In this essay we make four interrelated points. First, we reiterate previous arguments (Kleinberg et al 2015) that forecasting problems are more common in social science than is often appreciated. From this observation it follows that social scientists should care about predictive accuracy in addition to unbiased or consistent estimation of causal relationships. Second, we argue that social scientists should be interested in prediction even if they have no interest in forecasting per se. Whether they do so explicitly or not, that is, causal claims necessarily make predictions; thus it is both fair and arguably useful to hold them accountable for the accuracy of the predictions they make. Third, we argue that prediction, used in either of the above two senses, is a useful metric for quantifying progress. Important differences between social science explanations and machine learning algorithms notwithstanding, social scientists can still learn from approaches like the Common Task Framework (CTF) which have successfully driven progress in certain fields of AI over the past 30 years (Donoho, 2015). Finally, we anticipate that as the predictive performance of forecasting models and explanations alike receives more attention, it will become clear that it is subject to some upper limit which lies well below deterministic accuracy for many applications of interest (Martin et al 2016). Characterizing the properties of complex social systems that lead to higher or lower predictive limits therefore poses an interesting challenge for computational social science.

To study the spatiotemporal COVID-19 spread, we use the Global Epidemic and Mobility Model (GLEAM), an individual-based, stochastic, and spatial epidemic model [1, 2, 3, 4]. GLEAM uses real-world data to perform in-silico simulations of the spatial spread of infectious diseases at the global level.  We use the model to analyze the spatiotemporal spread and magnitude of the COVID-19 epidemic in the continental US. The model generates an ensemble of possible epidemic projections described by the number of newly generated infections, times of disease arrival in different regions, and the number of traveling infection carriers. Approximate Bayesian Computation is used to estimate the posterior distribution of the basic parameters of the model. The calibration of the global model for COVID-19 is reported in Science. The US model considers the timeline of mitigation interventions that are integrated as detailed in the model description published here. The projections will be regularly updated as new data and information about mitigation policies become available. Sensitivity analysis on the basic parameters is routinely performed along with the baseline projections considered. In order to calculate the number of deaths the model uses estimates of COVID-19 severity from available data [1, 2].

Modeling COVID-19 in the united states

openVirus aggregation of scholarly publications and extracted knowledge on viruses and epidemics.

The world faces (and will continue to face) viral epdemics which arise suddenly and where scientific/medical knowledge is a critical resource. Despite over 100 Billion USD on medical research worldwide much knowledge is behind publisher paywalls and only available to rich universities. Moreover it is usually badly published, dispersed without coherent knowledge tools. It particularly disadvantages the Global South. This project aims to use modern tools, especially Wikidata (and Wikpedia), R, Java, textmining, with semantic tools to create a modern integrated resource of all current published information on viruses and their epidemics. It relies on collaboration and gifts of labour and knowledge.

Estimating the onset of localtransmission of the COVID-19epidemicin African countries (Report V1.0)

We use a global metapopulation transmission model to project the likelihood of observing transmission of COVID-19 in African countries. The model is calibrated on international infectionimportation through January 23, 2020, and includes modeling travel restrictions within China and to and from international destinations. Here, we project the probability for the number of cumulative cases in African countries through March, 2020.

They can also reveal symptoms that at first went undetected. I may have found a new one.

Google Searches Can Help Us Find Emerging Covid-19 Outbreaks

The stock market provides a view of what investors expect for the future. It is precisely in complex situations such as the COVID-19 outbreak that the prescience of the market is particularly valuable, argues Alexander F. Wagner.

What the stock market tells us about the post-COVID-19 world

Lessons Learned From Korea: Covid-19 Pandemic

A global needs assessment in times of a global crisis: world psychiatry response to the COVID-19 pandemic

The twitter pandemic: The critical role of twitter in the dissemination of medical information and misinformation during the COVID-19 Pandemic

Coronavirus-infected international business transactions: a preliminary diagnosis

This article examines the effects on international business transactions of the pandemic-mitigation restrictions (PMRs) enacted in various countries in response to covid-19. Through nationwide lockdowns and trade limitations, PMRs impact international contracts significantly, causing many parties to fail to perform and claim force majeure. In a threefold analysis, this article offers an overview of force majeure clauses, an assessment of their role in relation to PMRs and an evaluation of the latter’s extraterritorial reach. Finally, it calls for the coordination of force majeure-related PMRs at the supranational level to prevent the excessive fragmentation of the legal regimes generated by the pandemic.

COVID-19: TESTING THE LIMITS OF HUMAN RIGHTS

Throughout time, operational laws and concepts from complex systems have been employed to quantitatively model important aspects and interactions in nature and society. Nevertheless, it remains enigmatic and challenging, yet inspiring, to predict the actual interdependencies that comprise the structure of such systems, particularly when the causal interactions observed in real-world phenomena might be persistently hidden. In this article, we propose a robust methodology for detecting the latent and elusive structure of dynamic complex systems. Our treatment utilizes short-term predictions from information embedded in reconstructed state space. In this regard, using a broad class of real-world applications from ecology, neurology, and finance, we explore and are able to demonstrate our method’s power and accuracy to reconstruct the fundamental structure of these complex systems, and simultaneously highlight their most fundamental operations.

Unveiling causal interactions in complex systems

Goal and methodology of this study

This brief anonymous questionnaire from the EFPA Project Group on eHealth takes about 5 minutes to complete and focuses on the current use of online consultation technology by psychologists and other mental healthcare professionals in the context of the COVID-19 Coronavirus pandemic.

Meeting global demand for growing the science, technology, engineering, and mathematics (STEM) workforce requires solutions for the shortage of qualified instructors. We propose and evaluate a model for scaling up affordable access to effective STEM education through national online education platforms. These platforms allow resource-constrained higher education institutions to adopt online courses produced by the country’s top universities and departments. A multisite randomized controlled trial tested this model with fully online and blended instruction modalities in Russia’s online education platform. We find that online and blended instruction produce similar student learning outcomes as traditional in-person instruction at substantially lower costs. Adopting this model at scale reduces faculty compensation costs that can fund increases in STEM enrollment.

Online education platforms scale college STEM instruction with equivalent learning outcomes at lower cost

Fast Funding for COVID-19 Science

Science funding mechanisms are too slow in normal times and may be much too slow during the COVID-19 pandemic. Fast Grants are an effort to correct this.If you are a scientist at an academic institution currently working on a COVID-19 related project and in need of funding, we invite you to apply for a Fast Grant. Fast Grants are $10k to $500k and decisions are made in under 48 hours. If we approve the grant, you'll receive payment as quickly as your university can receive it.

First-wave COVID-19 transmissibility and severity in China outside Hubei after control measures, and second-wave scenario planning: a modelling impact assessment

BackgroundAs of March 18, 2020, 13 415 confirmed cases and 120 deaths related to coronavirus disease 2019 (COVID-19) in mainland China, outside Hubei province—the epicentre of the outbreak—had been reported. Since late January, massive public health interventions have been implemented nationwide to contain the outbreak. We provide an impact assessment of the transmissibility and severity of COVID-19 during the first wave in mainland Chinese locations outside Hubei.MethodsWe estimated the instantaneous reproduction number (Rt) of COVID-19 in Beijing, Shanghai, Shenzhen, Wenzhou, and the ten Chinese provinces that had the highest number of confirmed COVID-19 cases; and the confirmed case-fatality risk (cCFR) in Beijing, Shanghai, Shenzhen, and Wenzhou, and all 31 Chinese provinces. We used a susceptible–infectious–recovered model to show the potential effects of relaxing containment measures after the first wave of infection, in anticipation of a possible second wave.FindingsIn all selected cities and provinces, the Rt decreased substantially since Jan 23, when control measures were implemented, and have since remained below 1. The cCFR outside Hubei was 0·98% (95% CI 0·82–1·16), which was almost five times lower than that in Hubei (5·91%, 5·73–6·09). Relaxing the interventions (resulting in Rt >1) when the epidemic size was still small would increase the cumulative case count exponentially as a function of relaxation duration, even if aggressive interventions could subsequently push disease prevalence back to the baseline level.InterpretationThe first wave of COVID-19 outside of Hubei has abated because of aggressive non-pharmaceutical interventions. However, given the substantial risk of viral reintroduction, particularly from overseas importation, close monitoring of Rt and cCFR is needed to inform strategies against a potential second wave to achieve an optimal balance between health and economic protection.FundingHealth and Medical Research Fund, Hong Kong, China.

Our society is built on a complex web of interdependencies whose effects become manifest during extraordinary events such as the COVID-19 pandemic, with shocks in one system propagating to the others to an exceptional extent. We analyzed more than 100 millions Twitter messages posted worldwide in 64 languages during the epidemic emergency due to SARS-CoV-2 and classified the reliability of news diffused. We found that waves of unreliable and low-quality information anticipate the epidemic ones, exposing entire countries to irrational social behavior and serious threats for public health. When the epidemics hit the same area, reliable information is quickly inoculated, like antibodies, and the system shifts focus towards certified informational sources. Contrary to mainstream beliefs, we show that human response to falsehood exhibits early-warning signals that might be mitigated with adequate communication strategies.

Assessing the risks of "infodemics" in response to COVID-19 epidemics

2004.03997v1

The Digital Identification in Finance Initiative in Africa (DigiFI) aims to generate rigorous evidence on how African governments, private companies, and NGOs can leverage digital payments and identification systems to improve lives through better public service delivery, governance, and financial inclusion. To achieve this vision, DigiFI plans to support governments and other implementers to monitor and evaluate relevant reforms.

Digital Identification in Finance Initiative in Africa

What are COVID-19 Models Modeling?

Two of the most prominently cited figures from US-focused COVID-19 models include estimated deaths between 100k and 2.2 million. How can the public make sense of differences that are more than an order of magnitude apart? It could lead many to throw up their arms, not seeing why we should trust any models. My hope is to convince you otherwise. Models can be useful tools for those of us who are interested in knowing where the pandemic is heading, but doing so requires building some common understanding of why various models are built, the diverse strategies available for accomplishing those aims, and the substantial uncertainty inherent in all models. Without this understanding, we are attempting to compare metaphorical apples and oranges — if not apples and dump trucks — and poorly equipped to meaningfully assess those differences. Here, I hope to help with that a little, by differentiating between: What models aim to accomplish. The mechanics of how they do that. The importance of uncertainty in all modeling approaches.

Co-creation Hub (CcHUB): Funding and Design Support for COVID-19 Projects In light of the Coronavirus pandemic and its related consequences in other sectors of various economies across the globe, Co-creation Hub (cchubnigeria.com) is looking to fund and provide research and design support, via our Design Lab, for COVID-19 related projects. These include, but not limited to, projects in the following areas: -> Last mile communication: educating the public and ensuring the right information reaches even remote locations. -> Support for the infected and the most vulnerable in society. -> Local production of essential medical supplies. -> Support for our food value chain, from producers to consumers, in the event of movement restrictions.If you are already working on a project or have an idea, with the capability to build out the solution with available resources from CcHUB, please fill out this form and we will be in touch.Projects can be focused on a particular African country, the African continent or the world at large.

[Call for Projects] - Solve for Coronavirus (COVID-19)

Business-led innovation in response to global disruption (de minimis)

Innovate UK, as part of UK Research and Innovation, will invest up to £20 million in innovation projects. The aim of this competition is to support UK businesses to focus on emerging or increasing needs of society and industries during and following the Covid-19 pandemic. By fast-tracking innovation, the UK will be better placed to maintain employment levels, a competitive position in global markets and make the UK more resilient to similar disruption. Your application must demonstrate both realistic and significant benefits for society (including communities, families and individuals) or an industry that has been severely impacted and/or permanently disrupted by the Covid-19 pandemic. Your proposal must focus on a clear need and the proposed innovation to address it. You must have the ability to deliver the project during the working restrictions of Covid-19 pandemic. You can claim 100% of your project costs up to the maximum of £50,000. These will be paid in advance of the project start date. We intend to monitor and review projects during implementation with a view to providing follow-on funding and support for those with the most potential for impact. The competition closes at midday 12pm UK time on the deadline stated.

Psychologists offer advice on ethical practice, research, teaching and applied work as professionals across the field adjust to a sudden shift in working conditions.

Ethical guidance for the COVID-19 era

Competitive Research Program

Tips to manage loneliness, anxiety while social distancing during coronavirus

It was only a few months ago that we held our loved ones close and exchanged warm handshakes with new friends, rarely if ever weighing the life-or-death calculus of opening a grocery store fridge.The coronavirus pandemic has been a fast and deep tectonic shift for human life, and if history is any guide, people can be incredibly adaptable. But as the weeks build, the news turns more grim, and social distancing becomes social isolation, the familiar contours of everyday life are fading.Many have a simmering unease that they are unprepared for the long haul — or may not have realized the struggle has arrived.We talked with mental health professionals about how you can take an honest look at yourself and determine what type of help you might need.

Is social isolation getting to you? Here’s how to know — and what experts say to do.

Measuring the predictability of life outcomes with a scientific mass collaboration

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.

Second Issue - Covid Economics: Vetted and Real-Time Papers

This is 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.

We all want to know how the coronavirus pandemic ends. How do we cope with uncertainty?

No one knows how this ends. The uncertainty may be as unsettling as the virus itself. 

Because disease and disability impair normal functioning, they limit the range of opportunities people would otherwise have, given their talents and skills. Therefore, health—or normal functioning—is of special moral importance from the perspective of social justice because it makes a limited but significant contribution to the range of opportunities open to people (Daniels, 1985, 2008). If, as many believe, society has an obligation to promote equality of opportunity, then it has a social obligation to promote population health and to distribute that health equitably. Meeting this obligation requires a just distribution of all the determinants of health, including public health services and personal medical services (Daniels, Kennedy, & Kawachi, 1999, 2000). Many health needs arise regularly and predictably and must be planned for accordingly. Other population health needs arise less predictably as a result of natural and manmade emergencies. The obligation to protect population health and distribute it fairly should accordingly also govern the approach to emergency preparedness, including the need for stockpiling medical supplies and treatments for a range of health emergencies. These obligations apply in regional emergencies following natural disasters or more localized forms of chemical and radiologic terrorism; they also apply in national or international health emergencies (p. 105) that might follow natural pandemics or some forms of biological terrorism. What principles, considerations, or procedures should play a role in planning for population health emergencies, including developing stockpiles of medical resources? The traditional emphasis in public health planning is on health maximization strategies—either minimizing the aggregate impact of certain health problems (e.g., lowering mortality rates, maternal mortality rates, or infant mortality rates) or maximizing certain measures of health benefits (e.g., life expectancy to life years or quality-adjusted life years [QALYs] gained or disability adjusted life years [DALYs] saved). More recently, however, the public health movement has paid more attention to promoting equity in health, both at national and international levels (World Health Organization Europe, 2002; World Health Organization Regional Office of Europe, 1999). In a simple maximizing principle or strategy, distributive effects can be comfortably ignored, and public health officials may have a clearer sense about their obligations, although they still face problems arising from uncertainty. When they realize that obligations of justice involve the pursuit of both equity and population health improvement, however, clarity and agreement on goals are harder to reach. People will disagree about how to resolve conflicts among these objectives. This perplexing problem arises in emergency planning as well as in meeting standard health needs. In both contexts, an appeal to principles must be supplemented with reliance on a fair, deliberative process (Daniels & Sabin, 1997). Planning for emergencies is not itself an emergency situation. Planners should not be tempted into thinking that all nuance or subtlety about ethical obligations can be finessed because they are considering a crisis situation. Proper planning ensures that due care is taken regarding how to meet moral obligations in actual emergencies. In this chapter, I examine more systematically the conflict between maximization and equity, especially as it arises in the context of stockpiling for emergencies. Because the many disagreements that arise about all of these matters are ethical (p. 106) in nature, questions about the legitimacy and fairness of stockpile design and other questions of emergency preparedness will be addressed. To that end, I shall briefly describe the kind of fair, deliberative process that should be used in planning for public health emergencies. The account of fair process provides a framework for thinking about community participation in planning and the proper communication of decisions.

Justice, Resource Allocation, and Emergency Preparedness: Issues Regarding Stockpiling

Psychological Aspects of Major Incidents

Human beings are not only physically vulnerable to adverse events, but psychologically vulnerable too. Every major incident will have mental health consequences for some of those involved. Lives will be disrupted to a greater or lesser extent, for short periods or permanently. People will be killed or injured. Victims will be left to recover, survivors to mourn. It has been estimated that up to 80% of those affected by a major incident or disaster will have short-term mild distress, 20–40% a psychological disorder in the medium term, and up to 5% may be left with a long-term problem. Fortunately, the majority of those affected will recover without professional intervention. In addition, although psychological effects will occur as a natural consequence of a major incident which is a consequence of human error or natural forces, in the case of terrorist incidents, it is the precise aim of the perpetrators to inflict psychological damage. Not only will the mechanism be chosen to inflict wounds associated with maximum horror or to cause anxiety and panic, but an element of deliberate malice is introduced which in itself is likely to increase the adverse psychological consequences. Actions designed to minimize psychological consequences both in rescuers and victims, as well as to identify such problems when they do occur and treat them promptly, are thus a key component of the response to any incident. In addition, means must be in place after any traumatic incident to establish a follow-up registry so that victims with mental health issues can be identified and offered assistance. Conversely, it must not be forgotten that for some, their role in a major incident may actually be life-enhancing, perhaps because the feelings of others towards them became clear, perhaps because they found and demonstrated new strengths or new or previously unknown abilities to cope and to assist others. It is essential that expert mental health advice and mental health services engagement occur at all stages of planning, response, and recovery.

Many physicists want to use their mathematical modelling skills to study the COVID-19 pandemic. Julia Gog, a mathematical epidemiologist, explains some ways to contribute.

How you can help with COVID-19 modelling

I would love to see far more research on disinformation and COVID-19 exploitation on platforms other than Twitter. We know YouTube, Reddit, the alt-tech platforms are hotspots. To my knowledge, we know very little about emerging platforms Tik Tok at all.

Planned study: The world post COVID-19

The best thing you can do to fight COVID-19 is nothing. Stop writing that paper. Don't put it on the arxiv.

#COVIBOOK

Supporting and reassuring children around the world ​

Science-ing from home

In the first of two articles about laboratory closures triggered by COVID-19, scientists affected by the shutdowns outline the tools they are using to run their research groups remotely.

Apply to switch your existing funding to Covid-19 priority areas

UKRI has put in place the following process to allow researchers to repurpose existing UKRI standard grant to address COVID-19 research priorities.  .panel { margin-bottom: 0; } .panel-title a { color: #fff; font-size: 20px; } .panel-title a:hover { color: #fff; text-decoration:underline; }

Factors Associated With Mental Health Outcomes Among Health Care Workers Exposed to Coronavirus Disease 2019

Importance  Health care workers exposed to coronavirus disease 2019 (COVID-19) could be psychologically stressed.Objective  To assess the magnitude of mental health outcomes and associated factors among health care workers treating patients exposed to COVID-19 in China.Design, Settings, and Participants  This cross-sectional, survey-based, region-stratified study collected demographic data and mental health measurements from 1257 health care workers in 34 hospitals from January 29, 2020, to February 3, 2020, in China. Health care workers in hospitals equipped with fever clinics or wards for patients with COVID-19 were eligible.Main Outcomes and Measures  The degree of symptoms of depression, anxiety, insomnia, and distress was assessed by the Chinese versions of the 9-item Patient Health Questionnaire, the 7-item Generalized Anxiety Disorder scale, the 7-item Insomnia Severity Index, and the 22-item Impact of Event Scale–Revised, respectively. Multivariable logistic regression analysis was performed to identify factors associated with mental health outcomes.Results  A total of 1257 of 1830 contacted individuals completed the survey, with a participation rate of 68.7%. A total of 813 (64.7%) were aged 26 to 40 years, and 964 (76.7%) were women. Of all participants, 764 (60.8%) were nurses, and 493 (39.2%) were physicians; 760 (60.5%) worked in hospitals in Wuhan, and 522 (41.5%) were frontline health care workers. A considerable proportion of participants reported symptoms of depression (634 [50.4%]), anxiety (560 [44.6%]), insomnia (427 [34.0%]), and distress (899 [71.5%]). Nurses, women, frontline health care workers, and those working in Wuhan, China, reported more severe degrees of all measurements of mental health symptoms than other health care workers (eg, median [IQR] Patient Health Questionnaire scores among physicians vs nurses: 4.0 [1.0-7.0] vs 5.0 [2.0-8.0]; P = .007; median [interquartile range {IQR}] Generalized Anxiety Disorder scale scores among men vs women: 2.0 [0-6.0] vs 4.0 [1.0-7.0]; P < .001; median [IQR] Insomnia Severity Index scores among frontline vs second-line workers: 6.0 [2.0-11.0] vs 4.0 [1.0-8.0]; P < .001; median [IQR] Impact of Event Scale–Revised scores among those in Wuhan vs those in Hubei outside Wuhan and those outside Hubei: 21.0 [8.5-34.5] vs 18.0 [6.0-28.0] in Hubei outside Wuhan and 15.0 [4.0-26.0] outside Hubei; P < .001). Multivariable logistic regression analysis showed participants from outside Hubei province were associated with lower risk of experiencing symptoms of distress compared with those in Wuhan (odds ratio [OR], 0.62; 95% CI, 0.43-0.88; P = .008). Frontline health care workers engaged in direct diagnosis, treatment, and care of patients with COVID-19 were associated with a higher risk of symptoms of depression (OR, 1.52; 95% CI, 1.11-2.09; P = .01), anxiety (OR, 1.57; 95% CI, 1.22-2.02; P < .001), insomnia (OR, 2.97; 95% CI, 1.92-4.60; P < .001), and distress (OR, 1.60; 95% CI, 1.25-2.04; P < .001).Conclusions and Relevance  In this survey of heath care workers in hospitals equipped with fever clinics or wards for patients with COVID-19 in Wuhan and other regions in China, participants reported experiencing psychological burden, especially nurses, women, those in Wuhan, and frontline health care workers directly engaged in the diagnosis, treatment, and care for patients with COVID-19.

Researchers: show world leaders how to behave in a crisis

Scientists are teaming up to fight COVID-19. Presidents and prime ministers should, too.

Digital Literacy and Online Political Behavior

Digital literacy is receiving increased scholarly attention as a potential explanatory factor in the spread of online misinformation. As a concept, however, it remains surprisingly elusive, with little consensus on definitions or measures. Building on work in communication studies and sociology, we provide a unified framework of digital literacy for political scientists and introduce survey items to measure it. Using a novel purposive sampling approach, we then validate our measure against real-world benchmarks of ground truth. There exists substantial variation in levels of digital literacy in the population, which we also document is correlated with age and could confound observed relationships. However, this is obscured by researchers' reliance on online convenience samples that select for people with computer and internet skills. We discuss the implications of this sample selection bias for effect heterogeneity in studies of online media effects on political behavior.

Speaking of Psychology: Managing Your Mental Health During COVID-19 with Lynn Bufka, PhD

The coronavirus has upended countless lives here at home and around the world. We’ve all become too familiar with new terms — shelter in place, flatten the curve, social distancing. The stock market is bouncing wildly. Just this week we saw the greatest number of unemployment applications in our nation’s history. And there’s still no toilet paper at the grocery store, if you can even go to the grocery store. So are you feeling stressed yet? Anxious? At your wits’ end because you’re trying to telework and the kids are making noise in the next room? And will someone please walk the dog? Joining me today is Lynn Bufka, PhD, Senior Director of Practice, Research and Policy here at APA. Bufka is also a practicing psychologist and has been talking constantly with journalists who are covering the coronavirus pandemic.

A clinical psychologist and behavioral health consultant develop a virtual town hall to address anxiety, depression and other mental health challenges among people being held at a national quarantine center.

Psychologist leads innovative approach to tackle psychological toll of COVID-19

Initiative of board member Ruben Brave to start a Special Interest Group within The Internet Society

Founding of Media Special Interest Group on the initiative of ISOC NL

Data-driven research in mobility has prospered in recent years, providing solutions to real-world challengesincluding forecasting epidemics and planning transportation. These advancements were facilitated by com-putational tools enabling the analysis of large-scale data-sets of digital traces. One of the challenges whenpre-processing spatial trajectories is the so-calledstop location detection, that entails the reduction of raw timeseries to sequences of destinations where an individual was stationary. The most widely adopted solution wasproposed by Hariharan and Toyama (2004) and involves filtering out non-stationary measurements, then ap-plying agglomerative clustering on the stationary points. The state-of-the-art method, however, suffers of twolimitations: (i) frequently visited places located very close (such as adjacent buildings) are likely to be mergedinto a unique location, due to inherent measurement noise, (ii) traces for multiple users can not be analysedsimultaneously, thus the definition of destination is not shared across users. In this paper, we describe theIn-fostopalgorithm for stop location detection that overcomes the limitations of the state-of-the-art solution byleveraging the flow-based network community detection algorithm Infomap. We test Infostop for a populationof∼1000 individuals with highly overlapping mobility. We show that the size of locations detected by Infostopsaturates for increasing number of users and that time complexity grows slower than for previous solutions.We demonstrate that Infostop can be used to easily infer social meetings. Finally, we provide an open-sourceimplementation of Infostop, written in Python and C++, that has a simple API and can be used both for labelingtime-ordered coordinate sequences (GPS or otherwise), and unordered sets of spatial points.

2003.14370v1

Infostop: Scalable stop-location detection in multi-user mobility data.

Beck Institute is committed to supporting our global community as it responds to the urgent mental health needs posed by the COVID-19 pandemic. We have compiled the following resources to assist professionals in the health, mental health, and adjacent fields in helping their clients during this time. *Please note that any item with an asterisk is appropriate for the general public, in addition to mental health professionals. This is a dynamic list and we will add resources as we create or find them. If you have anything you would like to share, please email imcdaniels@beckinstitute.org.

COVID-19 Resources

CSO Covid-19 Research funding proposal

The Scottish Government is making funding available for research aimed at tackling the challenges posed by the current Covid-19 pandemic in Scotland. The Chief Scientist Office (CSO) will manage this resource to fund studies that allow fundamental science being conducted in Scottish Academic Institutions to be applied to issues arising from the Covid-19 pandemic. The research will draw on the very best science and methodologies in Scotland to address specific issues, with the expectation that studies can start immediately on award of funding and will be completed within 3 – 6 months. CSO can work with successful applicants to help expedite the necessary research governance reviews and to approve fast track ethical opinion to ensure rapid study start-up. Studies will address specific hypotheses through a range of research approaches, including experimental medicine, data science or social science. The outputs from this research will provide timely evidence to inform clinical practice and policy relevant to the current pandemic. Where support of the NHS for use of resources is required then evidence of the support of the local nodal Health Board, or special Boards (as appropriate) must be provided.

Covid-19 Changed How the World Does Science, Together

Never before, scientists say, have so many of the world’s researchers focused so urgently on a single topic. Nearly all other research has ground to a halt.

COVID-19: Insights from behavioural science

On 20th March, the Scientific Advisory Group for Emergencies (SAGE) released the evidence behind the government response to Coronavirus disease (COVID-19). This series of short articles summarises these 32 documents. You can view all our reporting on this topic under COVID-19. This article goes over insights from behavioural science such as the risk of public disorder and adherence to household isolation.

Is uncertainty worse than either test result?

Open Letter to the Research and Innovation Community

To address COVID-19, UKRI has been working hard and at pace to refocus its activity. Alongside NIHR we have delivered a rapid response call for research proposals on COVID-19 with up to £20 million available, with the first six projects announced on 23rd March. Later today we are launching a single web portal for streamlined shorter grant and innovation applications, including guidance for researchers currently holding UKRI standard grants that wish to repurpose their funds for COVID-19. Through Innovate UK we are working closely with the manufacturing industry to boost the production of ventilators and increase vaccine manufacturing infrastructure. We will continue to work across government to drive action on the sustainability of funding.

Urgent Funding for Research into Humanitarian Crises Like Epidemics and Pandemics

The key to fighting a global crisis like a pandemic is gathering as much knowledge as possible in the shortest amount of time: knowledge about the virus itself, about effective therapies, as well as their social, economic, and legal impacts. The far-reaching effects of the SARS-CoV-2 outbreak are currently turning the lives of billions of people upside down. The knowledge and methodological expertise of basic researchers are indispensable in the struggle against such crises and for coping with their consequences. New research projects in Austria are intended to enhance the existing potential, contribute to the rapid gain in knowledge, and thus prepare for future challenging situations.

A WHO tool for rapid, flexible and cost-effective monitoring of public knowledge, risk perceptions, behaviours and trust is now available to countries in the WHO European Region to make their COVID-19-related response relevant and actionable.

WHO tool for behavioural insights on COVID-19

Among my academic colleagues and friends, I have observed a common response to the continuing Covid-19 crisis. They are fighting valiantly for a sense of normalcy — hustling to move courses online, maintaining strict writing schedules, creating Montessori schools at their kitchen tables. They hope to buckle down for a short stint until things get back to normal. I wish anyone who pursues that path the very best of luck and health. Yet as someone who has experience with crises around the world, what I see behind this scramble for productivity is a perilous assumption. The answer to the question everyone is asking — "When will this be over?" — is simple and obvious, yet terribly hard to accept. The answer is never.

Governments across the world are putting in place unprecedented measures to slow the spread of the SARS-CoV-2 virus. These measures focus on social distancing—keeping people away from each other—including closing hospitality venues and shops, stopping mass gatherings, and in many cases requiring the population to stay at home except for essential journeys. The impact this is having on people’s lives, mental health, and livelihoods is substantial. Everything possible should be done to reduce the time over which such measures need to be in force, while still limiting the spread of the virus. Despite our best efforts, some inter-personal contact is inevitable during this time, such as between members of the same household, or by key workers with caring responsibilities. A behaviour that could make a substantial difference in these and other contexts, and costs nothing, is avoiding touching the mouth, nose and eyes —what has been called the T-Zone.1

Decentralized Privacy-Preserving Proximity Tracing: Simplified Overview

ThereisgrowinginterestfrompoliticiansandepidemiologistsaroundtheworldintechnologicalapproachestohelpindividualsandcountriesnavigatetheCOVID-19pandemic.OnesuggestedapproachhasbeentomakeuseofbluetoothsignalsonpersonalsmartphonestoprovideasystemthatinformsusersaboutencounterswithindividualswhohavesincetestedpositiveforCOVID-19,andprovidesdatatoepidemiologiststhatsupportstheireffortstomodelthespreadofthepandemic.However,theproposedinfrastructuresthatunderliesuch​proximitytracingsystemsvastlydifferintheirprivacyandsecurityproperties.Someproposalsmayfailtoprotecthighlysensitivedata,orcanbemisusedorextendedfarbeyondtheirinitialpurposeandlifetimeofthecrisis.Thisisallthemoreimportantgiventheglobalnatureofthischallengeandthefactthatthepandemicreachesacrossbordersandjurisdictionswithdifferentlevelsoffundamentalrightsguaranteesandintimeswheremanygovernments are functioning under rules of exception.

The Covid-19 Pandemic and International Trade: Temporary Turbulences or Paradigm Shift?

The Covid-19 epidemic has taken the world by surprise. Initially, it was seen as a Chinese, and later South-East Asian, problem. Decision-makers around the world apparently believed that the disease could be contained and controlled within the region, following a pattern that was evident in previous outbreaks, such as SARS. However, due to a combination of different factors of natural, political and regulatory character,1the epidemic has quickly spread to other parts of the world. The existing interconnectedness among countries obviously facilitated that process.

There’s been some dire headlines about the current outbreak , such as ‘Virus Panic’ from the Daily Mirror last week, featuring one woman wearing a face mask, and reports of hand gel selling out. As a main official recommendation is to wash hands, buying hand gel hardly seems a panic reaction.

Communication about Coronavirus

What is the true cost of the coronavirus lockdown?

Covid-19 is a public health crisis. At least, this is what the doctors, epidemiologists and clinicians who command the air waves are telling us. They’re right, of course. But it isn’t only that: it’s an economic and social crisis too – and yet social scientists have hardly been heard from. They don’t seem to be influencing policy that much either. When the UK government says its decisions are guided by the science, they appear to be referring only to the science of transmission of the virus and its direct consequences for health. And even health is narrowly defined here: as mortality risks in the next few months. The only graphs that typically appear at news conferences are those relating to the number of infections and deaths. As we enter the third week of a lockdown in the UK, the focus of addressing the death toll means suppression remains the only argument in town.

COVID-19 - eine Zwischenbilanz oder eine Analyse der Moral, der medizinischen Fakten, sowie der aktuellen und zukünftigen politischen Entscheidungen

Überlegungen eines besorgten Schweizer Bürgers

There have been questions from journalists regarding herd immunity.

expert comments about herd immunity

Mathematics of Epidemicson Networks

PrefaceOver the past decade, the use of networks has led to a new modelling paradigmcombining several branches of science, including physics, mathematics, biology andsocial sciences. The spread of infectious diseases between nodes in a network hasbeen a central topic of this growing field. The fundamental questions are easilystated, but answering them draws on observations and techniques of many fields.There is a long successful history of mathematical modelling informing policiesto mitigate the impact of infectious disease. Typically, models divide the popula-tion into compartments based on infection status and use simple assumptions aboutmixing and movements between these compartments. Over time, these models havegrown more sophisticated to more accuratelyincorporate the contact structure ofthe population and to take advantage of increased computational resources. For ex-ample, sexually transmitted diseases have been investigated using high-dimensionalcompartmental models separating individuals by contact rates, socio-economic sta-tus and many other factors. However, when we make the additional observation thatpartnerships may be long-lasting, a new paradigm is needed, leading naturally to anetwork representation of the population structure.

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.

2004.04463v1

This call aims to strengthen the evidence base to better prevent and control coronavirus (COVID-19) epidemics and to increase research and response capacity. It's part of an existing partnership between Wellcome and the UK Department for International Development (DFID) through the Joint Initiative on Research in Epidemic Preparedness and Response to help low- and middle-income countries prepare for and tackle epidemics.

Epidemic Preparedness: COVID-19 funding call

“Inundated, overloaded and bombarded” – Seven insights for communicating research to busy policymakers

Parliamentarians and their staff want to use evidence to support their decisions, but they can often feel ‘bombarded’ with the sheer amount of material that is sent their way. Drawing on the findings of a wide ranging study looking at the use of evidence by UK parliamentarians and their staff, Dr David Rose and Dr Chris Tyler provide seven insights for how academics can improve the chances of their evidence being used in the fast-paced, time-poor, information environment of the UK Parliament.

The Psychological Science Accelerator (PSA) is the largest consortium of research psychologists in the world. Behavioral scientists can play an important role in combating the growing COVID-19 pandemic if we quickly take action to collect high-quality data from large, global samples. The PSA wants to team up with researchers working on important questions related to this pandemic. 

The PSA Calls for Rapid and Impactful Study Proposals on COVID-19

The effectiveness of moral messages on public health behavioral intentions during the COVID-19 pandemic

With the COVID-19 pandemic threatening millions of lives, changing our behaviors to prevent the spread of the disease is a moral imperative. Here, we investigated the persuasiveness of messages inspired by three major moral traditions. A sample of US participants representative for age, sex and race/ethnicity (N=1032) viewed messages from either a leader or citizen containing deontological, virtue-based, utilitarian, or non-moral justifications for adopting social distancing behaviors during the COVID-19 pandemic. We measured the messages’ effects on participants’ self-reported intentions to wash hands, avoid social gatherings, self-isolate, and share health messages, as well as their beliefs about others’ intentions, impressions of the messenger’s morality and trustworthiness, and beliefs about personal control and responsibility for preventing the spread of disease. Consistent with our pre-registered predictions, compared to non-moral control messages, deontological arguments had a modest effect on intentions to share the message. Message source (leader vs. citizen) did not moderate any of the observed effects of message type. A majority of participants predicted the utilitarian message would be most effective, but we found no evidence that the utilitarian message was effective in changing intentions or beliefs. We caution that our findings require confirmation in replication studies and are modest in size, likely due to ceiling effects on our measures of behavioral intentions and strong heterogeneity across all dependent measures along several demographic dimensions including age, self-identified gender, self-identified race, political conservatism, and religiosity. Although we found no evidence that the utilitarian message was effective in changing intentions and beliefs, exploratory analyses showed that individual differences in one key dimension of utilitarianism—impartial concern for the greater good—were strongly and positively associated with public health intentions and beliefs. Overall, our preliminary results suggest that public health messaging focused on duties and responsibilities toward family, friends and fellow citizens is a promising approach for future studies of interventions to slow the spread of COVID-19 in the US. Ongoing work is investigating the reproducibility and generalizability of our findings across different populations, what aspects of deontological messages may drive their persuasive effects, and how such messages can be most effectively delivered across global populations.

Crisis knowledge management: Reconfiguring the behavioural science community for rapid responding in the Covid-19 crisis

The present crisis demands an all-out response if it is to be mastered with minimal damage. This means we, as the behavioural science community, need to think about how we can adapt to best support evidence-based policy in a rapidly changing, high-stakes environment. This piece is an attempt to initiate this process. The ‘recommendations’ made are first stabs that will hopefully be critiqued, debated and improved.

Background Exaggerated or simplistic news is often blamed for adversely influencing public health. However, recent findings suggested many exaggerations were already present in university press releases, which scientists approve. Surprisingly, these exaggerations were not associated with more news coverage. Here we test whether these two controversial results also arise in press releases from prominent science and medical journals. We then investigate the influence of mitigating caveats in press releases, to test assumptions that caveats harm news interest or are ignored. Methods and Findings Using quantitative content analysis, we analyzed press releases (N = 534) on biomedical and health-related science issued by leading peer-reviewed journals. We similarly analysed the associated peer-reviewed papers (N = 534) and news stories (N = 582). Main outcome measures were advice to readers and causal statements drawn from correlational research. Exaggerations in press releases predicted exaggerations in news (odds ratios 2.4 and 10.9, 95% CIs 1.3 to 4.5 and 3.9 to 30.1) but were not associated with increased news coverage, consistent with previous findings. Combining datasets from universities and journals (996 press releases, 1250 news), we found that when caveats appeared in press releases there was no reduction in journalistic uptake, but there was a clear increase in caveats in news (odds ratios 9.6 and 9.5 for caveats for advice and causal claims, CIs 4.1 to 24.3 and 6.0 to 15.2). The main study limitation is its retrospective correlational nature. Conclusions For health and science news directly inspired by press releases, the main source of both exaggerations and caveats appears to be the press release itself. However we find no evidence that exaggerations increase, or caveats decrease, the likelihood of news coverage. These findings should be encouraging for press officers and scientists who wish to minimise exaggeration and include caveats in their press releases.

Exaggerations and Caveats in Press Releases and Health-Related Science News

Since the emergence of the new coronavirus (COVID-19) in December 2019, we have adopted a policy of immediately sharing research findings on the developing pandemic. These pages provide all output from the Imperial College COVID-19 Response Team, including publicly published online reports, planning tools, scientific resources, publications and video updates.

COVID-19

2003.07372v1

At the time of this writing, the novel coronavirus (COVID-19) pandemic outbreak has already put tremendous strain on many countries' citizens, resources and economies around the world. Social distancing measures, travel bans, self-quarantines, and business closures are changing the very fabric of societies worldwide. With people forced out of public spaces, much conversation about these phenomena now occurs online, e.g., on social media platforms like Twitter. In this paper, we describe a multilingual coronavirus (COVID-19) Twitter dataset that we have been continuously collecting since January 22, 2020. We are making our dataset available to the research community (this https URL). It is our hope that our contribution will enable the study of online conversation dynamics in the context of a planetary-scale epidemic outbreak of unprecedented proportions and implications. This dataset could also help track scientific coronavirus misinformation and unverified rumors, or enable the understanding of fear and panic --- and undoubtedly more. Ultimately, this dataset may contribute towards enabling informed solutions and prescribing targeted policy interventions to fight this global crisis.

COVID-19: The First Public Coronavirus Twitter Dataset

Most journals in the biomedical and behavioral sciences require that all co-authors contribute to the writing "or critical revision" of the paper, which is unwieldy when there are dozens or hundreds of co-authors.

All should be recognized for this in a way that our norms will allow recognition for when applying for grants and jobs. Unfortunately, nobody seems comfortable with putting papers on their CV for which they are mentioned only in the acknowledgments.

Large numbers of researchers are coming together to work on new projects, because of COVID-19, for example with @PsySciAcc. But journals' authorship "guidelines" (rules) deter these large-scale collaborations.

Moreover, ICMJE guidelines require that all authors are "accountable for all aspects of the work", which is sometimes unreasonable. To me, the overarching principle is that papers should say who did what, including everyone who contributed substantially to the paper.

Also, the names in acknowledgments don't get encoded in databases like Web of Science. Therefore we have to use the author list. But the word "author" is closely associated with "writer", which is one reason that authorship guidelines say all should contribute to the writing.

Yet the authorship guidelines of other fields, like chemistry, don't require writing contributions. McNutt et al. in PNAS, without explaining fully what they were doing, recommended eliminating the writing requirement for biomedicine, as I explain here https://mdpi.com/2304-6775/7/3/48/htm… .

In addition they recommend the adoption of CRediT, which is a machine-readable taxonomy for indicating which area of the project each author contributed to. Hundreds of journals are now adopting CRediT (and some, like @PLOS, adopted it a few years ago).

Most, however (including @PLOS), have kept the ICMJE writing contribution requirement. I think we should rewrite authorship guidelines from scratch, as contributorship guidelines. But no one has written such a thing, to my knowledge.

This week on the podcast we ask how we maintain our emotional and mental health during the Covid-19 pandemic. We also process the events of the week just gone, there’s top tips for staying healthy while working or studying at home, and we have a think about what universities and government should be doing to help. With John de Pury, Assistant Director of Policy at Universities UK and Fran Longstaff, Head of Psychology at Fika.

PODCAST: Mental health, universities and Covid-19

You need to know this background because it helps with search terms. For example, once you know that SARS, MERS and COVID-19 are respiratory syndromes caused by coronaviruses, you can use this to search for relevant psychology / mental health research.