Background: The COVID-19 pandemic in the Republic of Ireland precipitated the temporary closure of all non-essential services and a nationwide quarantine as of March 27th, 2020. This study represents the first assessment of the mental health of the nation during the initial phase of the COVID-19 response, as part of a multi-wave investigation into the social, behavioural, and psychological impact of the pandemic. Aims: First, estimate prevalence rates of depression, generalized anxiety disorder, and combined anxiety and depression, and identify risk factors associated with screening positive for anxiety/depression. Second, determine if COVID-19-related anxiety was highest amongst those identified with the greatest mortality risk from the virus. Method: Self-report data from a nationally representative Irish sample (N = 1,041) was collected online between March 31 and April 5; the first week of the Republic of Ireland’s nationwide quarantine measures. Results: A substantial proportion of people screened positive for depression (22.77%), generalized anxiety (20.00%), and anxiety/depression (27.67%). Screening positive for anxiety/depression was associated with younger age, female sex, loss of income due to COVID-19, COVID-19 infection, and higher perceived risk of COVID-19 infection. Citizens aged 65 and older reported significantly higher levels of COVID-19 anxiety than adults aged 18-34. Sex, underlying health conditions, and proximity to COVID-19 deaths were not associated with COVID-19 anxiety. Conclusions: Government responses to the current pandemic should ensure that measures protect not only the population’s physical health, but its mental health also, as an equally important component of health and wellbeing.

The COVID-19 pandemic substantially changed daily life in ways that may impact mental health. This study compared a nationally representative online sample of 2,032 U.S. adults in late April 2020 to 19,330 U.S. adult internet users who participated in the 2018 National Health Interview Survey (NHIS) using the Kessler-6 scale of mental distress in the last 30 days. Compared to the 2018 NHIS sample, U.S. adults in April 2020 were eight times more likely to fit criteria for serious mental illness (27.7% vs. 3.4%) and three times more likely to fit criteria for moderate or serious mental illness (70.4% vs. 22.0%). Differences between the 2018 and 2020 samples appeared across all demographic groups, with larger differences among younger adults and those with children in the household. These considerable levels of mental distress may portend substantial increases in diagnosed mental disorders and in the morbidity and mortality associated with them.

The aim of this study was to analyze the psychological impact of COVID-19 in the university community during the first weeks of confinement. A cross-sectional study was conducted. The Depression Anxiety Stress Scale (DASS-21) was employed to assess symptoms of depression, anxiety and stress. The emotional impact of the situation was analyzed using the Impact of Event Scale. An online survey was fulfilled by 2530 members of the University of Valladolid, in Spain. Moderate to extremely severe scores of anxiety, depression, and stress were reported by 21.34%, 34.19% and 28.14% of the respondents, respectively. A total of 50.43% of respondents presented moderate to severe impact of the outbreak. Students from Arts & Humanities and Social Sciences & Law showed higher scores related to anxiety, depression, stress and impact of event with respect to students from Engineering & Architecture. University staff presented lower scores in all measures compared to students, who seem to have suffered an important psychological impact during the first weeks of the COVID-19 lockdown. In order to provide timely crisis-oriented psychological services and to take preventive measures in future pandemic situations, mental health in university students should be carefully monitored.

And also, in the process, their citizens

Changes to our social settings caused by migration, cultural change or pandemics mean that we must learn and adapt to new social norms. Building on the notion that social norms provide a group of individuals with behavioural prescriptions and therefore can be inferred from individuals’ behaviour, I examined how two features of the behavioural patterns of social norms— saliency and valence —affect learning and adaptation. Using a multiplayer star-harvest game, I found that participants initially complied with a variety of social norms exhibited by the other players in the game. Yet after gaining experience with competitive norms, participants did not adapt their behaviour when playing with polite players. This lack of adaptation was explained by the combined contribution of an active and salient behavioural pattern and its negative outcome for others. A computational model fitting procedure suggested that saliency affected learning rates as players learned more from active behaviour than from passive behaviour, while negative outcomes were more readily generalized from one player to others. These results provide a novel cognitive foundation for social norm learning and adaptation and can inform future investigations of cross-cultural differences and social adaptation.

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As the current pandemic situation evolves, urges have been made to focus efforts toward examining the mental health status of the general population. This large-scale epidemiological investigation assesses the prevalence of depression and anxiety associated with the mitigation strategies aimed at impeding viral transmission chains during the COVID-19 pandemic. A representative sample of 10084 adults participated in the study. The results reveal that the globally ubiquitous mitigation strategies involving lockdowns, social distancing, quarantine, and isolation are associated with two to threefold increases in anxiety and depression symptoms. Risk factors and protective factors associated with these psychiatric symptoms were identified. Finally, factors associated with adherence rates to these mitigation strategies were investigated. The presented time-sensitive findings provide health-policy makers and government officials with a foundation for making informed decisions concerning the mental health impacts of the contemporaneously in-practice disease containment strategies, and suggests ways of increasing adherence to these protocols while simultaneously protecting the general public against detrimental mental health effects.

Research has demonstrated that situational factors such as perceived threats to the social order activate latent authoritarianism. The deadly COVID-19 pandemic presents a rare opportunity to test whether existential threat stemming from an indiscriminate virus moderates the relationship between authoritarianism and political attitudes toward the nation and outgroups. Using data from a large, nationally representative sample of adults in the UK (N = 2,025) collected during the first week of strict lockdown measures (23-28 March 2020), we find that the associations between right-wing authoritarianism and 1) nationalism and 2) anti-immigrant attitudes are conditional on levels of perceived threat. As anxiety about the COVID-19 pandemic increases, so too does the effect of right-wing authoritarianism on those political outcomes. Thus, it appears that grave threats to humanity from the COVID-19 pandemic activate authoritarians in society, which in turn, shifts opinion toward nationalistic and anti-immigrant sentiments.

Large-scale health crises, such as the Covid-19 pandemic, may evoke negative affective responses, which are closely linked to psychological maladjustment and psychopathology. Here, we shed light on the role of the personality trait neuroticism in predicting who is at risk and why. In a large-scale experience-sampling study based on a German convenience sample (N = 1,609; 38,120 momentary reports), individuals high in neuroticism experienced more negative affect in their daily lives during the Covid-19 pandemic. The effects of neuroticism on negative affect were substantially stronger than those of sociodemographic factors and personally experienced health threats. Underlying mechanisms included (a) higher attention to Covid-19-related information and higher engagement in Covid-19-related worries (crisis preoccupation), and (b) stronger negative affect during this preoccupation (affective reactivity). These findings highlight that global pandemics put not only people’s physical health at stake but also their psychological well-being and offer concrete starting points for large-scale prevention efforts.

Results of a three-wave longitudinal study conducted in the first four weeks of the COVID-19 pandemic in Poland (N = 889) indicate that right wing-authoritarianism increased as the pandemic unfolded, predicting significant growth in national cohesion and in the belief that non-traditional women and sexual minorities threaten the national survival. Latent growth curve modeling indicated linear, inter-related increases in those variables (but not in self-reported political conservatism, social dominance orientation, ambivalent sexism or outgroup hostility) across the three waves. Cross-lagged panel analysis supported the predicted directionality of the relationships. The results are in line with terror management theory predicting that self-continuity concerns increased under mortality salience should motivate investment in cohesive groups and rejection of those dissenters who threaten the prospects of national survival. The results are also in line with findings that the threat of infectious disease increases conformity and ingroup cohesion and sexual prejudice.

In the context of Covid-19 pandemic, barrier gestures such as regular hand washing, social distancing, and wearing face mask are highly recommended. Critically, interpersonal distances depend on the physical and emotional dimensions involved in social interaction, two factors that might be affected by the current Covid-19 context. In the present internet-based experimental study, we analyzed the preferred interpersonal distance of 461 participants, when facing a virtual character either wearing a face mask or displaying a neutral, happy or angry facial expression. The results showed that interpersonal distance is significantly reduced when the characters wear a face mask compared to other conditions. Importantly, it was also reduced in participants already infected with Covid-19, or living in a low-risk area. The present findings are of dramatic importance as they indicate that the general requirement to wear a mask in social contexts can have deleterious effects, interfering with social distancing recommendations.

Most countries have implemented nationwide lockdown policies aimed to decelerate the spread of SARS-CoV-2. Epidemiologists have recently discussed scenarios according to which these policies would need to be extended and/or intensified. To illuminate how the general public might react to such scenarios, we assessed Germans’ endorsement of and compliance with five specific scenarios. Results show that, in general, length of lockdown plays a more important role than intensity. Although half of the respondents reject any further extensions or intensifications, 20% would endorse long-term strategies if necessary. Simulations predicting the effects of different lockdown scenarios should take the public’s endorsement of and compliance with these scenarios into account.

The COVID-19 Psychological Research Consortium (C19PRC) Study aims to assess and monitor the psychological and social impact of the coronavirus (COVID-19) pandemic in the general population, using longitudinal surveys and mixed-methods studies in multiple countries. The first strand of the study, an internet-based panel survey, was launched in the UK in March 2020 during the earliest stages of the pandemic in that country (hereafter referred to as C19PRC-UKW1). This paper describes (1) the development, design and content for C19PRC-UKW1, which was informed by the extant evidence base on the psychosocial impact of previous global outbreaks of similar severe acute respiratory syndromes (e.g. SARS, H1N1, MERS); (2) the specific socio-economic and political context of the C19PRC-UKW1; (3) the recruitment of a large sample of UK adults aged 18 years and older (n=2025) via an internet-based panel survey; (4) the representativeness of the C19PRC-UKW1 sample compared to the UK adult population in terms of important sociodemographic characteristics (e.g. age, sex, household income, etc.); and (5) future plans for C19PRC Study including follow-up survey waves in the UK, supplementary non-survey based study strands linking from the C19PRC-UKW1 and the roll-out of the study to other countries.

This preliminary, exploratory and qualitative report aimed at at raising public debate on the topic of psychological consequences of Covid-19 lockdown in children. Results are exploratory and conclusions are speculative and must be confirmed through further rigorous studies. Our preliminary data suggests that during the first month of quarantine, the pandemic had an important effect on children's emotions and behavior. One in four children (26.48%) showed the regressive symptom of the demand for physical proximity to their parents during the night and almost one in five (18.17%) manifested fears that they never had before. Half of the children (53.53%) showed increased irritability, intolerance to rules, whims and excessive demands, and one in five presented mood changes (21.17%) and sleep problems including difficulty falling asleep, agitation, and frequent waking up (19.99%). One in three (34.26%) displayed nervousness about the topic of pandemic when it was mentioned at home or on TV. Almost one in three (31.38%) seemed calmer and one in two (49.57%) seemed wiser and more thoughtful. Almost all (92.57%) seemed able to adapt to the pandemic restrictions; even though one in two (43.26%) seemed more listless to the activities they were used to perform before the pandemic including playing, studying, and gaming.

Mental health problems are common in pregnancy, typically affecting between 10-25% of pregnant individuals. Elevated symptoms of depression and anxiety can negatively impact both the pregnant individual and developing fetus. The current COVID-19 pandemic is a unique stressor with potentially wide-ranging consequences for pregnancy and beyond. We assessed symptoms of anxiety and depression among pregnant individuals during the current COVID-19 pandemic and determined factors that were associated with psychological distress. 1987 pregnant participants were surveyed across Canada in April 2020. The assessment included questions about COVID-19-related stress and standardized measures of depression, anxiety, pregnancy-related anxiety, sleep and social support. We found substantially elevated psychological distress compared to similar pre-pandemic pregnancy cohorts, with 37% reporting clinically relevant symptoms of depression, 57% reporting clinically relevant symptoms of anxiety, and 68% reporting elevated pregnancy-related anxiety. Higher levels of social support and longer sleep duration were associated with lower psychological symptoms across domains. This study shows concerningly elevated levels of psychological distress among pregnant individuals during the COVID-19 pandemic, that may have long-term impacts on their children. Potential intervention targets are needed in addition to improving protective factors related to increased social support and sleep -- these should be urgently considered to mitigate long-term negative outcomes

Abstract This report presents a rapid review of the evidence on reflective practice in crisis situations. In the light of the current Covid-19 pandemic we asked the question “What lessons can we learn about the value of reflective practice that may be of service to our health professionals on the front line”. A search of Scopus (with no date restrictions was made. A total of 56 papers were identified. After review of titles and abstract 34 papers were rejected as not being relevant. The remaining 22 papers were reviewed and their findings synthesised. Most of the papers (n=9) were reflections on relevant experiences. Five were commentaries, six reported the findings from qualitative studies and two reported findings from cross sectional surveys. The findings and recommendations can be presented in terms of strategies that can be employed when preparing for a crisis, when in the midst of a crisis and following conclusion of a crisis. There are two key recommendations identified from the review: 1. There are things we can do now: The importance right now of ensuring that staff have both time and a safe space in which they can reflect on their experiences either with fellow staff or via peer- facilitated reflections. That way those staff who wish to, could be supported to reflect on and process their after a difficult shift. 2. There are things we can think about in the mid to longer term: the importance of organisations learning from the pandemic. Post pandemic, perhaps developing simulations, but also formally engaging in structured reflective practice to ensure practical skills are honed and lessons learned and b) paying attention to the organisational culture and ensuring that reflective practice is embedded as ‘business as usual’ Underpinning these two points are a range of issues organisations should bear in mind. These include: • Supportive and encouraging organisational culture that recognises the importance of embedding reflective practice in everyday work • Leaders should themselves be involved in the process of reflection as participants • Staff need time and space to reflect on their experiences

In the service of reducing disease transmission through interpersonal contact, humans have evolved a behavioral immune system that facilitates identification and avoidance of environmental pathogens. One behavioral strategy in response to pathogenic threat is the adoption of interpersonal reticence. However, reticence may impede status acquisition, a process that necessarily fosters distinctiveness from the rest of the group that affords increased access to resources. The current program of research tested whether activation of pathogen-avoidant motives through priming fosters reticence related to status, namely disinterest in pursuing a group leadership position (Study 1) or disinterest in accepting a group leadership position when led to believe one has been selected by others (Study 2). Results indicated that those high in germ aversion were particularly interested in pursuing leadership as a form of status, with disease salience unexpectedly heightening status motives among those low in germ aversion. Furthermore, those high in perceived infectability reported reluctance in accepting high-status positions, although disease salience heightened interest in accepting a leadership role. We contextualize these findings by identifying the dispositional and situational factors that foster individuals to invoke a motivational tradeoff between status and pathogen-avoidant motives.

Background: The coronavirus disease 2019 (COVID-19) pandemic is having a profound impact on the global population. The pandemic may raise additional, unique challenges for individuals with experience of eating disorders. Many factors have potential for detrimental impacts on psychological wellbeing and eating disorder recovery, including: Disruption to living situations, ‘social distancing’ restrictions, difficult access to healthcare, and societal changes to food behaviours and technology usage. To date, little is known on the impact of the pandemic on this population. Method: A mixed-methods online survey was used to gather data from individuals currently experiencing, or in recovery from, an eating disorder during the early stages of the UK pandemic lockdown. Results: Findings suggest that the COVID-19 pandemic is having a profound, negative impact upon individuals with experience of eating disorders. 7 key themes are identified: Disruption to living situation, increased social isolation and reduced access to usual support networks, changes to physical activity rates, reduced access to healthcare services, disruption to routine and perceived control, changes to relationship with food, and increased exposure to triggering public messages. The results suggest detrimental impacts on psychological wellbeing including decreased feelings of control, increased feelings of social isolation, increased rumination about disordered eating, and low feelings of social support. Conclusions: This research demonstrates how the ED population is at significant risk of negative impacts of pandemics. There is a vital need for interventions to support this population during such events. Inequalities in healthcare provision were also identified, emphasising a need for a more cohesive approach to remote ED treatment across UK healthcare services. Positive aspects of technology use were identified but the results suggest a need to address and/or limit the potential for negative impacts of public messages around food and exercise behaviours, and to co-design technologies with people with EDs to facilitate effective treatment.

In the wake of the coronavirus (COVID-19) pandemic, US organizations that provide applied behavior analysis (ABA) programs to individuals with autism spectrum disorder (ASD) have implemented a variety of safety precautions to minimize the spread of the virus, often shifting center-based services to the home or telehealth. Considered essential workers, ABA providers are exempt from government directives to close, so they have both the freedom and heavy responsibility to make their own decisions about how best to keep their clients safe while continuing to provide medically necessary services. In the coming weeks and months, ABA providers will be faced with the decision about whether to reopen centers. This article does not address that decision, except to acknowledge the urgency to reopen, both to help clients and remain solvent. Political rhetoric and contradictory public information further complicate this daunting decision. Because ABA providers do not have legal guidance to shift the burden of such decisions to local and state regulators, the burden is theirs alone. The unprecedented nature of the COVID-19 pandemic means that no decision is clearly wrong or right, and every decision has consequences. Although ABA providers do not have their own state guidance, many states have issued guidelines for childcare providers whose operations have continued throughout the pandemic. This article analyzes that guidance, identifies common variables potentially relevant to ABA organizations,, highlights clinical considerations and procedural compliance, and provides ABA organizations with the tools to make the best decision for their clients, in their community, and on their timeline.

The COVID-19 outbreak has affected people’s lives across the globe. To investigate how the pandemic impacts well-being, we compiled a large and comprehensive data-set of 2231 working adults covering454 counties across 48 states in the U.S. from social media using machine learning tools. We found that pandemic severityinfluencedworking adults’ negative affect rather than positive affect. However, the relationship between pandemic severity and the negative affect wasmoderated by personality (i.e., openness and conscientiousness) and family connectedness. Specifically, pandemic severitydidnot influence the well-being of those who wereopen to new experiences, who wereconscientious, and those who wereconnected to family. We didnot find the moderation effect of age, some other personality traits (i.e., extraversion, agreeableness, and neuroticism), faith, friend connectedness, or having kids on the relationship between pandemic severity and working adults’ negative affect.

2In press, TICSABSTRACTCognitive scientists have ramped up online testing in response to the coronaviruspandemic. Although research conducted online solves the problem of data collection, a lackof internet access among low-income and minority communities may reduce the diversityof study samples and, thus, impact the generalizability of scientific findings

PROSOCIAL BEHAVIORAND ADOLESCENT HEALTH2AbstractThisstudy examined adolescents’prosocial experiencesas both actors and recipients during COVID-19andassessed whether these experiences wereassociatedwith their mental health and community attachments. Adolescents (N=437; 78% female)wererecruited across the USusing social mediaand reported on theirCOVID-19 prosocial experiences (giving, receiving help), mental health (depressive symptoms, anxietysymptoms, burdensomeness, belongingness),and community attachments(social responsibility, socialtrust, self-interest). Greater engagement in COVID-19prosocial behavior was associated with greater anxiety symptoms, burdensomeness, andsocial responsibility. Receiving moreCOVID-19 helpwas associated with lower depressive symptomsandhigherbelongingness, social trust, andself-interest. Findings highlight the importance of furthering our understanding of these connections in adolescence to help inform post-pandemic recovery and relief efforts.

Human behaviour is central to transmission of SARS-Cov-2, the virus that causes COVID-19, and changing behaviour is crucial to preventing transmission in the absence of pharmaceutical interventions. Isolation and social distancing measures, including edicts to stay at home, have been brought into place across the globe to reduce transmission of the virus, but at a huge cost to individuals and society. In addition to these measures, we urgently need effective interventions to increase adherence to behaviours that individuals in communities can enact to protect themselves and others: use of tissues to catch expelled droplets from coughs or sneezes, use of face masks as appropriate, hand-washing on all occasions when required, disinfecting objects and surfaces, physical distancing, and not touching one’s eyes, nose or mouth. There is an urgent need for direct evidence to inform development of such interventions, but it is possible to make a start by applying behavioural science methods and models.

Social distancing is crucial for preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the closely physical contact between people, we can reduce chances of catching the virus and spreading it to the community. This paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, AI can enable, encourage, and even enforce social distancing. To that end, we provide a fundamental background of social distancing including basic concepts, measurements, models and propose practical scenarios. We then discuss enabling wireless technologies which are especially effective and can be widely adopted in practice to keep distance and monitor people. After that, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we provide important open issues and challenges (e.g., privacy-preserving, cybersecurity) in implementing social distancing in practice.

Introducción: La tecnología evoluciona exponencialmente y la relación médico-paciente no ha permanecido inmutable. La consulta no presencial se torna más frecuente cada vez. Objetivos: explorar la experiencia de la consulta virtual desde el punto de vista del paciente y del profesional. Metodología: Estudio observacional, exploratorio, descriptivo y transversal mediante encuesta online Resultados: 96,9% (n=94) de los profesionales encuestados reciben consultas de pacientes en su teléfono particular; 95,9% (n=93) utiliza aplicaciones de mensajería en la comunicación con sus pacientes. Estas consultas no presenciales les resultan siempre intrusivas a un 28.9% de los psiquiatras, y a un 52.6% a veces. 67% de los profesionales manifestó preocupación por las posibles consecuencias médico - legales de dichas consultas. Entre los pacientes encuestados, un 39.3% (N=35) refirió haber realizado consultas no presenciales a su médico en los últimos 30 días, de las cuales solo 33% fueron motivadas por situaciones de urgencia

The threat of contagion can twist our psychological responses to ordinary interactions, leading us to behave in unexpected ways

The rapid pace of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic (COVID-19) presents challenges to the robust collection of population-scale data to address this global health crisis. We established the COronavirus Pandemic Epidemiology (COPE) consortium to bring together scientists with expertise in big data research and epidemiology to develop a COVID-19 Symptom Tracker mobile application that we launched in the UK on March 24, 2020 and the US on March 29, 2020 garnering more than 2.8 million users as of May 2, 2020. This mobile application offers data on risk factors, herald symptoms, clinical outcomes, and geographical hot spots. This initiative offers critical proof-of-concept for the repurposing of existing approaches to enable rapidly scalable epidemiologic data collection and analysis which is critical for a data-driven response to this public health challenge.

A randomised controlled trial1Cao B Wang Y Wen D et al.A trial of lopinavir–ritonavir in adults hospitalized with severe Covid-19.N Engl J Med. 2020; (published online March 18.)DOI: 10.1056/NEJMoa2001282Crossref Google Scholar found no significant benefit of lopinavir-ritonavir over placebo in patients with severe corona-virus disease 2019 (COVID-19). Even though lopinavir-ritonavir might not be efficacious in cases of severe COVID-19, we postulate that similar treatments to these antivirals, which also effectively inhibit viral replication, will be more effective in preventing disease progression if used to treat mild disease than if used to treat severe disease. We encourage randomised trials to assess antiviral drugs for the treatment of mild COVID-19.Similar to influenza, we propose that antiviral drugs for severe acute respiratory syndrome coronavirus 2 are probably most effective if administered soon after symptom onset, when viral replication is likely to be an important driver of pathogenesis. For example, oseltamivir has shown benefits over placebo in patients with influenza if administered less than 48 h after the onset of symptoms. By contrast, administration of this influenza neuraminidase inhibitor more than 48 h after symptom onset showed no significant benefit over placebo. Likewise, baloxavir, which targets the cap-dependent endonuclease, was more effective when given to patients with influenza within 24 h of symptom onset than when given to patients after 24 h of symptom onset.2

Traditionally, center-based Applied Behavior Analysis (ABA) therapy is delivered in a face-to-face format across several sessions; however, given the recent COVID-19 pandemic, many behavior analysts practicing in ABA centers have temporarily been authorized to provide services using a telehealth model. This rapid change in service delivery has posed unique challenges for behavior analysts and families with loved ones who were receiving ABA services in centers or schools. Among one of the most pressing concerns is that behavior analysts commonly rely on direct observation of behavior to make data-based decisions regarding client treatment plans. The shift to a telehealth model in light of the COVID-19 pandemic requires behavior analysts to quickly adapt the manner in which many have been providing services. Although many behavior analysts may be familiar with various technologies, they may struggle to identify the benefits and limitations of each technology in terms of providing telehealth. Selecting an appropriate technology that allows behavior analysts to stay true to the science of behavior analysis is prudent. The purpose of this paper is to provide a review of some available technologies to support telehealth that will allow behavior analysts to conduct direct observation from a remote location. The three technologies reviewed are 1) web camera, 2) Swivl, and 3) telepresence robot. Features of these technologies will be described and compared including benefits and drawbacks of each (see Table 1). Sample task analyses for using each technology are also provided.

On March 6, 2020, the first case of coronavirus disease 2019 (COVID-19) was officially reported in Cameroon.1Tih F Cameroon confirms first coronavirus case.https://www.aa.com.tr/en/africa/cameroon-confirms-first-coronavirus-case/1756866Date: March 6, 2020Date accessed: April 28, 2020Google Scholar As of April 25, 2020, the number of cases had increased to 1569, with 53 deaths,2UN Office for the Coordination of Humanitarian AffairsCameroon: COVID-19 rapport de situation no 21_23–25 April 2020.https://www.humanitarianresponse.info/en/op%C3%A9rations/cameroon/document/cameroon-covid-19-rapport-de-situation-n%C2%B02123-25-avril-2020Date: April 27, 2020Date accessed: April 29, 2020Google Scholar indicating an exponential growth in the number of cases. Although these numbers already sound shocking, the truth is that they are an underestimation because the diagnostic system for COVID-19 in Cameroon is not robust. Realistic projections in this context suggest approximately 14 000 cases of COVID-19 in the country.A series of infection control measures have been implemented by the Government of Cameroon, including hygienic measures (eg, systematic hand washing), physical distancing, closure of all educational facilities and international borders, interministerial consultations that included the input of development partners, and financial measures allocated to implement this response. The university research community and national media outlets helped to develop and implement these measures alongside medical practitioners.3

“For whosoever hath, to him shall be given, and he shall have more abundance: but whosoever hath not, from him shall be taken away even that he hath.” This Biblical verse, Matthew 13:12, provides a rare modern-day example of religion informing science—and Science—in the form of sociologist Robert K Merton's article in a 1968 issue of that prestigious journal. Merton proposed the existence of what he called “The Matthew effect” in scientific research: one that “may serve to heighten the visibility of contributions to science by scientists of acknowledged standing and to reduce the visibility of contributions by authors who are less well known.” He added that this principle might apply not only to individual researchers, but also at an institutional level, and hence that “centers of demonstrated scientific excellence are allocated far larger resources for investigation than centers which have yet to make their mark”. 3 years after Merton, researcher and general practitioner Julian Tudor-Hart published an article on “the inverse care law” in The Lancet. He noted “that the availability of good medical care tends to vary inversely with the need of the population served”.This pattern of advantage fuelling advantage, while disadvantage has a vicious cycle of its own, also applies to patient groups: specifically, people living with severe mental illness. Before the coronavirus disease 2019 (COVID-19) pandemic, the wants and needs of people with diagnoses such as schizophrenia rarely entered into increasing public discussion of mental health issues. Even in professional movements—such as that for global mental health—offering desperately needed help to those experiencing severe mental illness was too often secondary to the more prominent discourse around easily scaled and delivered talking therapies for common mental disorders.

Volunteer! Universities, community groups and even the NHS recommend it, citing benefits for society and also yourself. The claimed personal outcomes include boosting your health and subjective wellbeing, but while the former is slowly gathering experimental backing, the wellbeing research is overwhelmingly correlational, making it hard to prove that volunteering is causing the gains (it’s certainly plausible, for instance, that happier people are simply more inclined to give up their time for free). Now the journal Comprehensive Results in Social Psychology has published a more robust test: a randomised study. The researchers looked for evidence to support the mental wellbeing benefits from volunteering … but they looked in vain.

Human decision-making is often swayed by irrecoverable investments even though it should only be based on future – and not past – costs and benefits. Although this sunk cost effect is widely documented and can lead to devastating losses, the underlying psychological mechanisms are unclear. To tease apart possible explanations through a comparative approach, we assessed capuchin and rhesus monkeys’ susceptibility to sunk costs in a psychomotor task. Monkeys needed to track a moving target with a joystick-controlled cursor for variable durations. They could stop at any time, ending the trial without reward. To minimize the work required for a reward, monkeys should have always persisted for at least 1 second, but should have abandoned the trial if that did not yield a reward. Capuchin monkeys and especially rhesus macaques persisted to trial completion even when it was suboptimal, and were more likely to complete the trial the longer they had already tracked the target. These effects were less pronounced, although still present, when the change in expected tracking duration was signalled visually. These results show that sunk cost effects can arise in the absence of human-unique factors and may emerge, in part, because persisting can resolve uncertainty.

As of April 20, 2020, 14 068 people have been infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Africa, of whom 3158 (22·4%) are in South Africa.1WHOCoronavirus (COVID-19).https://www.afro.who.int/health-topics/coronavirus-covid-19Date accessed: April 20, 2020Google Scholar The transmissibility of SARS-CoV-2, combined with the scarcity of crucial health equipment and the challenges of implementing widespread physical distancing and case isolation, poses a grave threat to the continent.To illustrate the potential burden of SARS-CoV-2 epidemics within the most vulnerable countries in Africa, we simulated a SARS-CoV-2 outbreak in DR Congo in the absence of interventions. Using an age-structured epidemiological model (appendix p 1), and assuming a basic reproductive number of 2·72 (95% CI 2·56–2·87),2MIDAS NetworkMIDAS 2019 novel coronavirus repository.https://github.com/midas-network/COVID-19Date accessed: April 9, 2020Google Scholar we estimate that there would be 76 213 155 infections (95% CI 74 156 965–77 800 029) and 319 441 deaths (313 079–324 175) in the absence of physical distancing (figure). Although individuals younger than 20 years account for 42 752 770 (95% CI 41 551 696–43 683 014; 56·1%) of these simulated SARS-CoV-2 infections, individuals aged 50 years and older constitute 280 623 (275 356–284 509; 87·8%) of the deaths in our model prediction. Given the high prevalence of comorbidities in DR Congo, as there is in Africa more broadly, the death toll could even be much higher.3

A core proposition in economics is that voluntary exchanges benefit both parties. We show that people often deny the mutually beneficial nature of exchange, instead espousing the belief that one or both parties fail to benefit from the exchange. Across 4 studies (and 7 further studies in the Supplementary Materials), participants read about simple exchanges of goods and services, judging whether each party to the transaction was better off or worse off afterwards. These studies revealed that win–win denial is pervasive, with buyers consistently seen as less likely to benefit from transactions than sellers. Several potential psychological mechanisms underlying win–win denial are considered, with the most important influences being mercantilist theories of value (confusing wealth for money) and naïve realism (failing to observe that people do not arbitrarily enter exchanges). We argue that these results have widespread implications for politics and society.

Bei der epidemiologischen Abklärung geht es darum, darzustellen, wie sich ein Krankheitsausbruch innerhalb der Bevölkerung verbreitet: Dafür versucht man, Quellen der Infektion bzw. Übertragungsketten der Fälle durch persönliche Befragungen von erkrankten bzw. positiv getesteten Personen (= Fällen) zu identifizieren. Wenn man weiß, wie sich die Krankheit in der Bevölkerung verbreitet, können Maßnahmen gesetzt werden, die am wahrscheinlichsten dazu beitragen, die Verbreitung einzudämmen oder zu verlangsamen.

Ceasefires are breaking down as America and China bicker at the UN

As research and government responses to the COVID-19 outbreak escalate in the face of a global public health crisis, Vincent Larivière, Fei Shu and Cassidy R. Sugimoto reflect on efforts to make research on this subject more widely available. Arguing that a narrow focus on research published in high ranking journals predominantly in English has impeded research efforts, they suggest that the renewed emphasis on carrying out open research on the virus presents an opportunity to reassess how research and scholarly communication systems serve the public good.

As a greater portion of the world begins to live more of their life online, the world’s top 100 websites continue to see explosive growth in their traffic numbers. To claim even the 100th spot in this ranking, your website would need around 350 million visits in a single month. Using data from SimilarWeb, we’ve visually mapped out the top 100 biggest websites on the internet. Examining the ranking reveals a lot about how people around the world search for information, which services they use, and how they spend time online.

Predict social and behavioral science results. Help improve scientific research methods. Earn rewards for your insights.

The impacts of COVID-19 and how we deal with them hinge on how politicians, firms and the public respond. What lessons can we learn from behavioural science about how we act in a time of crisis characterised by great uncertainty? What lessons can behavioural science learn about how it can be best placed to provide guidance in an uncertain world?

BC’s Restart Plan lays out a series of steps that we will take together to protect people and ensure that our province can come back from COVID-19 stronger than before.

In basic terms, online status indicators convey availability: whether someone is on or offline, or when they last logged into a particular app. But if you’ve ever anxiously awaited a response from a prospective partner or suspected your friend might be ignoring you, you’ll be painfully aware of just how much weight that indicator can actually hold. Now a new study has found that many users are not only aware of all that online status indicators can convey, but also change their behaviour accordingly. The research is due to be published in the Proceedings of the 2020 ACM conference on Human Factors in Computing Systems.

Psychologists will need to make some adjustments to testing children during the public health emergency.

Allyship is a growing phenomenon in many organizational contexts, and the involvement of allies in identity-based social movements (e.g., men in the feminist movement) is ubiquitous. However, the impression that these allies have on their intended beneficiaries is unclear. Over the course of three studies, we explore how different types of allyship behaviors are perceived by their beneficiaries. We find converging evidence that beneficiaries make critical judgments of their allies when their allies engage in actions that demonstrate lower levels of trustworthiness (e.g., selflessness, loyalty) and higher levels of influence (e.g., centrality, power) in the movement. This evidence was observed in a survey of 117 social movement activists (Study 1), and in two experiments sampling 752 liberal women and nonbinary individuals (Study 2), and 305 feminist social activists (Study 3). Taken together, our research documents the causal effects that different allyship behaviors have on beneficiaries’ attitudes toward allies (Studies 2 & 3) while recruiting samples of currently engaged movement activists to solicit their unique perspectives (Studies 1 & 3). We thereby identify the specific ways of being an ally that elicit the most positive impressions from their intended beneficiaries, which can reinforce intergroup coalitions, prosociality, and ultimately, downstream societal change.

Doing What Matters in Times of Stress: An Illustrated Guide is a stress management guide for coping with adversity. The guide aims to equip people with practical skills to help cope with stress. A few minutes each day are enough to practice the self-help techniques. The guide can be used alone or with the accompanying audio exercises.Informed by evidence and extensive field testing, the guide is for anyone who experiences stress, wherever they live and whatever their circumstances.   

Intergroup competitions such as democratic elections can intensify intergroup polarization and conflict. Partisan attitudes toward the elected leader can also shift following an election, but the biology underlying these attitudinal shifts remains unknown. An important factor could be the hormone testosterone, which is theorized to fluctuate during competition and to influence status-seeking. In a longitudinal study of 113 voters conducted during the 2012 US presidential election, supporters of the losing candidate experienced acute increases in testosterone levels on the evening of the election and flatter diurnal testosterone slopes up to two days after the election, compared to supporters of the winner. Furthermore, these competition-related changes in testosterone concentrations among supporters of the losing candidate were associated with less positive evaluations of the winning candidate. These findings suggest that hormonal responses to an intergroup competition may shape how we perceive elected leaders, shedding light on the biology of intergroup relations.

Feelings of anxiety are rising across the United States during the COVID-19 pandemic, and social distancing mandates preclude access to in-person mental healthcare. Anxiety is not necessarily pathological; however, increased worrying about COVID-19 shows links to more severe anxiety pathology. Greater perceived control over anxiety has predicted decreased anxiety pathology, including adaptive responses to uncontrollable stressors. Evidence suggests that no-therapist, single-session interventions can strengthen perceived control over emotions like anxiety; similar programs, if designed for the COVID-19 context, could hold substantial public health value. We will test whether a no-therapist, single-session online intervention adapted for the COVID-19 context can: 1) increase perceived control over anxiety, versus a placebo intervention 2) without decreasing social-distancing intentions. We will test these questions using a within-subjects design in a weighted-probability U.S. sample (N=300). Findings will inform whether a highly-scalable intervention can increase perceived control over anxiety without yielding unwanted secondary effects.

Im Folgenden finden Sie Einblicke in fortlaufend aktualisierte Ergebnisse Internationale Beiträge zu den Auswirkungen des Coronavirus auf Gesellschaft, Wirtschaft und Politik Deutsche Ergebnisse – Die Wahrnehmung aktueller Entwicklungen im Zeitverlauf

Durch die weitere Ausbreitung des Coronavirus weltweit wird die Bedrohung durch das Virus für die öffentliche Gesundheit häufiger als groß wahrgenommen, vor allem durch die älteren Befragten. Das zeigen YouGov-Umfragen von Februar und März im Vergleich

Welcome to the COVID-19 social science project tracker. This unofficial, community-driven, open initiative is led by Nate Matias

Angesichts der Corona-Pandemie hat sich der Alltag für viele Menschen in kurzer Zeit gravierend verändert. Wir wollen mit den folgenden Fragen herausfinden, welche Auswirkungen diese Veränderungen auf die Arbeit und das Familienleben haben und welche Sorgen sich die Menschen machen. Hierfür müssen möglichst viele und unterschiedliche Personen an dieser kurzen Befragung teilnehmen. Darum möchten wir Sie bitten, uns ein paar Fragen zu Ihrer aktuellen Situation zu beantworten und den Link zu dieser Befragung an möglichst viele Bekannte weiterzuleiten oder in Ihren sozialen Netzwerken zu teilen. Die Befragung wird etwa 11 Minuten dauern. Vielen Dank, dass Sie sich die Zeit nehmen! Klicken Sie auf Weiter um zur Einverständniserklärung zu gelangen.

In economics many articles are subjected to multiple rounds of refereeing at the same journal, which generates time costs of referees alone of at least $50 million. This process leads to remarkably longer publication lags than in other social sciences. We examine whether repeated refereeing produces any benefits, using an experiment at one journal that allows authors to submit under an accept/reject (fast-track or not) or the usual regime. We evaluate the scholarly impacts of articles by their subsequent citation histories, holding constant their sub-fields, authors’ demographics and prior citations, and other characteristics. There is no payoff to refereeing beyond the first round and no difference between accept/reject articles and others. This result holds accounting for authors’ selectivity into the two regimes, which we model formally to generate an empirical selection equation. This latter is used to provide instrumental estimates of the effect of each regime on scholarly impact.

The Center for Open Science and the Web of Science Group, a Clarivate Analytics company, are collaborating to incorporate TOP Factor among the information and metrics available through participating journals in its Master Journal List.  The Master Journal List allows researchers, publishers and librarians to keep track of the publication landscape. The online list is a go-to place for researchers to explore which journals are included in the Web of Science, and to help them identify suitable publishing destinations for their research. Now, further assisting researchers in identifying the transparent and open practices of journals by incorporating TOP Factor data. Based on the Transparency and Openness Promotion (TOP) Guidelines, TOP Factor rates journals on the degree to which their policies implement open data, research design transparency, replication studies, and address publication bias.  “We are delighted that the Web of Science Group will be including TOP Factor in its journal information pages on the Master Journal List,” says David Mellor, Director of Policy Initiatives at the Center for Open Science. “Diversifying the information that is available about scientific journals benefits authors, readers, publishers, and editors by providing specific practices that are important to the scientific community.” As part of its mission to increase the transparency and reproducibility of scientific research, the Center for Open Science is working to showcase steps being taken by the scientific community to tackle these issues. This includes not only pointing out best practices, but also pointing out specific recommendations for improvement.  “There is a lot of room for improvement in the publishing community,” says Mellor, “but the good news is that TOP Factor lets the community see that small steps can be easily taken toward more transparency. And the bigger steps covered by the more ambitious policies covered in the TOP Guidelines are also being taken by journals across several disciplines. This can serve as an inspiration that these practices are not pie in the sky dreams but are, in fact, achievable.” TOP Factor and its data is a publicly available resource, and COS encourages its use by all parties who wish to recognize and promote transparent and open practices. Take a moment to view the TOP Factor integration on the Master Journal List here.

Introduction The performance of modern Question Answering Models (BERT, ALBERT ...) has seen drastic improvements within the last year enabling many new opportunities for accessing information more efficiently. However, those models are designed to find answers within rather small text passages. Haystack lets you scale QA models to large collections of documents! While QA is the focussed use case for haystack, we will soon support additional options to boost search (re-ranking, most-similar search ...). Haystack is designed in a modular way and lets you use any models trained with FARM or Transformers. Core Features Powerful ML models: Utilize all latest transformer based models (BERT, ALBERT, RoBERTa ...) Modular & future-proof: Easily switch to newer models once they get published. Developer friendly: Easy to debug, extend and modify. Scalable: Production-ready deployments via Elasticsearch backend & REST API Customizable: Fine-tune models to your own domain & improve them continuously via user feedback Components DocumentStore: Database storing the documents for our search. We recommend Elasticsearch, but have also more light-weight options for fast prototyping (SQL or In-Memory). Retriever: Fast, simple algorithm that identifies candidate passages from a large collection of documents. Algorithms include TF-IDF or BM25, custom Elasticsearch queries, and embedding-based approaches. The Retriever helps to narrow down the scope for Reader to smaller units of text where a given question could be answered. Reader: Powerful neural model that reads through texts in detail to find an answer. Use diverse models like BERT, RoBERTa or XLNet trained via FARM or Transformers on SQuAD like tasks. The Reader takes multiple passages of text as input and returns top-n answers with corresponding confidence scores. You can just load a pretrained model from Hugging Face's model hub or fine-tune it to your own domain data. Finder: Glues together a Reader and a Retriever as a pipeline to provide an easy-to-use question answering interface. REST API: Exposes a simple API for running QA search, collecting feedback and monitoring requests Labeling Tool: Hosted version (Beta), Docker images (coming soon)

he best responses to COVID-19 involve behavior change, including social distancing, mask use, and, eventually, vaccine uptake. So why has the behavioral public policy community, encompassing governmental behavioral insights units and a variety of not-for-profit and private organizations, played a marginal role?

Increasing evidence suggests that, similar to face-to-face communications, human emotions also spread in online social media. However, the mechanisms underlying this emotion contagion, for example, whether different feelings spread in unlikely ways or how the spread of emotions relates to the social network, is rarely investigated. Indeed, because of high costs and spatio-temporal limitations, explorations of this topic are challenging using conventional questionnaires or controlled experiments. Because they are collection points for natural affective responses of massive individuals, online social media sites offer an ideal proxy for tackling this issue from the perspective of computational social science. In this paper, based on the analysis of millions of tweets in Weibo, surprisingly, we find that anger travels easily along weaker ties than joy, meaning that it can infiltrate different communities and break free of local traps because strangers share such content more often. Through a simple diffusion model, we reveal that weaker ties speed up anger by applying both propagation velocity and coverage metrics. To the best of our knowledge, this is the first time that quantitative long-term evidence has been presented that reveals a difference in the mechanism by which joy and anger are disseminated. With the extensive proliferation of weak ties in booming social media, our results imply that the contagion of anger could be profoundly strengthened to globalize its negative impact.

The rapid spread of the coronavirus and the strategies to slow it have disrupted just about every aspect of our lives. Such disruption may be reflected in changes in psychological function. The present study used a pre-posttest design to test whether Five Factor Model personality traits changed with the coronavirus outbreak in the United States. Participants (N=2,137) were tested in early February 2020 and again during the President’s 15 Days to Slow the Spread guidelines. In contrast to the preregistered hypotheses, Neuroticism decreased across these six weeks, particularly the facets of Anxiety and Depression, and Conscientiousness did not change. Exploratory analyses indicated that quarantine/isolation status moderated change such that Neuroticism only decreased for those not in quarantine, whereas Openness, Agreeableness, and Conscientiousness declined for participants in quarantine. The present research suggests modest changes in personality traits across the acute phase of the coronavirus outbreak.

With the availability of cell phones, internet, social media etc. the interconnectedness of people within most societies has increased drastically over the past three decades. Across the same timespan, we are observing the phenomenon of increasing levels of fragmentation in society into relatively small and isolated groups that have been termed filter bubbles, or echo chambers. These pose a number of threats to open societies, in particular, a radicalisation in political, social or cultural issues, and a limited access to facts. In this paper we show that these two phenomena might be tightly related. We study a simple stochastic co-evolutionary model of a society of interacting people. People are not only able to update their opinions within their social context, but can also update their social links from collaborative to hostile, and vice versa. The latter is implemented such that social balance is realised. We find that there exists a critical level of interconnectedness, above which society fragments into small sub-communities that are positively linked within and hostile towards other groups. We argue that the existence of a critical communication density is a universal phenomenon in all societies that exhibit social balance. The necessity arises from the underlying mathematical structure of a phase transition phenomenon that is known from the theory of a kind of disordered magnets called spin glasses. We discuss the consequences of this phase transition for social fragmentation in society.

Case isolation and contact tracing is a widely-used intervention method for controlling epidemic outbreaks. Here, we argue that the effectiveness of contact tracing and isolation is likely underestimated by existing studies because they do not take into account the different forms of heterogeneity and sampling biases from the network structure. Specifically, we show that contact tracing can be even more effective than acquaintance sampling at locating hubs. Our results call for the need for contact tracing to go both backward and forward, in multiple steps, to leverage all forms of positive biases. Using simulations on networks with a power-law degree distribution, we show that this deep contact tracing can potentially prevent almost all further transmissions even at a small probability of detecting infected individuals. We also show that, when the number of traced contacts is small, the number of prevented transmission per traced node is even higher---although most traced individuals are healthy---than that from case isolation without contact tracing. Our results also have important consequences for new implementations of digital contact tracing and we argue backward and deep tracing can be incorporated without the important sacrificing privacy-preserving requirements of these new platforms.

The new coronavirus disease (COVID-19) threatens the lives of millions of people around the world, making it the largest health threat in recent times. Billions of people around the world are asked to adhere to strict shelter-in-place rules, finalised to slow down the spread of the virus. Appeals and messages are being used by leaders and policy-makers to promote pandemic response. Given the stakes at play, it is thus important for social scientists to explore which messages are most effective in promoting pandemic response. In fact, some papers in the last month have explored the effect of several messages on people’s intentions to engage in pandemic response behaviour. In this paper, we make two contributions. First, we explore the effect of messages on people’s actual engagement, and not on intentions. Specifically, our dependent variables are the level of understanding of official COVID-19 pandemic response governmental informative panels, measured through comprehension questions, and the time spent on reading these rules. Second, we test a novel set of appeals built through the theory of norms. One message targets the personal norm (what people think is the right thing to do), one targets the descriptive norm (what people think others are doing), and one targets the injunctive norm (what people think others approve or disapprove of). Our experiment is conducted online with a representative (with respect to gender, age, and location) sample of Italians. Norms are made salient using a flier. We find that norm-based fliers had no effect on comprehension and on time spent on the panels. These results suggest that norm-based interventions through fliers have very little impact on people’s reading and understanding of COVID-19 pandemic response governmental rules.

The desire for a meaningful life is ubiquitous, and those who find their lives meaningful display superior mental and physical health. Yet the lay concept of a meaningful life is poorly understood. We investigated whether lay people think that meaningfulness depends on one’s psychological states or on the objective conditions of one’s life. Amongst academic philosophers, subjectivists argue that the former, and objectivists argue that latter, is necessary for meaning, while hybrid theorists claim that both conditions must be met for a life to be meaningful. Study 1 found that laypeople do not hold any of these three views. Instead, they consider a life meaningful if it meets either condition. Study 2 extended this finding, showing that positive psychological states can increase attributions of meaning even when those states are derived from senseless activities. However, Study 3 showed that severe immorality, while it does not reduce the incremental effect of fulfillment, can keep a life from being meaningful. These findings reveal that neither psychologists nor philosophers have previously understood meaning in the way that laypeople do.

Vast majority of 750,000 people who signed up to help are yet to be called into action

This study investigates change in students’ social networks and mental health at the time of the COVID-19 crisis in April 2020. We surveyed multiple dimensions of social networks (pleasant interaction, friendship, social support, co-studying) and mental health indicators (depression, anxiety, stress, loneliness) before and during the crisis among Swiss undergraduate students (N=212). We find that interaction and co-studying networks had become sparser, and more students were studying alone. Furthermore, students’ levels of stress, anxiety, loneliness, and depressive symptoms got worse. Stressors shifted from fears of missing out on social life to worries about health, family, friends, and their future. Exploratory analyses suggest that COVID-19 specific worries, isolation in social networks, lack of interaction and emotional support, and physical isolation were associated with negative mental health trajectories. The results offer starting points to identify and support students at higher risk of social isolation and negative psychological effects during the COVID-19 pandemic.

As cases of COVID-19 escalate exponentially, the British Psychological Society and psychologists from across the globe are working to combat the pandemic and manage its implications for everyday life.

Our goal is to help accelerate scientific research, with tools to visualize the emerging literature network around COVID-19. Use our exploratory search tools to find out what groups are working on what directions, see how biomedical concepts interact and evolve over time, and discover new connections.

WIPO today launched a new tool that tracks COVID-19 related intellectual property (IP) policy changes or other measures being implemented by WIPO member states in their response to the global pandemic. This is the latest in a series of measures taken by the Organization in relation to the COVID-19 pandemic.

In response to the COVID-19 pandemic, the White House and a coalition of leading research groups have prepared the COVID-19 Open Research Dataset (CORD-19). CORD-19 is a resource of over 59,000 scholarly articles, including over 47,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses. This freely available dataset is provided to the global research community to apply recent advances in natural language processing and other AI techniques to generate new insights in support of the ongoing fight against this infectious disease. There is a growing urgency for these approaches because of the rapid acceleration in new coronavirus literature, making it difficult for the medical research community to keep up.

For families eager for schools to throw open their doors, the tale of a 9-year-old British boy who caught COVID-19 in the French Alps in January offers a glimmer of hope. The youngster, infected by a family friend, suffered only mild symptoms; he enjoyed ski lessons and attended school before he was diagnosed. Astonishingly, he did not transmit the virus to any of 72 contacts who were tested. His two siblings didn’t become infected, even though other germs spread readily among them: in the weeks that followed, all three had influenza and a common cold virus.

The evolving COVID-19 pandemic has created an urgent need for scientific evidence, and quickly. We need politicians to be able to make informed decisions, and we need to support the development of effective vaccines and treatments, as well as understanding the unfolding impact of the pandemic on society. The speed with which the global scientific community has risen to this sudden pressing need is remarkable.

Dear Editor, In this response, I take issue with the pessimistic view of Devi Sridhar and Maimuna Majumder [1] on axiomatic modeling in epidemiology and defend this way of informing nonpharmaceutical interventions. In "Modelling the pandemic," the authors claim that our current mitigation and suppression measures deployed to cope with the SARS-Cov-2 outbreak are guided by (and over-rely on) mathematical models. They highlight the reliance of mathematical models on certain assumptions and point out at the value-ladenness of decisions regarding the inclusion or exclusion of factors from such models. As a solution, the authors advise making both data and code publicly available to ensure replicability and supporting modeling with other types of evidence such as case studies of policies implemented in other countries, anecdotal evidence from frontline healthcare workers, and studies of previous epidemics.

TikTok is a video-sharing social networking service, whose popular-ity is increasing rapidly. It was the world’s second-most downloadedapp in 2019. Although the platform is known for having users danc-ing, lip-syncing and showing off their talents, there is an increasein videos designed to express political opinions. In this study, weperform the first evaluation of political communication on this plat-form. We collect a set of US Republican and Democratic partisanvideos and investigate how users communicate with each other.With the help of computer vision, natural language processing,and statistical tools, we illustrate that political communication ismuch more interactive on TikTok in contrast to other social mediaplatforms, with users combining multiple information channelsto spread their messages. We show that political communicationtakes place in the form of communication trees since users generatebranches of responses on existing content. Finally, we investigateuser demographics and their interactions with opposing views. Wefind that partisan users from both parties are young and behavesimilarly on the platform. We also find that Republican users gener-ate more political content, and their videos receive more reactions.However, Democratic partisans engage significantly more in cross-partisan discussions.

2004.05478v1

Globe-trotting humans were the culprits 

In light of the COVID-19 pandemic it may be useful to explore how resilience can be promoted for teachers at this time – supporting positive adaptation in the face of significant challenge.Research exploring teacher resilience has looked at ways that resilience can be promoted and eroded over time4. This suggests that resilience is a process as opposed to an internal trait. Resilience can change over time depending on the context or situation – it is not a case of having or not having resilience. Therefore, factors known to promote resilience in teachers can be fostered and drawn upon in the current climate. This short paper is a quick reference guide and a ‘conversation starter’ designed to promote teacher resilience. The aim is not to be prescriptive as we know that teachers demonstrate resilience on a daily basis in their busy lives in the profession. We also know that resilience is often community-focused and therefore support is often best placed within pre-existing relationships. We hope this paper provides a framework, informed by psychology, to support what is likely to be happening in some capacity already within educational organisations in the UK. The suggestions that we make in this paper are based on evidence from research and practice.

There are several opinion dynamics models where extremism is defined as part of their characteristics. However, the way extremism is implemented in each model does not correspond to equivalent definitions. While some models focus on one aspect of the problem, others focus on different characteristics. This paper shows how each model only captures part of the problem and how Bayesian inspired opinion models can help put those differences in perspective. That discussion suggests new ways to introduce variables that can represent the problem of extremism better than we do today. Keywords: Extremism, Opinion dynamics, CODA, Sociophysics

Multilayer networks have been widely used to represent and analyze systems of interconnected entities where both the entities and their connections can be of different types. However, real multilayer networks can be difficult to analyze because of irrelevant information, such as layers not related to the objective of the analysis, because of their size, or because traditional methods defined to analyze simple networks do not have a straightforward extension able to handle multiple layers. Therefore, a number of methods have been devised in the literature to simplify multilayer networks with the objective of improving our ability to analyze them. In this article we provide a unified and practical taxonomy of existing simplification approaches, and we identify categories of multilayer network simplification methods that are still underdeveloped, as well as emerging trends.

The always increasing mobile connectivity affects every aspect of our daily lives, including how and when we keep ourselves informed and consult news media. By studying a DPI (deep packet inspection) dataset, provided by one of the major Chilean telecommunication companies, we investigate how different cohorts of the population of Santiago De Chile consume news media content through their smartphones. We find that some socio-demographic attributes are highly associated to specific news media consumption patterns. In particular, education and age play a significant role in shaping the consumers behaviour even in the digital context, in agreement with a large body of literature on off-line media distribution channels.

Network theory provides tools which are particularly appropriate for assessing the complex interdependencies that characterise our modern connected world. This article presents an introduction to network theory, in a way that doesn't require a strong mathematical background. We explore how network theory unveils commonalities in the interdependency profiles of various systems, ranging from biological, to social, and artistic domains. Our aim is to enable an intuitive understanding while conveying the fundamental principles and aims of complexity science. Additionally, various network-theoretic tools are discussed, and numerous references for more advanced materials are provided.

This study examines the mental health outcomes among the healthcare personnel and the general population and the role of socioeconomic status. Eight hundred and eighty-four (884) residents in Nigeria comprising 382 Healthcare Personnel and 502 general residents aged between 18 and 78 years (M = 28.75, SD = 8.17) responded to an online survey with measures of Impact of Event Scale-Revised (IES-R), Generalized Anxiety Disorder (GAD-7), Patient Health Questionnaire (PHQ–9) and Insomnia Severity Index. Collected data were subjected to statistical analysis using the SPSS v.25. Results revealed significant difference in the prevalence of depressive symptoms (χ2 = 14.26; df = 4; p <0.01); insomnia symptoms (χ2 = 40.21; df = 3; p< 0.01); posttraumatic stress symptoms (χ2 = 08.34; df = 3; p< 0.05) and clinical anxiety symptoms (χ2 = 06.71; df = 1; p <0.05) among healthcare personnel and the general population, with a higher prevalence reported by the healthcare personnel. Further, socioeconomic status significantly influence prevalence of depressive symptoms (χ2 = 04.5; df = 4; p <0.05). The study concluded that the prevalence of poor mental health outcomes during the COVID-19 crisis among Nigerians is worrisome. Also, the socioeconomic status of the citizens has serious implications on depressive symptoms. It was recommended that better attention should be paid to the mental health of residents in Nigeria, most especially healthcare workers during the COVID-19 pandemic.

This study examined the psychological distress experience of Nigerians amid COVID-19 pandemic. From March 20, 2020 to April 12, 2020, this descriptive survey used a snowballing sampling technique to select 502-Nigerian with an online semi-structured questionnaire containing Impact of Event Scale-Revised, GAD-7, Patient Health Questionnaire and Insomnia Severity Index. Gender had insignificant difference in the level of insomnia (χ2=04.93; df=3; p>0.05), however, 20.8% of males had sub-threshold of insomnia, 8.2% experienced moderate insomnia and 5.9% had severe insomnia; 32% females reported sub-threshold of insomnia, 12.4% had moderate insomnia while, 3.6% had severe insomnia. Also, gender had insignificant difference in the measures of depression (χ2=01.94; df=4; p>0.05); 55.4% males reported minimal depression, 22.3% had mild depression, 11.9% had moderate depression; 6.7% to 3.7% males had moderately to severe depression while, 49.3% of the females had minimal depression, 26.7% reported mild depression, 14.29% had moderate depression, 4.4% to 5.3% had moderately to severe depressive symptoms. Posttraumatic Stress Symptoms (PTSS) has no gender difference among respondents (χ2=02.51; df =3; p>0.05); 23% of males reported partial PTSS, 17.5% presented clinical PTSS, and 21.6% males had severe PTSS; while 29.3% of females had severe PTSS, 24% reported partial PTSS and 18.7% had clinical PTSS. Respondents reported insignificant gender difference on anxiety (χ2=0.08; df=1; p>0.05), while 51% reported moderate anxiety and 49% exhibited severe anxiety during COVID-19 pandemic in Nigeria. Findings revealed that Nigerians experienced psychological distress during COVID-19 pandemic. Government and stakeholders in health sectors should provide psychological health services for the residents in Nigeria.

Naive observers can and do make a wide range of social judgments from static images of faces. These include evaluations of properties like trustworthiness and competence, but can also include estimations of mental health (e.g. depression, schizotypy). This latter class of judgments is particularly important insofar as these evaluations may inform the interactions between care providers and patients, and could potentially provide diagnostic value if they prove to be veridical. In a previous report, Kleiman and Rule (2013) demonstrated that naive observers were capable of classifying static images at above chance levels according to suicidality: The faces of individuals who had died by suicide could be distinguished from the faces of those who had not. Further, the authors also reported that the faces of individuals who died by suicide were perceived as more impulsive than control faces. Our goals in the current study were to carry out a replication of these important experiments with a larger participant sample (more than doubling the sample used in the original report) and to extend the original report by implementing a 2AFC paradigm that minimizes response bias. We found that while naive observers could categorize faces according to suicidality at an above-chance level using the original testing paradigm, performance dropped to chance in a 2AFC discrimination task. We also did not find evidence supporting differences in perceived impulsivity across categories in either paradigm. We discuss these outcomes from theoretical and methodological perspectives, and outline new directions for further research investigating social vision in clinical contexts.

People infer a number of traits about others’ based simply on facial appearance. Even when inaccurate, face-based trait impressions can have important behavioral consequences including voting behavior and criminal sentencing. Thus, understanding how perceivers infer others’ traits is an important social and psychological issue. Recent evidence suggests that face-based trait impressions may vary by culture. In the present work, we attempt to understand cultural differences in face-based trait impressions. As part of the Psychological Science Accelerator’s Secondary Data Analysis Challenge, we report a set of pre-registered analyses testing how cultural differences in present-day diversity relate to a) 13 face-based trait impressions, b) sensitivity to physical features of the face, c) and the mental structure underlying trait impressions.We findthat greater present-day diversity might be related to lower trustworthiness ratings, in particular. We discuss this finding in the context of other recent work and suggest further analysis of the mental structure of face-based trait impressions across cultures.

This study anonymously screened 13,332 individuals worldwide for psychological symptoms related to Corona virus disease 2019 (COVID-19) pandemic from March 29th to April 14th, 2020. A total of n=12,817 responses were considered valid with responses from 12 featured countries and five WHO regions. Female gender, pre-existing psychiatric condition, and prior exposure to trauma were identified as notable risk factors, whereas optimism, ability to share concerns with family and friends like usual, positive prediction about COVID-19, and daily exercise predicted fewer psychological symptoms. These results could aid in dynamic optimization of mental health services during and following COVID-19 pandemic.

Social distancing is the single most effective method to reduce the spread of COVID-19. As such, researchers across varying fields are currently attempting to identify the variables that predict social distancing and which interventions can heighten social distancing. Yet, much of this research relies on self-report measures (in part because of social distancing guidelines themselves). In two studies we examine whether self-reported social distancing overlaps with real-world behavior. In Study 1, individuals’ self-reported social distancing predicted decreased movement as quantified by participants’ average daily step-counts (assessed via smartphone pedometers). In Study 2, the degree of self-reported social distancing in different U.S. States predicted the degree to which people in those States reduced their overall movement and travel to non-essential retail as assessed by ~17 million smart-phone GPS coordinates. Collectively, our results indicate that self-report measures of social distancing track actual behavior both at the individual and at the group level.

As social robots are introduced into increasingly more health interventions, one potential area where they might prove valuable is in supporting people's psychological health through conversation. Given the importance of self-disclosure for psychological health, this study assessed the viability of using social robots for eliciting rich disclosures that identify needs and emotional states in human interaction partners. Three within-subjects experiments were conducted with participants interacting with another person, a humanoid social robot, and a disembodied conversational agent (voice assistant). We performed a number of objective evaluations of disclosures to these three agents via speech content and voice analyses, and also probed participants' subjective evaluations of their disclosure to the three agents. Our findings suggest that participants overall disclose more to humans than artificial agents, that agents' embodiment influences disclosure quantity and quality, and that people are generally aware of differences in their personal disclosures to the three agents studied here. Together, the findings set the stage for further investigation into the psychological underpinnings of self-disclosures to robots and their potential role in eliciting disclosures as part of mental and physical health interventions.

With the COVID-19 pandemic resulting in social distancing recommendations many service providers find themselves altering the way they must provide medically necessary therapy. Even with the advent of more advanced telehealth technology, implementation of behavioral programming falls mainly on the caregivers of the clients that are served. This crisis brings to question ethical dilemmas and upends the current ways many programs may have been implemented across the world. As a result, a re-evaluation of how these services are delivered is in order. This paper reviews how a University-Based, State-funded Service Delivery Program (USSDP) provided essential and necessary services during the COVID-19 pandemic. Specifically, the purpose of this paper is to describe how the USSDP quickly adopted a telehealth care model in a program that previously had not delivered services in this modality. Ethical, contextual, and competency-based factors are reviewed in the context of this organization followed by a dialogue on broader generalization suggestions utilizing an active support model of care within telehealth restrictions.

How does the order of individuals' interactions affect the emergence of shared conventions at the population level? The answer to this question is relevant for a number of fields, such as cultural evolution, linguistics, cognitive science or behavioral economics. In this study we investigate experimentally how two different network connectivity dynamics affect the evolution of the diversity of cultural variants of the communication system. We report an experiment in the lab in which participants engage in a Pictionary-like graphical communication task as members of a 4-participant micro-society, interacting in pairs with the other three members of the community across 4 rounds. The experiment has two main goals: First, to evaluate the effect of two network connectivity dynamics (early and late) on the evolution of the convergence of micro-societies on shared communicative conventions under controlled conditions. Second, to compare the predictions of the agent-based model described in a previous study (Segovia-Martín, Walker, Fay, & Tamariz, 2019) against experimental data, and calibrate the model to find the best-fitting parameter setting. Our experimental data shows that, as predicted by the model, an early connectivity dynamic increases convergence and a late connectivity dynamic slows down convergence. We found significant differences between conditions in round 3 and round 4. We estimate the best-fit parameter combination for the 96 data structures coded. Medium to high content bias, neutral to egocentric coordination bias and memory size of 3 rounds was associated with a better model fit. In the light of the model evaluation and the experiment outcome, we discuss the impact of our predictions on social influence research and possible factors that might help to improve model precision.

Noncompliance with social distancing during the early stage of the COVID-19 pandemic poses a great challenge to disease containment and adds more demands on public healthcare resources. These noncompliance behaviors may partly reflect people’s concerns for the inherent costs of social distancing while discounting its public health benefits. We propose that this oversight may be associated with the limitation in one’s mental capacity to simultaneously retain multiple pieces of information in working memory (WM) for rational decision making leading to social distancing compliance. We tested this hypothesis in 850 U.S. residents during the first 2 weeks following the presidential declaration of national emergency because of COVID-19. We found that participants’ social distancing compliance at this initial stage could be predicted by individual differences in WM capacity, partly due to increased awareness of benefits over costs of social distancing among higher WM capacity individuals. Critically, the unique role of WM capacity in social distancing compliance could not be explained by other psychological and socioeconomic factors (e.g., moods, personality, education and income levels) underlying the compliance with a set of rules for social interactions (i.e., social norms). Furthermore, the critical role of WM capacity in social distancing compliance can be generalized to the compliance with another social norm, namely the fairness norm, in Western cultures. Collectively, our data reveal novel contributions of a core cognitive process underlying social distancing compliance during the early outbreak of the COVID-19 pandemic, highlighting a potential cognitive venue for developing strategies to mitigate a public health crisis.

Amidst an outbreak of COVID-19 in January 2020, this study, which based on the behavioral immune system, focused on the preventive behaviors related to COVID-19 infection and Japanese people’s exclusionary attitude toward foreigners, examining the time-series changes associated with the spread of the novel coronavirus in Japan, which was one of the first countries to confirm cases outside China. Also, we examined the effects of individual differences in infection-avoidance tendencies and the frequency of people’s daily contact with foreigners. In late January, mid-February, and early March of 2020, this study conducted a panel survey 1,004 Japanese citizens aged 18 years or above living in Japan, and who had registered with the crowdsourcing service. The results indicated that as the spread of COVID-19 increased, tendencies toward infection-preventive behaviors increased, and exclusionary attitudes toward foreigners became stronger. Similarly, the avoidance response against unfamiliar people, including Japanese strangers increased. No relationship between the increased risk of infection and the Japanese respondents’ exclusionary attitudes toward the Chinese but were more negative than that for other groups. However, it is difficult to conclude that all the reported exclusionary behaviors and attitudes were irrational or excessive false-positive errors. People with strong infection-avoidance tendencies engaged in more preventive actions, regardless of whether they were living under normal circumstances or under threat of infection, and they indicated strong exclusionary attitudes toward foreigners in general and the Chinese specifically under threat of infection. We observed a moderating that the higher the frequency of daily contact with foreigners, the weaker the exclusionary attitudes toward them. This study recorded the behavioral and psychological states of people in Japan during the 40 days preceding the WHO’s 11 March 2020 declaration of the COVID-19 pandemic. The data collected in Japan, where the infection spread widely early on, will provide valuable insights for countries anticipating significant social changes.

Why are open people open? A recent theory suggests that openness/intellect reflects sensitivity to the reward value of information, but so far this has undergone few direct tests. To assess preferences for information, we constructed a novel task, adapted from information-seeking paradigms within decision science, in which participants could choose to see information related to a guessing game they had just completed. Across two studies (one exploratory, n = 151; one confirmatory, n = 301), openness/intellect did not predict information-seeking. Our results thus do not support a straightforward version of the theory, whereby open individuals display a general-purpose sensitivity to any sort of new information. However, trait curiosity (arguably a facet of openness/intellect) predicted information-seeking in both studies, and uncertainty intolerance (inversely related to openness/intellect) predicted information-seeking in Study 2. Thus, it is possible that the domain-level null association masks two divergent information-seeking pathways, one approach-motivated (curiosity), and one avoidance-motivated (uncertainty intolerance). It remains to be seen whether these conflicting motivations can be isolated, and if doing so reveals any association between information-seeking and the broader openness/intellect domain.

Background Worldwide, the vast majority of people have been impacted by COVID-19. While millions of individuals have become infected, billions of individuals have been asked or required by local and national governments to change their behavioral patterns. Previous research on epidemics or traumatic events suggest this can lead to profound behavioral and mental health changes, but rarely are researchers able to track these changes with frequent, near real-time sampling or compare these to previous years of data on the same individuals. Objectives We seek to answer two overarching questions by combining mobile phone sensing and self-reported mental health data among college students participating in a longitudinal study for the past two years. First, have behaviors and mental health changed in response to the COVID-19 pandemic as compared to previous time periods within the same participants? Second, did behavior and mental health changes track the relative news coverage of COVID-19 in the US media? Methods Behaviors were measured using the StudentLife mobile smartphone sensing app. Depression and anxiety were assessed using weekly self-reported Ecological Momentary Assessments (EMAs). Differences in behaviors and self-reported mental health collected during the Winter 2020 term (the term in which the coronavirus pandemic started), as compared to prevous terms in the same cohort, were modeled using mixed linear models. Results During the initial COVID-19 impacted academic term (Winter 2020), individuals were more sedentary and reported increased anxiety and depression symptoms (P<.001), relative to the previous academic terms and subsequent academic breaks. Interactions between the Winter 2020 term and week of academic term (linear and quadratic) were significant. In a mixed linear model, phone usage, number of locations visited, and week of the term, were strongly associated with increased coronavirus-related news. When mental health metrics (e.g., depression and anxiety) were added to the previous measures (week of term, number of locations visited, and phone usage), both anxiety (P<.001) and depression (P<.05) were significantly associated with coronavirus-related news. Conclusions Compared with prior academic terms, individuals in Winter 2020 were more sedentary, anxious, and depressed. A wide variety of behaviors, including increased phone usage, decreased physical activity, and fewer locations visited, are associated with fluctuations in COVID-19 news reporting. While this large-scale shift in mental health and behavior is unsurprising, its characterization is particularly important to help guide the development of methods that could reduce the impact of future catastrophic events on the mental health of the population.

The spread of COVID-19 is serious, threatening not only health but also life worldwide. To resolve the crisis, governments need to encourage citizens to voluntarily change their behaviour, such as social distancing and self-restraint. This is especially important in those countries where governments cannot stop people going out, congregating, or attending events, or lock down a city legally, as is the case in Japan. Previous research on social cognition has suggested that emphasising self-identity is key to changing a person's behaviour. This study will examine whether reminders that highlight self-identity are effective in controlling behaviour related to the spread of COVID-19: Will those putting greater emphasis on self-identity (‘Don’t be a spreader’) inhibit high-risk behaviours related to infection control better than those using less (‘Don’t spread’)? A two-wave survey of the same participants will be conducted with a one-week interval, during which one of three reminder conditions will be assigned: ‘Don’t spread’ (spreading condition), ‘Don’t be a spreader’ (spreader condition), and no reminder (control condition). Participants will mark their responses to items related to the Japanese Ministry of Health, Labour and Welfare guidelines and the number of times per week they go out on a COVID-19 infection prevention scale. Based on the findings, effective and practical ways of designing reminders that encourage people to change their behaviour to prevent the COVID-19 pandemic will be suggested.

A hallmark of science is the open exchange of knowledge. At this time of crisis, it is more important than ever for scientists around the world to openly share their knowledge, expertise, tools, and technology. Scientific models are critical tools for anticipating, predicting, and responding to complex biological, social, and environmental crises, including pandemics. They are essential for guiding regional and national governments in designing health, social, and economic policies to manage the spread of disease and lessen its impacts. However, presenting modeling results alone is not enough. Scientists must also openly share their model code so that the results can be replicated and evaluated.Given the necessity for rapid response to the coronavirus pandemic, we need many eyes to review and collectively vet model assumptions, parameterizations, and algorithms to ensure the most accurate modeling possible. Transparency engenders public trust and is the best defense against misunderstanding, misuse, and deliberate misinformation about models and their results. We need to engage as many experts as possible for improving the ability of models to represent epidemiological, social, and economic dynamics so that we can best respond to the crisis and plan effectively to mitigate its wider impacts.We strongly urge all scientists modeling the coronavirus disease 2019 (COVID-19) pandemic and its consequences for health and society to rapidly and openly publish their code (along with specifying the type of data required, model parameterizations, and any available documentation) so that it is accessible to all scientists around the world. We offer sincere thanks to the many teams that are already sharing their models openly. Proprietary black boxes and code withheld for competitive motivations have no place in the global crisis we face today. As soon as possible, please place your code in a trusted digital repository (1) so that it is findable, accessible, interoperable, and reusable (2).

The first-line epidemiological response to coronavirus disease 2019 (COVID-19) requires estimation of key parameters, including case fatality risk, and reproduction number, to monitor and predict the probable course of the pandemic. The challenge for public health scientists is that these data are partly a function of testing coverage. The number of deaths attributed to COVID-19 is problematic because the criteria for defining a death almost certainly depends on whether the death occurs in somebody who tested positive for severe acute respiratory syndrome coronavirus 2. Furthermore, an unknown fraction of the cases classified as COVID-19 deaths had underlying health conditions and were probably already at an increased risk of death. Although some countries tend to attribute to COVID-19 most deaths of people who had the virus, others might tend to register other causes of death in the presence of major chronic diseases, even if the deceased person had COVID-19.As the pandemic progresses, consistent measurement of its scale, across time and space, should be a priority. Objective and comparable data are crucial to determine the effectiveness of different national strategies used to mitigate and suppress, and thus to better prepare for the probable continuation of, the epidemic over the next year or more. For the reasons outlined above, the metrics on incidence and fatality have shortcomings that make such comparisons problematic.

The coronavirus disease 2019 (COVID-19) outbreak that originated in Wuhan, China has rapidly propagated due to widespread person-to-person transmission and has resulted in over 1,133,758 cases in 197 countries with a total of 62,784 deaths as of April 5, 2020. Laboratory confirmation of SARS-CoV-2 is performed with a virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test. This test can take up to two days to complete, and, due to the possibility of false negatives, serial testing may be required to reliably exclude infection. A current supply shortage of RT-PCR test kits compounds the shortcomings of entrusting diagnosis to the PCR test alone and underscores the urgent need to provide alternative methods for the rapid and accurate diagnosis of SARS-CoV-2 patients. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value to fully exclude infection, because of the normal radiologic findings in some early disease patients. In this study, we use artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history, and/or laboratory testing to more accurately and rapidly diagnose SARS-CoV-2 (+) patients. We included 905 RT-PCR confirmed patients. 419 (46.2%) tested positive for SARS-CoV-2 by laboratory-confirmed real-time RT-PCR assay and next-generation sequencing, while 486 patients (53.8%) tested negative (confirmed by at least two additional negative RT-PCR tests and clinical observation). The proposed AI system achieved an AUC of 0.92 and performed equally well in sensitivity compared to a senior thoracic radiologist on a testing set of 279 cases. The AI system also improved the detection of RT-PCR positive SARS-CoV-2 patients who presented with normal CTs, correctly identifying 17/25 (68%) patients, whereas all 25 RT-PCR SARS-CoV-2-positive CT-normal patients were classified as SARS-CoV-2 negative by radiologists.

Understanding the temporal dynamics of COVID-19 patient phenotypes is necessary to derive fine-grained resolution of pathophysiology. Here we use state-of-the-art deep neural networks over an institution-wide machine intelligence platform for the augmented curation of 15.8 million clinical notes from 30,494 patients subjected to COVID-19 PCR diagnostic testing. By contrasting the Electronic Health Record (EHR)-derived clinical phenotypes of COVID-19-positive (COVIDpos, n=635) versus COVID-19-negative (COVIDneg, n=29,859) patients over each day of the week preceding the PCR testing date, we identify anosmia/dysgeusia (37.4-fold), myalgia/arthralgia (2.6-fold), diarrhea (2.2-fold), fever/chills (2.1-fold), respiratory difficulty (1.9-fold), and cough (1.8-fold) as significantly amplified in COVIDpos over COVIDneg patients. The specific combination of cough and diarrhea has a 3.2-fold amplification in COVIDpos patients during the week prior to PCR testing, and along with anosmia/dysgeusia, constitutes the earliest EHR-derived signature of COVID-19 (4-7 days prior to typical PCR testing date). This study introduces an Augmented Intelligence platform for the real-time synthesis of institutional knowledge captured in EHRs. The platform holds tremendous potential for scaling up curation throughput, with minimal need for retraining underlying neural networks, thus promising EHR-powered early diagnosis for a broad spectrum of diseases.

With the economic and practical limits of medical screening for SARS-CoV-2/COVID-19 coming sharply into focus worldwide, scientists are turning now to wastewater-based epidemiology (WBE) as a potential tool for assessing and managing the pandemic. We employed computational analysis and modeling to examine the feasibility, economy, opportunities and challenges of enumerating active coronavirus infections locally and globally using WBE. Depending on local conditions, detection in community wastewater of one symptomatic/asymptomatic infected case per 100 to 2,000,000 non-infected people is theoretically feasible, with some practical successes now being reported from around the world. Computer simulations for past, present and emerging epidemic hotspots (e.g., Wuhan, Milan, Madrid, New York City, Teheran, Seattle, Detroit and New Orleans) identified temperature, average in-sewer travel time and per-capita water use as key variables. WBE surveillance of populations is shown to be orders of magnitude cheaper and faster than clinical screening, yet cannot fully replace it. Cost savings worldwide for one-time national surveillance campaigns are estimated to be in the million to billion US dollar range (US$), depending on a nation's population size and number of testing rounds conducted. For resource poor regions and nations, WBE may represent the only viable means of effective surveillance. Important limitations of WBE rest with its inability to identify individuals and to pinpoint their specific locations. Not compensating for temperature effects renders WBE data vulnerable to severe under-/over-estimation of infected cases. Effective surveillance may be envisioned as a two-step process in which WBE serves to identify and enumerate infected cases, where after clinical testing then serves to identify infected individuals in WBE-revealed hotspots. Data provided here demonstrate this approach to save money, be broadly applicable worldwide, and potentially aid in precision management of the pandemic, thereby helping to accelerate the global economic recovery that billions of people rely upon for their livelihoods.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused more than 200,000 reported COVID-19 cases in Spain resulting in more than 20,800 deaths as of April 21, 2020. Faecal shedding of SARS-CoV-2 RNA from COVID-19 patients has extensively been reported. Therefore, we investigated the occurrence of SARS-CoV-2 RNA in six wastewater treatments plants (WWTPs) serving the major municipalities within the Region of Murcia (Spain), the area with the lowest COVID-19 prevalence within Iberian Peninsula. Firstly, an aluminum hydroxide adsorption-precipitation concentration method was tested using a porcine coronavirus (Porcine Epidemic Diarrhea Virus, PEDV) and mengovirus (MgV). The procedure resulted in average recoveries of 10.90 and 10.85% in influent water and 3.29 and 6.19 in effluent water samples for PEDV and MgV, respectively. Then, the method was used to monitor the occurrence of SARS-CoV-2 from March 12 to April 14, 2020 in influent, secondary and tertiary effluent water samples. By using the real-time RT-PCR (RT-qPCR) Diagnostic Panel validated by US CDC that targets three regions of the virus nucleocapsid (N) gene, we estimated quantification of SARS-CoV-2 RNA titers in untreated wastewater waters of 5.38 log genomic copies/L on average. Two secondary water samples resulted positive (2 out of 18) and all tertiary water samples tested as negative (0 out 12). This environmental surveillance data were compared to declared COVID-19 cases at municipality level, revealing that SARS-CoV-2 was circulating among the population even before the first cases were reported by local or national authorities in many of the cities where wastewaters have been sampled. The detection of SARS-CoV-2 in wastewater in early stages of the spread of COVID-19 highlights the relevance of this strategy as an early indicator of the infection within a specific population. At this point, this environmental surveillance could be implemented by municipalities right away as a tool, designed to help authorities to coordinate the exit strategy to gradually lift its coronavirus lockdown.

Tests at wastewater treatment plants in France have revealed levels of coronavirus that rise and fall over time like clinical cases.

This paper discusses patterns of public health misinformation observed during infectious disease epidemics. Specifically we group epidemic-related misinformation into four categories: transmission, prevention, treatment, and vaccination. By developing tools, algorithms, and other resources around these categories, institutions, companies, and individuals can proactively limit and counter the spread of misinformation and its potential negative health effects.

Since many SARS-CoV-2 carriers are assumed to exhibit no or few non-specific symptoms, SARS-CoV-2 circulation among human populations may be detected too lately and only when massive human testing is available or when clinal COVID-19 cases are reported. This is obviously a major pitfall for evaluating and possibly controlling the current epidemic. Due to the presence of SARS-CoV-2 in stool samples qualitative detection of SARS-CoV-2 in wastewaters has recently been proposed as a complementary tool to investigate the virus circulation in human populations. If this assumption is correct, SARS-CoV-2 relative amounts in wastewaters should correlate with the number of confirmed COVID-19 cases. To test this hypothesis, we performed a time-course quantitative analysis of SARS-CoV2 by RT-qPCR in 23 raw and 8 treated wastewater samples collected from 3 major wastewater treatment plant (WWTP) of the Parisian area collecting 3 to 4 million inhabitants reject. This study was conducted from 5 March to 7 April 2020. All raw wastewater samples scored positive for SARS-CoV2. Additionally, 6 out of 8 samples from treated wastewater scored positive by RT-qPCR. Treated wastewater effluents showed a 100 times reduction in the viral load compared to the corresponding raw wastewater samples, which agrees with previous work on enteric viruses. We next compared the average level of SARS-CoV2 genomes in wastewater samples over time with the number of confirmed fatal cases of COVID19 in Paris area and in France As expected, we confirmed that the increase of genome units in raw wastewaters accurately followed the increase in the number of fatal cases observed at the regional and national level Therefore, our study demonstrates that the contamination of wastewater and the detection of viral genome occurred before the beginning of the exponential growth of the epidemic. This work demonstrated that a quantitative monitoring of SARS-CoV2 genomes in wastewaters should bring important and additional information for better survey of SARS-CoV2 circulation at the local or regional scale. Additionally, wastewater survey may provide an alternative and possibly early tool to detect pathogens in populations when investigations in humans is difficult for logistic, ethical or economic reasons.

Searching wastewater for the coronavirus is fast, cheap, and anonymous.

Effective public health response to viral outbreaks such as SARS-CoV-2 is often informed by real-time PCR screening of large populations. Pooling samples can increase screening capacity. However, when a traditional pool is tested positive, all samples in the pool need individual retesting, which becomes ineffective at a higher proportion of positive samples. Here, we report a new pooling protocol that mitigates this problem by replicating samples across multiple pools. The resulting pool set allows the sample status to be resolved more often than with traditional pooling. At 2% prevalence and 20 samples per pool, our protocol increases screening capacity by factors of 5 and 2 compared to individual testing and traditional pooling, respectively. The corresponding software to layout and resolve samples is freely available under a BSD license (https://github.com/phiweger/clonepool).

The COVID-19 pandemic is rapidly spreading throughout the world. Recent reports suggest that 10-30% of SARS-CoV-2 infected patients are asymptomatic. Other studies report that some subjects have significant viral shedding prior to symptom onset. Since both asymptomatic and pre-symptomatic subjects can spread the disease, identifying such individuals is critical for effective control of the SARS-CoV-2 pandemic. Therefore, there is an urgent need to increase diagnostic testing capabilities in order to also screen asymptomatic carriers. In fact, such tests will be routinely required until a vaccine is developed. Yet, a major bottleneck of managing the COVID-19 pandemic in many countries is diagnostic testing, due to limited laboratory capabilities as well as limited access to genome-extraction and Polymerase Chain Reaction (PCR) reagents. We developed P-BEST - a method for Pooling-Based Efficient SARS-CoV-2 Testing, using a non-adaptive group-testing approach, which significantly reduces the number of tests required to identify all positive subjects within a large set of samples. Instead of testing each sample separately, samples are pooled into groups and each pool is tested for SARS-CoV-2 using the standard clinically approved PCR-based diagnostic assay. Each sample is part of multiple pools, using a combinatorial pooling strategy based on compressed sensing designed for maximizing the ability to identify all positive individuals. We evaluated P-BEST using leftover samples that were previously clinically tested for COVID-19. In our current proof-of-concept study we pooled 384 patient samples into 48 pools providing an 8-fold increase in testing efficiency. Five sets of 384 samples, containing 1-5 positive carriers were screened and all positive carriers in each set were correctly identified. P-BEST provides an efficient and easy-to-implement solution for increasing testing capacity that will work with any clinically approved genome-extraction and PCR-based diagnostic methodologies.

We argue that frequent sampling of the fraction of infected people (either by random testing or byanalysis of sewage water), is central to managing the COVID-19 pandemic because it both measuresin real time the key variable controlled by restrictive measures, and anticipates the load on thehealthcare system due to progression of the disease. Knowledge of random testing outcomes will (i)significantly improve the predictability of the pandemic, (ii) allow informed and optimized decisionson how to modify restrictive measures, with much shorter delay times than the present ones, and(iii) enable the real-time assessment of the efficiency of new means to reduce transmission rates.Here we suggest, irrespective of the size of a suitably homogeneous population, a conservativeestimate of 15’000 for the number of randomly tested people per day which will suffice to obtainreliable data about the current fraction of infections and its evolution in time, thus enabling close toreal-time assessment of the quantitative effect of restrictive measures. Still higher testing capacitypermits detection of geographical differences in spreading rates. Furthermore and most importantly,with daily sampling in place, a reboot could be attempted while the fraction of infected people isstill an order of magnitude higher than the level required for a relaxation of restrictions with testingfocused on symptomatic individuals. This is demonstrated by considering a feedback and controlmodel of mitigation where the feed-back is derived from noisy sampling data.

As conspiracy theories about COVID-19 take root in the United States, understanding the psychological foundations of conspiracy beliefs is increasingly critical. Our research shows that beliefs in two popular variants of COVID-19 conspiracy theory are the joint product of the psychological predispositions 1) to reject information coming from experts and other authority figures and 2) to view major events as the product of conspiracies, as well as partisan and ideological motivations. The psychological foundations of conspiracy beliefs have implications for the development of strategies designed to curtail their negative consequences.

In a time of increasing demand and limited resources, diverse approaches to disseminating evidence-based mental health services are imperative to reduce the overall burden of psychopathology. Unmet treatment needs are especially notable for youth; up to 80% of U.S. youth with mental health needs receive no treatment at all, and few who successfully connect with services receive evidence-supported care. Even among those who do receive quality care, the modal number of sessions clients attend is one, raising the concern that clients may discontinue treatment before engaging in the therapeutic mechanisms that make them effective. Thus, the field of psychotherapy is in a dire need of innovative shifts in intervention research to disrupt the continuously increasing prevalence, incidence, and burden of mental illness. This brief column introduces ways in which "single-session interventions" (SSIs) for psychopathology might forward such shifts in evidence-based mental health practice.

Historic England has launched a new project to record life under lockdown - the first experiment of its kind since World War Two.

Working with COVID-19 data Participants can also use COVID-19 data for their project description. It might be interesting to link this data to other sources. More on information on how to link COVID-19 data here.

Each of us has a part to play in combatting the COVID-19 pandemic, including journals and Registered Reports (RRs). For those unfamiliar with RRs, they are a form of empirical article offered by more than 200 journals in which study proposals are peer reviewed and pre-accepted before research is undertaken. This article type offers a powerful tool for advancing research on COVID-19 by reducing publication bias and reporting bias in the growing evidence base. As the RR editor at Royal Society Open Science, I am announcing a special call for submissions that are relevant to any aspect of COVID-19 in any field, including but not limited to biological, medical, economic, and psychological research. All RRs at Royal Society Open Science are published open access (CC-BY) with accompanying open review. To maximise accessibility of the call, all article processing charges for these submissions are automatically waived, and to ensure that high quality protocols can be implemented as swiftly as possibly, the journal will strive to complete initial Stage 1 review within 7 days of receiving a submission.  This form of rapid review for RRs has never been attempted, and for it to succeed we need your help. 

n this this webinar, Professor Simon Dennis will speak about the collection and analysis methods that are applicable to experience sampling data from dense data sources. Smartphones, social media networks, wearable sensors and the internet of things are being used to provide an unparalleled window into psychological processes as they occur in the real world. We will present some of the current psychological research that has used these technologies in the fields of clinical psychology, cognitive psychology, and psychiatry. There will be a Q&A section at the end of the webinar where attendees can share their areas of research interest, and Simon can provide suggestions about which data streams may be useful and how to start. This webinar will provide everything you need get started in experience sampling research.

The ongoing coronavirus disease 2019 (COVID-19) pandemic is a substantial challenge for health-care systems and their infrastructure. RT-PCR-based diagnostic confirmation of infected individuals is crucial to contain viral spread because infection can be asymptomatic despite high viral loads. Sufficient molecular diagnostic capacity is important for public health interventions such as case detection and isolation, including for health-care professionals.1Koo JR Cook AR Park M et al.Interventions to mitigate early spread of SARS-CoV-2 in Singapore: a modelling study.Lancet Infect Dis. 2020; (published online March 23.)https://doi.org/10.1016/S1473-3099(20)30162-6Google ScholarProtocols for RNA RT-PCR testing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) became available early in the pandemic, yet the infrastructure of testing laboratories is stretched and in some areas it is overwhelmed.2Corman VM Landt O Kaiser M et al.Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR.Euro Surveill. 2020; 252000045Google Scholar We propose a testing strategy that is easy to implement and can expand the capacity of the available laboratory infrastructure and test kits when large numbers of asymptomatic people need to be screened. We introduced the pooling of samples before RT-PCR amplification, and only in the case of positive pool test results is work-up of individual samples initiated, thus potentially substantially reducing the number of tests needed.

Intersectoral governance for health has long been seen as a “nice to have”- a search for useful win-wins between sectors. In the COVID-19 pandemic, it is critical and occurring virtually everywhere in one way or another. 

The UK government has been accused of paying too much attention to epidemiologists over other experts

How can social media platforms fight the spread of misinformation? One possibility is to use newsfeed algorithms to downrank content from sources that users rate as untrustworthy. But will laypeople be handicapped by motivated reasoning or lack of expertise, and thus unable to identify misinformation sites? And will they "game" this crowdsourcing mechanism in order to promote content that aligns with their partisan agendas? We conducted a survey experiment in which =984 Americans indicated their trust in numerous news sites. To study the tendency of people to game the system, half of the participants were told their responses would inform social media ranking algorithms. Participants trusted mainstream sources much more than hyper-partisan or fake news sources, and their ratings were highly correlated with professional fact-checker judgments. Critically, informing participants that their responses would influence ranking algorithms did not diminish these results, despite the manipulation increasing the political polarization of trust ratings.

Inequality has been linked with numerous negative consequences at the societal and individual levels. The detrimental effects of inequality are proposed to operate at the individual level through social comparisons, where perceived unfair disadvantage leads to the experience of personal relative deprivation (i.e., subjective feelings of anger and resentment), which in turn causes psychosocial stress. To date, little empirical work has investigated how individual differences in personal relative deprivation influences group dynamics. In a simulated high-pressure hypothetical scenario, first-year business students (n = 150) in groups of four to six were tasked to reach a consensus decision despite being assigned roles with competing interests, then they individually completed a survey. Greater feelings of personal relative deprivation were associated with reduced group engagement. Personal relative deprivation explained 9% of variance in group exercise engagement over and above demographic and situation-related variables (e.g., stress, perceived competition, role fit), and the overall regression model accounted for 58% of total variance in group. These findings suggest that such negative socioemotional comparison reactions as personal relative deprivation have important implications for group-based decision making and small group dynamics.

Preregistration has been lauded as one of the solutions to the so-called ‘crisis of confidence’ in the social sciences and has therefore gained in popularity in recent years. However, despite its growing popularity, preregistration is still in its infancy and preregistration practices are far from optimal. Moreover, the current infrastructure for preregistration is limited and is primarily relevant for studies where new data will be collected. This is unfortunate as preregistering secondary data analyses is crucially important since researchers’ hypotheses and analyses may be biased by their prior knowledge of the data, and because the extensive nature of many secondary data sets provide ample opportunity for p-hacking. In this tutorial, we present a template specifically designed for the preregistration of secondary data analyses, and provide comments and a practical example that may help with using the template effectively.

Interacting agents may anticipate their partner’s upcoming response and include it in their action plan. In turn, observing an overt response can trigger agents to adapt. But while anticipation and adaptation are known to shape action control, their interplay in social interactions remains largely unexplored. In four experiments, we asked how both of these mechanisms could contribute to one striking phenomenon: Agents initiate actions faster when they know their partner will produce a compatible rather than an incompatible response. In Experiment 1, we manipulated the compatibility between agents’ actions and partners’ responses, and investigated the interplay between adaptation and anticipation within the same dyadic interaction. In Experiments 2-4 we isolated the contribution of each of the two mechanisms by having agents interact with virtual partners whose responses could be experimentally controlled. We found that adaptation and anticipation exert parallel but independent effects on action execution: Participants initiated their actions more quickly when the upcoming partner response was compatible and, independently, when their partner had responded more quickly on the preceding trial. These findings elucidate models of action control in social interactions.

Survey experiments with nearly 7,000 Americans suggest that increasing the visibility of publishers is an ineffective, and perhaps even counterproductive, way to address misinformation on social media. Our findings underscore the importance of social media platforms and civil society organizations evaluating interventions experimentally rather than implementing them based on intuitive appeal.

Try to reach it without a vaccine, and millions will die.

Recent studies have shown that online users tend to select information adhering to their system of beliefs, ignore information that does not, and join groups - i.e., echo chambers - around a shared narrative. Although a quantitative methodology for their identification is still missing, the phenomenon of echo chambers is widely debated both at scientific and political level. To shed light on this issue, we introduce an operational definition of echo chambers and perform a massive comparative analysis on more than 1B pieces of contents produced by 1M users on four social media platforms: Facebook, Twitter, Reddit, and Gab. We infer the leaning of users about controversial topics - ranging from vaccines to abortion - and reconstruct their interaction networks by analyzing different features, such as shared links domain, followed pages, follower relationship and commented posts. Our method quantifies the existence of echo-chambers along two main dimensions: homophily in the interaction networks and bias in the information diffusion toward likely-minded peers. We find peculiar differences across social media. Indeed, while Facebook and Twitter present clear-cut echo chambers in all the observed dataset, Reddit and Gab do not. Finally, we test the role of the social media platform on news consumption by comparing Reddit and Facebook. Again, we find support for the hypothesis that platforms implementing news feed algorithms like Facebook may elicit the emergence of echo-chambers.