NHS coronavirus crisis volunteers frustrated at lack of tasks

10.31234/osf.io/ua6tq

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.

Students under lockdown: Assessing change in students’ social networks and mental health during the COVID-19 crisis

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.

‘We had to bring people together’

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.

CoViz is a tool for exploring the evolving network of science in the COVID-19 Open Research Dataset, from Semantic Scholar at the Allen Institute for AI.

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.

WIPO Launches Tool to Track IP Policy Information in Member States during 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.

COVID-19 Open Research Dataset Challenge (CORD-19)An AI challenge with AI2, CZI, MSR, Georgetown, NIH & The White House

There is now a flood of COVID-19 preprints from the behavioural sciences - these need critical evaluation and discussion! Are you a behavioural scientist? Then please join our community-based discussion platform now, see:

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

Dancing to the Partisan Beat: A First Analysis of PoliticalCommunication on TikTok

10.1126/science.abc6227

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.

Should schools reopen? Kids’ role in pandemic still a mystery

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.

What you need to know about how coronavirus is changing science

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.

Rapid Response: Response to 'Modelling the pandemic': reconsidering the quality of evidence from epidemiological models

Globe-trotting humans were the culprits 

Virus Mutations Reveal How COVID-19 Really Spread

We perform a counterfactual time series analysis using two different Data Science methods applied to 2020 mortality data reported from towns in Italy, with data from the previous five years as control. We find an excess mortality that is correlated in time with the COVID-19 reported death rate time series. Our analysis shows good agreement with reported COVID-19 mortality for age<70 years, but an excess in total mortality increasing with age above 70 years, suggesting there is a large population of predominantly old people missing from the official fatality statistics. We estimate that the number of COVID-19 deaths in Italy is 52,000 ± 2000 as of April 18 2020, more than a factor of 2 higher than the official number. The Population Fatality Rate (PFR) has reached 0.22% in the most affected region of Lombardia and 0.57% in the most affected province of Bergamo,which constitutes a lower bound to the Infection Fatality Rate (IFR). We estimate PFR as a function of age, finding a steep age dependence: in Lombardia (Bergamo province) 0.6% (1.7%) of the total population in age group 70-79 died, 1.6% (4.6%) in age group 80-89, and 3.41% (10.2%) in the age group above 90. We combine this with the Test Positivity Rate to estimate the lower bound of 0.84% on the IFR for Lombardia. We observe IFR to trace the Yearly Mortality Rate (YMR) above 60 years, which can be used to estimate the IFR for other regions in the world. We predict an IFR lower bound of 0.5% for NYC and 26% of total COVID-19 mortality arising from the population below 65 years, in agreement with the existing data and several times higher than Lombardia. Combining PFR with the Princess Diamond cruise ship IFR for ages above 70 we estimate the infection rates(IR) of regions in Italy, which peak in Lombardia at 23% (12%-41%, 95% c.l.), and for provinces in Bergamo at 67% (33%-100%, 95% c.l.). This suggests that Bergamo may have reached herd immunity, and that the number of infected people greatly exceeds the number of positive tests, by a factor of 35 in Lombardia.

10.1101/2020.04.15.20067074

Total COVID-19 Mortality in Italy: Excess Mortality and Age Dependence through Time-Series Analysis

10.1016/S0140-6736(20)30933-8

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.

COVID-19: a need for real-time monitoring of weekly excess deaths

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.

10.1101/2020.04.12.20062661

Artificial intelligence for rapid identification of the coronavirus disease 2019 (COVID-19)

10.1101/2020.04.19.20067660

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.

Augmented Curation of Unstructured Clinical Notes from a Massive EHR System Reveals Specific Phenotypic Signature of Impending COVID-19 Diagnosis

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.

10.1016/j.scitotenv.2020.138875

Computational analysis of SARS-CoV-2/COVID-19 surveillance by wastewater-based epidemiology locally and globally: Feasibility, economy, opportunities and challenges

10.1101/2020.04.22.20075200

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.

SARS-CoV-2 RNA titers in wastewater anticipated COVID-19 occurrence in a low prevalence area

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

Coronavirus found in Paris sewage points to early warning system

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

Poop could be the key to tracking COVID-19 outbreaks

10.1101/2020.04.12.20062679

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.

Time course quantitative detection of SARS-CoV-2 in Parisian wastewaters correlates with COVID-19 confirmed cases

Increased PCR screening capacity using a multi-replicate pooling scheme

10.1101/2020.04.16.20067603

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).

Increased PCR screening capacity using a multi-replicate pooling scheme

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.

10.1101/2020.04.14.20064618

Efficient high throughput SARS-CoV-2 testing to detect asymptomatic carriers

2004.04614v2

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.

Using random testing in a feedback-control loop to manage a safe exit from theCOVID-19 lockdown

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

First 'Mass Observation' experiment since World War Two as lockdown diaries project is launched

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.

EU Datathon and COVID-19 data

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.

Using Smartphone, Social Media, and Sensor Data for Psychological Research (Webinar 1 - May 13)

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. 

Cross-Country Analysis How are countries centralizing governance and at what stage are they doing it?

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.

10.1145/3313831.3376232

Will the Crowd Game the Algorithm?: Using Layperson Judgments to Combat Misinformation on Social Media by Downranking Distrusted Sources

10.31234/osf.io/hvfmr

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.

Preregistration of secondary data analysis: A template and tutorial

10.37016/mr-2020-001

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.

Emphasizing publishers does not effectively reduce susceptibility to misinformation on social media

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

What the Proponents of ‘Natural’ Herd Immunity Don’t Say

Sir David King, government critic and former chief scientific adviser, announces 12‑strong group and criticises lack of transparency in Boris Johnson’s decisions

Coronavirus lockdown: scientists challenge No 10 with rival advice on Covid‑19

The Covid-19 pandemic presents numerous new challenges, and the British Psychological Society’s research priorities workstream has formed out of the belief that psychological science has much to offer in helping societies to cope with the pandemic.

Help us decide on the Covid-19 research priorities for psychological science

It is often assumed that issue advocacy will compromise the credibility of scientists. We conducted a randomized controlled experiment to test public reactions to six different advocacy statements made by a scientist—ranging from a purely informational statement to an endorsement of specific policies. We found that perceived credibility of the communicating scientist was uniformly high in five of the six message conditions, suffering only when he advocated for a specific policy—building more nuclear power plants (although credibility did not suffer when advocating for a different specific policy—carbon dioxide limits at power plants). We also found no significant differences in trust in the broader climate science community between the six message conditions. Our results suggest that climate scientists who wish to engage in certain forms of advocacy have considerable latitude to do so without risking harm to their credibility, or the credibility of the scientific community.

10.1080/17524032.2016.1275736

Does Engagement in Advocacy Hurt the Credibility of Scientists? Results from a Randomized National Survey Experiment

Keeping up with the latest research is tough. Millions of new research articles are published each year. Then there’s reports, white papers and company documents. Your usual research tools can find the most recent or influential ones, but you still have hundreds of papers on your desktop – and no time to read them. Skimming the abstract doesn’t give you enough insight into how important a paper could be to your work.

So many great articles, so little time.

Our mission is to understand the spread of the COVID19 and analyse its numerous implications on the health of populations, health care, society, the economy at large and its financial and legal consequences. This is done by using rigorous evaluation methodologies across many disciplines. The research aims at helping analyze and design efficient policies in relation to the COVID19 crisis. We disseminate our findings and initiatives through several channels, including academic publications, this website, workshops and media releases.

Covid Crisis Lab Laboratory for Coronavirus Crisis Research

Opinion: The theory is enjoying a moment of popularity in policy research and practice

Complex systems thinking is being used for policymaking. Is it the future?

KNOWLEDGE BASE ON EPIDEMICS FROM AMELICA/REDALYC JOURNALS

COVID-19 testing strategies are primarily driven by medical need - focusing on people already hospitalized with significant symptoms or on people most at risk. However, such testing is highly biased because it fails to identify the extent to which COVID-19 is present in people with mild or no symptoms. If we wish to understand the true rate of COVID-19 infection and death, we need to take full account of the causal explanations for the resulting data to avoid highly misleading conclusions about infection and death rates. We describe how causal (Bayesian network) models can provide such explanations and the need to combine these with more random testing in order to achieve reliable data and predictions for the both policy makers and the public.

10.1080/13669877.2020.1756381

COVID-19 infection and death rates: the need to incorporate causal explanations for the data and avoid bias in testing

Dr. Talya Miron-Shatz says more refined decision-making will improve public health

‘Israel needs better coronavirus communication’

10.31234/osf.io/efk93

In tragic circumstances, it is not uncommon to see an upsurge in the generation of disaster jokes, which humorously depict the macabre aspects of ongoing crises. Many of such jokes appeared also at the time of the Coronavirus disease 2019 (Covid-19) outbreak, often becoming viral. However, little is known about the emotional response to disaster jokes, let alone Covid-19 ones. With a large-scale survey completed during Italy’s nationwide lockdown, we studied the appreciation, i.e., funniness and aversiveness, of Covid-19 humor. Using a mixed models approach, we also analyzed the role of personality factors and psychological distance. Results showed that Covid-19 humor is associated with a mark of aversiveness, greater than for non-pandemic humor. Individuals more inclined to use humor to cope with uneasy circumstances perceived Covid-19 humor as funnier and less aversive. Furthermore, the perceived risk of being infected with SARS-CoV-2 amplified Covid-19 humor aversiveness, while greater spatial distance from the Italian epicenter of the contagion allowed to deeper enjoy humor both related and not-related to Covid-19. These findings should raise awareness on the emotional correlates to Covid-19 humor, of possible support in deciding whether and with whom to joke on the pandemic in social and political communication.

Funny but aversive: A large-scale survey on the emotional response to Covid-19 humor in the Italian population during the lockdown

Disclaimer: Before I begin, I want to start by acknowledging a major constraint on the generalizability of what I am about to say. In this post I will use the term “psychologist” periodically for the sake of brevity, instead of explicitly naming every sub-field when discussing points I think apply to multiple sub-fields of the discipline. When I say “psychologist,” however, it should be clear that I am not referring to the clinicians and other therapists that most people in the general public think of when hearing that term. The work of clinicians is highly essential at this moment; it is urgently needed to get us through this pandemic. Clinicians, and other essential workers, I thank you for your service. This post is about, and for, the rest of us.

How many (and whose) lives would you bet on your theory?

More than 3 million Australians have downloaded the Australian government’s COVIDSafe contact-tracing app in the three days since its release. That’s impressive. But not as impressive as the 2 million downloads in the first 24 hours. The slowing rate suggests it will take longer to get to 4 million, and remember the federal government wants 10 million people, 40% of the population, to download and use the app.

Contact tracing apps: a behavioural economist’s guide to improving uptake

10.1016/S0140-6736(20)31017-5

Offline: A global health crisis? No, something far worse

Every evening we scour those graphs looking for signs that the epidemic is in retreat. New cases of coronavirus disease 2019 (COVID-19). People in hospital with COVID-19. Critical care beds with COVID-19 patients. Daily COVID-19 deaths in hospital. And then the final and bluntly worded “global death comparison”. Those with responsibility for leading us through this emergency have called it “a once in a century global health crisis”. This statement is incorrect on two grounds. First, because we cannot know what the rest of the century will bring. It is highly probable that this current pandemic will be neither the last nor the worst global health crisis of the present century. But second, and more importantly, this global calamity is not a crisis concerning health. It is a crisis about life itself. We have been tempted in recent years to assume the omnipotence of our species. The idea of the Anthropocene places human activity as the dominant influence on the future of life on our planet. Although this newest of geological eras is supposed to underline the harm our species is doing to fragile planetary systems, paradoxically it also asserts our supremacy. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has revealed the hubris of this view. Our species has many reasons to be self-critical about the effects of our way of life on planetary sustainability. But we are only one species among many, and we are certainly not a dominant influence when faced with a virus that can destroy life with such ease and facility.

The purpose of this page here is simply to lists all our visualizations on the pandemic.

Coronavirus Pandemic (COVID-19) – the data

10.31234/osf.io/k72vd

The emergence of COVID-19 resulted in a sudden, unprecedented change in context that impacted the way behavior analysts live and work worldwide. Any rapidly shifting context requires behavioral flexibility, in addition to the acquisition of new skills and access to resources that foster resilience in the face of practical challenges and uncertainty about the future. Behavior analysts (particularly novice practitioners) may already be vulnerable to burn-out (Plantiveau et al., 2018), and in need of greater support to adopt protective self-care practices. Such practices will enable them to continue providing effective services to distressed families, while navigating their own challenges. This paper seeks to offer behavior analysts some tools and practices drawn from the work of contextual behavior scientists that can promote well-being and resilience. This includes strategies for clarifying and committing to an overarching value of self-care, acting congruently with personal and professional values across many domains of living, and practicing self-compassion in the process.

An Invitation to Act on the Value of Self-Care: Being a whole person in all that you do

10.31234/osf.io/wakmz

This study encompasses the knowledge of pakistani adult population about the symptoms and prevention of COVID-19 symptoms. The findings revealed that overall, 174 (87%) participants knew about COVID-19; 170 (85 %) believed that the disease is dangerous for elderly or already sick people, and 134(67%) thought that they know about the symptoms 144 (97 %) male and 63 (33 %) were female were aware about the common symptoms of COVID-19 such as fever, cough and tiredness. The current study concluded that there is still a need to improve the awareness of people regarding the symptoms and seriousness of the disease so that people take preventive measure to protect themselves and rapid transmission of the disease could be controlled.

Knowledge of COVID-19 Symptoms and Prevention among Pakistani Adults: A Cross-sectional Descriptive Study