Stream Graph

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

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

Evaluating the fake news problem at the scale of the information ecosystem

“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.

IBM Offers "Watson Assistant for Citizens" to Provide Responses to COVID-19 Questions

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

Helping the public to understand the effectiveness of face masks to reduce the spread of SARS-CoV-2 and to use them effectively

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.

Physical interventions to interrupt or reduce the spread of respiratory viruses. Part 1 - Face masks, eye protection and person distancing: systematic review and meta-analysis

On the Statistical Differences between BinaryForecasts and Real World Payoffs

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).

1907.11162v3

How should the Nuffield Foundation research community respond to the social implications of the coronavirus (COVID-19) pandemic?

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.

Government of Canada funds 49 additional COVID-19 research projects – Details of the funded projects

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).

Mapping county-level mobility pattern changes in the United States in response to COVID-19

2004.04544

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.

What role does each of us play in protecting our community ?

Distributed peer review enhanced with natural language processing and machine learning

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).

machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models

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.

2004.04019

Covid-19 UK Mobility Project First report (8th April 2020): analysis of human mobility in the UK during the COVID-19 pandemic

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.

4 In 10 U.S. Teens Say They Haven't Done Online Learning Since Schools Closed

'The world is going to hell': Coronavirus can be deadly for people recovering from addiction

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.

Psychology of COVID-19 Preprint Tracker

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.

Learning from COVID-19: A catalyst for European R&D policy and practice?

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

The Viral ‘Study’ About Runners Spreading Coronavirus Is Not Actually a Study

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.

Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal

Apple, Google Bring Covid-19 Contact-Tracing to 3 Billion People

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.

Working from home? Here's how to set boundaries for a better work-life balance

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

Network Epidemiology Online Workshop Series

Call for Papers:Commentaries on the Coronavirus Pandemic

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.

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

Why it’s so hard to see into the future of Covid-19

Net-COVID Session1A: Network Epidemiology Tutorial by Laurent Hebert-Dufresne

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.

COVID19 Mobility Reports

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.

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.

Virtual health care in the era of COVID-19

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.

Centring sexual and reproductive health and justice in the global COVID-19 response

“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.

Offline: COVID-19—bewilderment and candour

The gendered dimensions of COVID-19

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.

COVID-19 and risks to the supply and quality of tests, drugs, and vaccines

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.

Softening the blow of the pandemic: will the International Monetary Fund and World Bank make things worse?

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.

Dynamic Causal Modelling of COVID-19

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.

Beware of the second wave of COVID-19

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.

Pandemic school closures: risks and opportunities

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.

Alcohol use and misuse during the COVID-19 pandemic: a potential public health crisis?

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.

Respiratory virus shedding in exhaled breath and efficacy of face masks

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.

If the world fails to protect the economy, COVID-19 will damage health not just now but also in the future

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.

COVID-19: living map of the evidence

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.

Initiatives from the CERN community in global fight against COVID-19

The Coronavirus and Climate Change: How We’re Making the Same Mistakes

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.

How the coronavirus lockdown is hitting Mexico's drug cartels

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.

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.

Policy for the COVID-19 Crisis