patients who died from COVID-19 in Hubei, mainland China (reported by national and provincial health commissions to Feb 8, 2020), and for cases outside of mainland China (from government or ministry of health websites and media reports for 37 countries, as well as Hong Kong and Macau, until Feb 25, 2020). These individual-case data were used to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the case fatality ratio by relating the aggregate distribution of cases to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for demography and age-based and location-based under-ascertainment. We also estimated the case fatality ratio from individual line-list data on 1334 cases identified outside of mainland China. Using data on the prevalence of PCR-confirmed cases in international residents repatriated from China, we obtained age-stratified estimates of the infection fatality ratio. Furthermore, data on age-stratified severity in a subset of 3665 cases from China were used to estimate the proportion of infected individuals who are likely to require hospitalisation.FindingsUsing data on 24 deaths that occurred in mainland China and 165 recoveries outside of China, we estimated the mean duration from onset of symptoms to death to be 17·8 days (95% credible interval [CrI] 16·9–19·2) and to hospital discharge to be 24·7 days (22·9–28·1). In all laboratory confirmed and clinically diagnosed cases from mainland China (n=70 117), we estimated a crude case fatality ratio (adjusted for censoring) of 3·67% (95% CrI 3·56–3·80). However, after further adjusting for demography and under-ascertainment, we obtained a best estimate of the case fatality ratio in China of 1·38% (1·23–1·53), with substantially higher ratios in older age groups (0·32% [0·27–0·38] in those aged <60 years vs 6·4% [5·7–7·2] in those aged ≥60 years), up to 13·4% (11·2–15·9) in those aged 80 years or older. Estimates of case fatality ratio from international cases stratified by age were consistent with those from China (parametric estimate 1·4% [0·4–3·5] in those aged <60 years [n=360] and 4·5% [1·8–11·1] in those aged ≥60 years [n=151]). Our estimated overall infection fatality ratio for China was 0·66% (0·39–1·33), with an increasing profile with age. Similarly, estimates of the proportion of infected individuals likely to be hospitalised increased with age up to a maximum of 18·4% (11·0–37·6) in those aged 80 years or older.InterpretationThese early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and show a strong age gradient in risk of death.

Governments are drawing on ‘what-if’ models to inform policy decisions – such as when/whether to use suppression or mitigation, recommend social distancing, close schools, enforce lock-down, testing regimes etc. As non-experts we would like to know more about the assumptions that go into these what-if models, and how the government use the expert advice based on these models to make decisionsSome questions (by no means exhaustive) … How do the models factor in:· Uncertainty in assumptions/parameters/ reliability of data and testing etc …· Outside information - eg about what’s happening in other countries (China/Italy etc), which have similarities/differences· Unknowns – such as unanticipated events or developments (eg new breathing aids, make-shift hospitals etc ) ..we would expect some new developments, even if one can't specify which.· People’s behaviour in reaction to the measures - notions of fatigue etc .. take-up of advice/messages etc … how are these included?Retrospective judgmentsI'm also wondering how these models might be used once the crisis runs its course, and we seek to attribute responsibility and blame (and learn for the future) -For causal questions, it seems we should include causal factors that happen through the course of the crisis, including events unanticipated at time of decisions, such as the design of new breathing aids, building new hospitals etc. We want to know which things made a difference to what actually happened.But for questions of blame we perhaps should not include factors that were not known by the decision makers, and need to focus on what the decision makers should reasonably have known at the time …which seems very hard to assess and model … How are these issues to be dealt with?

The pandemic will change the world permanently and profoundly. Even if countries can control the spread of COVID-19 in the coming months, there will be vast political, economic, social, technological, legal and environmental consequences which will last many decades.In this article, we summarise and synthesise various - often opposing - views about how the world might change. Clearly, these are speculative; no-one knows what the future will look like. But we do know that crises invariably prompt deep and unexpected shifts, so that those anticipating a return to pre-pandemic normality may be shocked to find that many of the previous systems, structures, norms and jobs have disappeared and will not return.

In this report, we use survey data collected in late March and early April 2020 to document and understand how people in six countries (Argentina, Germany, South Korea, Spain, the UK, and the US) accessed news and information about COVID-19 in the early stages of the global pandemic, how they rate the trustworthiness of the different sources and platforms they rely on, how much misinformation they say they encounter, and their knowledge of and responses to the coronavirus crisis.

As a response to the Covid-19 outbreak, Ofcom is providing a range of information about how people are getting news and information about the crisis.We have commissioned a weekly online survey of around 2,000 people over the next three months, and also provide key findings from other datasets such as BARB and comScore.We are publishing this under our media literacy duties, as part of our Making Sense of Media programme. This work furthers our understanding around the access, consumption and critical engagement with news at this time, recognising that habits may intensify or change given the nature of the crisis. For pre-Covid-19 news consumption and attitudes, please see our News Consumption Survey.Given the increased concern about misinformation during this time, we are also providing information about fact-checking and debunking sites and tools.

I am very interested in looking at resilience through Covid - how it impacts safe and healthy behaviours, and whether it will develop of this 'acute stressor'.However there are a huge stream of Covid-related projects going through ethics approval already, which will be competing with each other for participants. Before creating another, I wanted to know:Is anyone currently looking at this, or willing to include it in a study?If not, are there other questions which are not currently being asked, but could be included on such an application?

The coronavirus pandemic has changed the use and communication of evidence, says Jonathan Breckon We are still in the midst of a terrifying upward curve of Covid-19 cases in the UK. History may show that countries such as the UK, United States and Sweden didn’t move fast enough. And there is so little understanding about the virus that the epidemiologist John Ioannidis wrote that it “might be a one-in-a-century evidence fiasco”.  But that sort of post-mortem is far off at the moment. Events are moving too fast to stop for an evaluation of progress. However, there are grounds for optimism around the use and communication of evidence during this pandemic, particularly around social science research and expertise. We need to search for the positives at times like these. So here are seven welcome emerging trends on the use and communication of evidence.

The European Commission, led by the European Innovation Council and in close collaboration with the EU member states, will host a pan-European hackathon to connect civil society, innovators, partners and investors across Europe in order to develop innovative solutions for coronavirus-related challenges.

Businesses to help boost the UK’s resilience to the long-term impact of coronavirus and similar future situations as a result of £20 million government funding.

Today, we're proud to share how we've re-invented our programmes for these extraordinary times – always maintaining connection, community and our values (kindness, community, trust, bravery, learning) at their heart.

One striking feature of the current COVID-19 crisis is the crucial role that local communities have played in responding quickly to local needs and mobilising much-needed resources.Identifying which local causes need support and getting funds to them quickly is a common challenge.This blog gives a short introduction to how crowdfunding can be a good way of raising funds in a crisis by connecting people to local worthy causes, fast.

Nesta is leading a $10m funding round for BibliU, which works with 40% of UK universities, Sky News understands.

As the coronavirus disease 2019 (COVID-19) pandemic progresses, one debate relates to the use of face masks by individuals in the community. We previously highlighted some inconsistency in WHO's initial January, 2020, guidance on this issue.1WHOAdvice on the use of masks in the community, during home care and in healthcare settings in the context of the novel coronavirus (2019-nCoV) outbreak: interim guidance.https://apps.who.int/iris/handle/10665/330987Date: Jan 29, 2020Date accessed: April 15, 2020Google Scholar,  2Chan AL-y Leung CC Lam TH Cheng KK To wear or not to wear: WHO's confusing guidance on masks in the covid-19 pandemic.BMJ Blog. March 11, 2020; https://blogs.bmj.com/bmj/2020/03/11/whos-confusing-guidance-masks-covid-19-epidemic/Date accessed: April 15, 2020Google Scholar WHO had not yet recommended mass use of masks for healthy individuals in the community (mass masking) as a way to prevent infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in its interim guidance of April 6, 2020.3WHOAdvice on the use of masks in the context of COVID-19: interim guidance.https://www.who.int/publications-detail/advice-on-the-use-of-masks-in-the-community-during-home-care-and-in-healthcare-settings-in-the-context-of-the-novel-coronavirus-(2019-ncov)-outbreakDate: April 6, 2020Date accessed: April 15, 2020Google Scholar Public Health England (PHE) has made a similar recommendation.4Public Health EnglandCoronavirus (COVID-19)—what you need to know.https://publichealthmatters.blog.gov.uk/2020/01/23/wuhan-novel-coronavirus-what-you-need-to-know/Date: Jan 23, 2020Date accessed: April 15, 2020Google Scholar By contrast, the US Centers for Disease Control and Prevention (CDC) now advises the wearing of cloth masks in public5US Centers for Disease Control and PreventionRecommendation regarding the use of cloth face coverings, especially in areas of significant community-based transmission.https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/cloth-face-cover.htmlDate: April 3, 2020Date accessed: April 15, 2020Google Scholar and many countries, such as Canada, South Korea, and the Czech Republic, require or advise their citizens to wear masks in public places.6Lee HK South Korea takes new measures to have enough face masks domestically amid coronavirus. ABC News.https://abcnews.go.com/International/south-korea-takes-measures-face-masks-domestically-amid/story?id=69254114Date: April 27, 2020Date accessed: April 15, 2020Google Scholar,  7Government of CanadaConsiderations in the use of homemade masks to protect against COVID-19. Notice to general public and healthcare professionals.https://www.canada.ca/en/health-canada/services/drugs-health-products/medical-devices/activities/announcements/covid19-notice-home-made-masks.htmlDate: 2020Date accessed: April 15, 2020Google Scholar,  8Government of the Czech RepublicThe government requires the wearing of protective equipment and reserved time for pensioners to do their food shopping.https://www.vlada.cz/en/media-centrum/aktualne/the-government-has-decided-to-require-the-wearing-of-protective-equipment-and-reserved-time-for-senior-citizens-to-do-their-food-shopping-180465/Date: March 18, 2020Date accessed: April 15, 2020Google Scholar An evidence review9Howard J Huang A Li Z Tufekci Z et al.Face masks against COVID-19: an evidence review.Preprints. 2020; (published online April 12.) (preprint).DOI: 10.20944/preprints202004.0203.v1Google Scholar and analysis10Greenhalgh T Schmid MB Czypionka T Bassler D Gruer L Face masks for the public during the covid-19 crisis.BMJ. 2020; 369m1435Google Scholar have supported mass masking in this pandemic. There are suggestions that WHO and PHE are revisiting the question.11Giordano C Coronavirus: wearing face masks in public will likely become new norm, says WHO expert.The Independent. April 13, 2020; https://www.independent.co.uk/news/health/coronavirus-face-masks-who-health-advice-covid-19-expert-a9462391.htmlDate accessed: April 15, 2020Google Scholar,  12