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

Using a delay-adjusted case fatality ratio to estimate under-reporting

The aim of our study was to explore psychological determinants of COVID-19 responsible behavior. We focused on trait anxiety and worry about the corona crisis, and knowledge/unfounded beliefs about coronavirus and thinking dispositions (cognitive reflection, actively open-minded thinking, faith in intuition and science curiosity) that should drive knowledge/beliefs. Additionally, we tested the effectiveness of a one-shot intervention based on the “consider-the-opposite” debiasing technique in changing COVID-19 unfounded beliefs. We used a convenience sample of 1439 participants who filled in the questionnaire on-line. Comparison of latent means showed that the “consider-the-opposite” intervention did not affect unfounded beliefs. Structural equation model, conducted on 880 participants with data on all variables, indicated that greater worry and weaker endorsement of COVID-19 unfounded beliefs lead to more responsible COVID-19 behavior. The relationship of trait anxiety and thinking dispositions with the criterion was mediated through the worry about COVID-19 and unfounded beliefs about COVID-19, respectively.

Misbehaving in the Corona Crisis: The Role of Anxiety and Unfounded Beliefs

The construal of the self as interdependent may offer perceived protection against an external threat to survival. This hypothesis implies that interdependent self-construal may reduce normtightening in response to the threat. Here, we tested this possibility by focusing on two electrocortical responses to norm violations: N400 (a marker of norm violation detection) and suppression of upper α-band power (a marker of vigilance to the violations). 59 American young adults were primed or not with a pathogen threat and then read norm-violating or normal behaviors. In the control priming condition, interdependent self-construal predicted an increase in N400 to norm violations, implying that it enhances the accessibility of social norms. In the threat priming condition, however, interdependent self-construal predicted a decrease in both markers. Thus, this self-construal offers a sense of security, even when it is patently incapable of addressing the threat itself. We thus conclude that it breeds complacency under threat.

Interdependent Self-Construal Predicts Complacency Under Pathogen Threat: An Electrocortical Investigation

The COVID-19 pandemic has led governments worldwide to implement unprecedented response strategies. While crucial to limiting the spread of the virus, “social distancing” may lead to severe psychological consequences, especially in lonely individuals. We used cross-sectional (n=380) and longitudinal (n=74) designs to investigate the links between loneliness, mental health symptoms (MHS) and COVID-19 risk perception and affective response in young adults who implemented social distancing during the first two weeks of the state of epidemic threat in Poland. Loneliness was correlated with MHS and with affective response to COVID-19’s threat to health. However, increased worry about the social isolation and heightened risk perception for financial problems was observed in lonelier individuals. The cross-lagged influence of the initial affective response to COVID-19 on subsequent levels of loneliness was also found. Thus, the reciprocal connections between loneliness and COVID-19 response may be of crucial importance for MHS during COVID-19 crisis.

Safe but lonely? Loneliness, mental health symptoms and COVID-19

BACKGROUND: Covid-19 pandemic is burning all over the world. National healthcare systems are facing the contagion with incredible strength, but concern regarding psychosocial and economic effects is critically growing. The PsyCovid Study assessed the influence of psychosocial variables on individual differences in the perceived impact of Covid-19 outbreak on health and economy in the Italian population. METHODS: Italian volunteers from different regions completed an online anonymous survey. Main outcomes were the perceived impact of Covid-19 outbreak on health and economy. A two-way MANOVA evaluated differences in main outcomes, with geographical area (northern, central and southern regions) and professional status (healthcare workers or not) as factors. We then tested the relationship linking psychosocial variables (i.e. perceived distress and social isolation, empathy and coping style) to the main outcomes through two different mediation models. RESULTS: 1163 responders completed the survey (835 females; mean age: 42±13.5 y.o.; age range: 18-81 y.o.) between March 14 and 21, 2020. Healthcare workers and people living in northern Italy reported significantly worse outbreak impact on health, but not on economy. In the whole sample, distress and loneliness were key variables influencing perceived impact of Covid-19 outbreak on health, while empathy and coping style affected perceived impact on economy. CONCLUSION: Covid-19 pandemic represents a worldwide emergency in term of psychological, social and economic consequences. Our data suggests that in the Italian population actual differences in individual perception of the Covid-19 outbreak severity for health are dramatically modulated by psychosocial frailty (i.e., distress and loneliness). At the same time, problem-oriented coping strategies and enhanced empathic abilities increase people awareness about the severity of the impact of Covid-19 emergency on economics. There is an immediate need of consensus guidelines and healthcare policies to support interventions aimed to manage psychosocial distress and increase population resilience towards the imminent crisis.

COVID-19 OUTBREAK IN ITALY: ARE WE READY FOR THE PSYCHOSOCIAL AND ECONOMIC CRISIS? BASELINE FINDINGS FROM THE PSYCOVID STUDY

Amidst the worldwide outbreak of COVID-19 in January 2020, this study focused on the preventive behaviours against COVID-19 infection and the exclusionary attitude towards foreigners in Japan. Particularly, we examined the effects of individual differences in the infection-avoidance tendency based on the behavioural immune system. A web survey of 1,248 Japanese citizens aged 18 years or above living in Japan who were registrants of a crowdsourcing service indicated that as the threat of the COVID-19 spread increased, there were tendencies for infection-preventive behaviours to increase. In addition, people with a strong infection-avoidance tendency adopted more preventive actions, regardless of whether they were under normal circumstances or the threat of infection, indicating their strong rejective attitudes towards the Chinese and other foreigners under the threat of infection. This study recorded the behavioural and psychological states of people who were in the midst of rapid and unpredictable real-world changes in the early stages of the infection. Data collected in Japan, where the infection had begun earlier, will provide valuable knowledge to countries worldwide where major social changes are expected in the future.

The Relationship between Infection-Avoidance Tendency and Exclusionary Attitudes towards Foreigners: A Case Study of the COVID-19 Outbreak in Japan

Effective communication during the COVID-19 pandemic can save lives. At the present time, social and physical distancing measures are the lead strategy in combatting the spread of COVID-19. In this pilot, survey-based study, we obtained responses from 705 adults in Switzerland about their support and practice of social distancing measures to examine if these responses are: (1) influenced by whether these measures are supported by an internationally recognized celebrity or a government official, (2) dependent on whether the spokesperson is liked, and (3) age-dependent. We also considered several attitudinal and demographic variables that may influence the degree to which people support and comply with social distancing measures. We found that the government official was more effective, particularly in response to current compliance with social distancing measures, and was substantially stronger among older respondents despite having lower risk perception. Being liked seems to boost this effect. In addition, respondents’ greater support and compliance was positively associated with (1) higher concern for the current situation, (2) higher concern for the well-being of others, (3) greater belief that others are practicing social distancing, (4) feeling greater constraint in freedom of movement, and negatively with (5) city size, and (6) household size. Since different parts of the population appear to have different perceptions of risk and crisis, our preliminary results suggest that different spokespersons may be needed for the younger and the older populations, and for rural and urban populations. The development of evidence-based knowledge is required to further identify who would be the most effective spokesperson, and in particular to groups with low risk perception and low compliance.

Who is listening? Spokesperson Effect on Communicating Social and Physical Distancing Measures During the COVID-19 Pandemic

Humans can shorten their decisions at the expense of the decisions' accuracy, a phenomenon known as speed-accuracy tradeoff (SAT). The dominant account of SAT is the diffusion model, which is often thought to explain all effects related to SAT. However, previous research has typically examined either only a few SAT conditions or only tested a few subjects. Here we collected data from 20 subjects who performed a perceptual discrimination task with five different difficulty levels. We further included five different SAT conditions with each subject completing a total of 5,000 trials over five sessions. We found that the five SAT conditions produced robustly U-shaped curves for (i) the difference between error and correct response times (RTs), (ii) the ratio of the standard deviation and mean of the RT distributions, and (iii) the skewness of the RT distributions. Each of these three effects was present for each of the five difficulty levels. We show the standard diffusion model cannot account for the last two effects and can only account for the first U-shaped function if the starting point of the accumulation is not constrained to always be within the two boundaries. Further, both the SAT and difficulty manipulations resulted in changes in all diffusion model parameters despite the fact that the diffusion model’s “selective influence” assumption postulates that these manipulations should only affect the boundary and drift rate, respectively. These results demonstrate that the diffusion model cannot fully explain the effects of speed-accuracy tradeoff on RT and establishes three robust but challenging effects that models of speed-accuracy tradeoff should account for.

Does the diffusion model account for the effects of speed-accuracy tradeoff on response times?

Near Real-Time Studies Look for Behavioral Measures Vital to Stopping Coronavirus

But remember, there are 10,000 infections overall. So if we simulate transmission randomly from each infected person accounting for the above variation, then add up the new infections, we'd expect the following range of possibilities: 6/

For coronaviruses, there's evidence that some infectious cases may generate a lot transmission (superspreading events), some very little, and some none. E.g. we looked at this for MERS-CoV (https://eurosurveillance.org/content/10.2807/1560-7917.ES2015.20.25.21167…), building on this study of SARS etc: https://nature.com/articles/nature04153… 2/

But what about a larger epidemic? Say there are currently 10,000 infections in a population (as there may be in many countries now). How many more would we expect these people to infect in the next few days? 4/

In other words, we might have high variation at the *individual level*, but once we have a large number of infections, the *population level* dynamics are relatively much less variable. This is the logic behind most population-based epidemic models (e.g. the SIR model). 7/

Let's assume high variation in transmission at the individual-level – some cases generate lots of infection, but most generate none. If we assume SARS-like potential for superspreading and early COVID-19 transmission (R=2.5), we'd get following pattern at individual level: 5/

Now you might say, "Surely these superspreading events are predictable? Shouldn't we therefore include them in all models, then target these events to bring outbreak under control?" Unfortunately, like many 'obvious' solutions, it's rarely that simple: https://twitter.com/AdamJKucharski/status/1240774378834534400?s=20… 9/

Often SIR-type models will incorporate some randomness in the transmission process to account for this population-level variability during each generation of infection, e.g. https://thelancet.com/journals/laninf/article/PIIS1473-3099(20)30144-4/fulltext… 8/

In other words, it's important to think about age groups and context of interactions for respiratory infections, but if we focus too much on individual-level social behaviour, we may risk adding complexity without necessarily adding more accuracy. 11/

Intuitively, it may seem like individual social interactions might a good predictor of spread. But our analysis of 2009 flu pandemic found that average social behaviour of an age group could capture patterns better than individual-level contacts... https://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.1004206… 10/

It's tempting to add as much detail as possible to a model, and criticise anything simpler. But the appropriateness of a model will depend on situation you're facing, the evidence you have that a specific process is predictive of risk, and question you're trying to answer. 13/13

Indeed, as this (aptly titled) piece suggests, complex models may be no more reliable than simple ones if they miss key aspects of the biology. Complex models can create the illusion of realism, and make it harder to spot crucial omissions https://pnas.org/content/103/33/12221… 12/

When there are small numbers of cases, this variation is obviously very important. It can influence the risk of a new outbreak (Fig 3: https://thelancet.com/journals/laninf/article/PIIS1473-3099(20)30144-4/fulltext…) and the potential effectiveness of measures like contact tracing/ring vaccination (https://wwwnc.cdc.gov/eid/article/22/1/15-1410_article…) 3/

A common criticism of population-based epidemic models is that they don't account for individual-level variation in transmission (i.e. superspreading events). But how much of a problem is this? 1/

Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries

Following the emergence of a novel coronavirus (SARS-CoV-2) and its spread outsideof China, Europe is now experiencinglarge epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently,widescale social distancing including local and national lockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across11 European countries.Our methods assume that changes in the reproductive number –a measure of transmission -areanimmediate response to these interventionsbeing implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from thedeaths observed over time to estimate transmission that occurred several weeks prior, allowing forthe time lag between infection and death.

Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study

The coronavirus disease 2019 (COVID-19) epidemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), began in Wuhan city, Hubei province, in December, 2019, and has spread throughout China. Understanding the evolving epidemiology and transmission dynamics of the outbreak beyond Hubei would provide timely information to guide intervention policy.MethodsWe collected individual information from official public sources on laboratory-confirmed cases reported outside Hubei in mainland China for the period of Jan 19 to Feb 17, 2020. We used the date of the fourth revision of the case definition (Jan 27) to divide the epidemic into two time periods (Dec 24 to Jan 27, and Jan 28 to Feb 17) as the date of symptom onset. We estimated trends in the demographic characteristics of cases and key time-to-event intervals. We used a Bayesian approach to estimate the dynamics of the net reproduction number (Rt) at the provincial level.FindingsWe collected data on 8579 cases from 30 provinces. The median age of cases was 44 years (33–56), with an increasing proportion of cases in younger age groups and in elderly people (ie, aged >64 years) as the epidemic progressed. The mean time from symptom onset to hospital admission decreased from 4·4 days (95% CI 0·0–14·0) for the period of Dec 24 to Jan 27, to 2·6 days (0·0–9·0) for the period of Jan 28 to Feb 17. The mean incubation period for the entire period was estimated at 5·2 days (1·8–12·4) and the mean serial interval at 5·1 days (1·3–11·6). The epidemic dynamics in provinces outside Hubei were highly variable but consistently included a mixture of case importations and local transmission. We estimated that the epidemic was self-sustained for less than 3 weeks, with mean Rt reaching peaks between 1·08 (95% CI 0·74–1·54) in Shenzhen city of Guangdong province and 1·71 (1·32–2·17) in Shandong province. In all the locations for which we had sufficient data coverage of Rt, Rt was estimated to be below the epidemic threshold (ie, <1) after Jan 30.InterpretationOur estimates of the incubation period and serial interval were similar, suggesting an early peak of infectiousness, with possible transmission before the onset of symptoms. Our results also indicate that, as the epidemic progressed, infectious individuals were isolated more quickly, thus shortening the window of transmission in the community. Overall, our findings indicate that strict containment measures, movement restrictions, and increased awareness of the population might have contributed to interrupt local transmission of SARS-CoV-2 outside Hubei province.

Business disruptions from social distancing

Social distancing interventions can be effective against epidemics but are potentially detrimental for the economy. Businesses that rely heavily on face-to-face communication or close physical proximity when producing a product or providing a service are particularly vulnerable. There is, however, no systematic evidence about the role of human interactions across different lines of business and about which will be the most limited by social distancing. Here we provide theory-based measures of the reliance of U.S. businesses on human interaction, detailed by industry and geographic location. We find that 49 million workers work in occupations that rely heavily on face-to-face communication or require close physical proximity to other workers. Our model suggests that when businesses are forced to reduce worker contacts by half, they need a 12 percent wage subsidy to compensate for the disruption in communication. Retail, hotels and restaurants, arts and entertainment and schools are the most affected sectors. Our results can help target fiscal assistance to businesses that are most disrupted by social distancing.

2003.13983

Special report: The simulations driving the world’s response to COVID-19

We show that malicious COVID-19 content, including hate speech, disinformation, and misinformation, exploits the multiverse of online hate to spread quickly beyond the control of any individual social media platform. Machine learning topic analysis shows quantitatively how online hate communities are weaponizing COVID-19, with topics evolving rapidly and content becoming increasingly coherent. Our mathematical analysis provides a generalized form of the public health R0 predicting the tipping point for multiverse-wide viral spreading, which suggests new policy options to mitigate the global spread of malicious COVID-19 content without relying on future coordination between all online platforms.

Hate multiverse spreads malicious COVID-19 content online beyond individual platform control

2004.00673

Multiscale modelling of infectious disease systems falls within the domain of complexity science—the study of complex systems. However, what should be made clear is that current progress in multiscale modelling of infectious disease dynamics is still as yet insufficient to present it as a mature sub-discipline of complexity science. In this article we present a methodology for development of multiscale models of infectious disease systems. This methodology is a set of partially ordered research and development activities that result in multiscale models of infectious disease systems built from different scientific approaches. Therefore, the conclusive result of this article is a methodology to design multiscale models of infectious diseases. Although this research and development process for multiscale models cannot be claimed to be unique and final, it constitutes a good starting point, which may be found useful as a basis for further refinement in the discourse for multiscale modelling of infectious disease dynamics.

The research and development process for multiscale models of infectious disease systems

A County-level Dataset for Informing the United States' Response to COVID-19

As the coronavirus disease 2019 (COVID-19) becomes a global pandemic, policy makers must enact interventions to stop its spread. Data driven approaches might supply information to support the implementation of mitigation and suppression strategies. To facilitate research in this direction, we present a machine-readable dataset that aggregates relevant data from governmental, journalistic, and academic sources on the county level. In addition to county-level time-series data from the JHU CSSE COVID-19 Dashboard, our dataset contains more than 300 variables that summarize population estimates, demographics, ethnicity, housing, education, employment and in come, climate, transit scores, and healthcare system-related metrics. Furthermore, we present aggregated out-of-home activity information for various points of interest for each county, including grocery stores and hospitals, summarizing data from SafeGraph. By collecting these data, as well as providing tools to read them, we hope to aid researchers investigating how the disease spreads and which communities are best able to accommodate stay-at-home mitigation efforts. Our dataset and associated code are available at this https URL.

2004.00756

Perception of emergent epidemic of COVID-2019 / SARS CoV-2 on the Polish Internet

We study the perception of COVID-2019 epidemic in Polish society using quantitative analysis of its digital footprints on the Internet (on Twitter, Google, YouTube, Wikipedia and electronic media represented by Event Registry) from January 2020 to 12.03.2020 (before and after official introduction to Poland on 04.03.2020). To this end we utilize data mining, social network analysis, natural language processing techniques. Each examined internet platform was analyzed for representativeness and composition of the target group. We identified three temporal major cluster of the interest before disease introduction on the topic COVID-2019: China- and Italy-related peaks on all platforms, as well as a peak on social media related to the recent special law on combating COVID-2019. Besides, there was a peak in interest on the day of officially confirmed introduction as well as an exponential increase of interest when the Polish government declared war against disease with a massive mitigation program. From sociolingistic perspective, we found that concepts and issues of threat, fear and prevention prevailed before introduction. After introduction, practical concepts about disease and epidemic dominate. We have found out that Twitter reflected the structural division of the Polish political sphere. We were able to identify clear communities of governing party, mainstream oppostition and protestant group and potential sources of misinformation. We have also detected bluring boundaries between comminities after disease introduction.

2004.00005

COVID-19 Resources for Mental Health Professionals

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

Combating COVID-19: health equity matters

COVID-19 has affected vulnerable populations disproportionately across China and the world. Solid social and scientific evidence to tackle health inequity in the current COVID-19 pandemic is in urgent need.

Cultural and Institutional Factors Predicting the Infection Rate and Mortality Likelihood of the COVID-19 Pandemic

The spread of COVID-19 represents a global public health crisis, yet some nations have been more effective at limiting the spread of the virus and the likelihood that people die from infection. Here we show that institutional and cultural factors combine to explain these cross-cultural differences. Nations with efficient governments and tight cultures have been most effective at limiting COVID-19’s infection rate and mortality likelihood. An evolutionary game theory model suggests that these trends may be partly driven by variation in adherence to cooperative norms across nations. We summarize basic and policy implications of these findings.

Coronavirus (COVID-19) roundup

Office for National Statistics (ONS) data and analysis are vital for informing the public and the government’s response to COVID-19. This page is a summary of insights from our most recent analysis and will be updated as new publications are released. If you are looking for statistics on the number of COVID-19 cases in the UK, the latest figures are available on GOV.UK.

Social Licensing of Privacy-Encroaching Policies to Address the COVID-19 Pandemic (U.K.)

The nature of the COVID-19 pandemic may require governments to use big data technologies to help contain its spread. Countries that have managed to “flatten the curve”, (e.g., Singapore), have employed collocation tracking through mobile Wi-Fi, GPS, and Bluetooth as a strategy to mitigate the impact of COVID-19. Through collocation tracking, Government agencies may observe who you have been in contact with and when this contact occurred, thereby rapidly implementing appropriate measures to reduce the spread of COVID-19. The effectiveness of collocation tracking relies on the willingness of the population to support such measures, implying that government policy-making should be informed by the likelihood of public compliance. Gaining the social license — broad community acceptance beyond formal legal requirements — for collocation tracking requires the perceived public health benefits to outweigh concerns of personal privacy, security, and any potential risk of harm.

Seven tips to manage your mental health and well-being during the COVID-19 outbreak

Epidemic Modeling 101: Or why your CoVID19 exponential fits are wrong

How behavioural science data helps mitigate the COVID-19 crisis

In the current absence of medical treatment and vaccination, the unfolding COVID-19 pandemic can only be brought under control by massive and rapid behaviour change. To achieve this we need to systematically monitor and understand how different individuals perceive risk and what prompts them to act upon it, argues Cornelia Betsch.

Germany COVID-19 Snapshot MOnitoring (COSMO Germany): Monitoring knowledge, risk perceptions, preventive behaviours, and public trust in the current coronavirus outbreak in Germany

In a crisis such as the current outbreak of the newly emerged coronavirus, it is of utmost importance to monitor public perceptions of risk, protective and preparedness behaviours, public trust, as well as knowledge and misinformation to enable government spokespeople, the media, and health organizations to implement adequate responses (WHO Europe, 2017; World Health Organization, 2017). The purpose of this serial cross-sectional study COSMO is to allow rapid and adaptive monitoring of these variables over time and to assess the relations between risk perceptions, knowledge and misinformation to preparedness and protective behaviour regarding COVID-19 in Germany.

Survey Data and Human Computation for Improved Flu Tracking

While digital trace data from sources like search engines hold enormous potential for tracking and understanding human behavior, these streams of data lack information about the actual experiences of those individuals generating the data. Moreover, most current methods ignore or under-utilize human processing capabilities that allow humans to solve problems not yet solvable by computers (human computation). We demonstrate how behavioral research, linking digital and real-world behavior, along with human computation, can be utilized to improve the performance of studies using digital data streams. This study looks at the use of search data to track prevalence of Influenza-Like Illness (ILI). We build a behavioral model of flu search based on survey data linked to users online browsing data. We then utilize human computation for classifying search strings. Leveraging these resources, we construct a tracking model of ILI prevalence that outperforms strong historical benchmarks using only a limited stream of search data and lends itself to tracking ILI in smaller geographic units. While this paper only addresses searches related to ILI, the method we describe has potential for tracking a broad set of phenomena in near real-time.

2003.13822

2003.12614

Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: how 21st century GIS technologies are supporting the global fight against outbreaks and epidemics

In December 2019, a new virus (initially called ‘Novel Coronavirus 2019-nCoV’ and later renamed to SARS-CoV-2) causing severe acute respiratory syndrome (coronavirus disease COVID-19) emerged in Wuhan, Hubei Province, China, and rapidly spread to other parts of China and other countries around the world, despite China’s massive efforts to contain the disease within Hubei. As with the original SARS-CoV epidemic of 2002/2003 and with seasonal influenza, geographic information systems and methods, including, among other application possibilities, online real-or near-real-time mapping of disease cases and of social media reactions to disease spread, predictive risk mapping using population travel data, and tracing and mapping super-spreader trajectories and contacts across space and time, are proving indispensable for timely and effective epidemic monitoring and response. This paper offers pointers to, and describes, a range of practical online/mobile GIS and mapping dashboards and applications for tracking the 2019/2020 coronavirus epidemic and associated events as they unfold around the world. Some of these dashboards and applications are receiving data updates in near-real-time (at the time of writing), and one of them is meant for individual users (in China) to check if the app user has had any close contact with a person confirmed or suspected to have been infected with SARS-CoV-2 in the recent past. We also discuss additional ways GIS can support the fight against infectious disease outbreaks and epidemics.

The extent of the COVID-19 crisis is no more certain this week than it was last.

Experts Say The Coronavirus Outlook Has Worsened, But The Trajectory Is Still Unclear

Social and behavioral implications of changing COVID-19 measures

In the coming days and weeks, governments will increasingly re-consider their current COVID-19 strategies, for example, considering a possible shift from a partial or complete lockdown to less severe non-pharmaceutical measures (e.g., increasing people’s mobility, while still not allowing large gatherings), introducing new measures (e.g., making wearing masks the norm; see also this post), while still maintaining previous measures (physical distancing, hand washing, not touching your face etc.).One important question those governments are currently facing is how to implement and communicate any such changes while maintaining (or even increasing) compliance with those changes and previous measures.Let’s gather concrete issues and ideas that governments need to consider as they will be making decisions along these lines in the very near future.**An example related to communication: Authorities could talk about “lifting” or “shifting” non-pharmaceutical measures. “Lifting” sounds more like people can go back to how they were leading their lives, thus possibly compromising compliance with the changed measures. “Shifting” might be a better frame as it emphasizes more that people still need to do things differently than before.This is just one example of potentially many. Please add your ideas as comments below. Thanks!

Predicting a Pandemic: testing crowd wisdom and expert forecasting amidst the novel COVID-19 outbreak

Taking countermeasures to protect against future events requires predicting what the future will be like. In late 2019, a novel coronavirus known as NCov-2019 emerged in Wuhan, China, and has since spread to most countries in the world. Anticipatory responses by civilians facing the crisis have included self-isolation measures, extreme stockpiling of food or medical supplies, and other forms of preparation to meet the expected crisis. However, no consensus exists as to the accuracy of civilian expectations, nor toward the relative value of different informational sources used by citizens to build these expectations (e.g. mainstream news as opposed to an educational background in virology). In the present study, we used an online survey (n = 333 in final sample) to collect individual characteristics and general knowledge regarding viruses and the novel coronavirus, in addition to their forecasts for the various outcomes expected to result from it in the near future. This will allow for the individual correlates of accurate forecasting to be known by 2021, which could prove important for assigning relative weights to forecasts for other events in the future.

At a time when we are all thinking about how best to respond to the present global crisis, it seems timely to think also about how we, as the Cognitive Science community, can be most effective. What kind of science can we do, and how should we go about doing it? This blog post is an attempt to help fuel discussion on these issues in order to formulate the best community response. It offers a starting point for thinking about cognitive science and coronavirus.  Though thoughts first turn to medicine, virologists, and epidemologists, the CogSci community has many potential contributions to make. Research areas that are established cognitive science topics, ranging from e-learning, e-delivery, media literacy, through risk analysis, risk perception, decision-making, behaviour change, argumentation, or communication are suddenly in high demand. But as cognitive scientists, we also possess key skills: the ability to interface with AI, handle ‘big data’, engage in computational social science, and maybe first and foremost, modelling skills and an ability for model thinking. And, finally, the sheer disciplinary breadth of Cognitive Science, from computer science, through to anthropology and philosophy, can offer much needed, complementary, perspectives and views. While cognitive scientists around the world consider how their own research skills and ideas may usefully be applied, we should also spend some time rethinking and looking to adjust, how we go about doing science. 

COVID-19, Cognitive Science, And Adaptive Responding: What Can The CogSci Community Do?

Evidence suggests that the negative consequences of COVID-19 may extend far beyond its considerable death toll, having a significant impact on psychological well-being. Prior work has highlighted that previous epidemics are linked to elevated suicide rates, however, there is no research to date on the relationship between the COVID-19 pandemic and suicidal thoughts and behaviors. Utilizing an online survey, the current study aimed to better understand the presence, and extent, of the association between COVID-19-related experiences and past-month suicidal thoughts and behaviors among adults in the United States. Results support an association between several COVID-19-related experiences (i.e., general distress, fear of physical harm, effects of social distancing policies) and past-month suicidal ideation and attempts. Further, we found that a significant proportion of those with recent suicidal ideation explicitly link their suicidal thoughts to COVID-19. Exploratory analyses highlight a potential additional link between COVID-19 and suicidal behavior, suggesting that a portion of individuals may be intentionally exposing themselves to the virus with intent to kill themselves. These findings underscore the need for increased suicide risk screening and access to mental health services. Particular attention should be paid to employing public health campaigns to disseminate information on such services in order to reduce the enormity of distress and emotional impairment associated with COVID-19 in the United States.

Preliminary Investigation of the Association Between COVID-19 and Suicidal Thoughts and Behaviors in the U.S.

Interview with Jonathon Crystal about reducing face touches to reduce COVID-19 spread

Jonathon Crystal and I met online to talk about the first set of recommendations – to reduce face touching – made by the Behavioral Science Response to COVID-19 Working Group. Our hands are disease vectors, so by reducing the times we touch our faces, we reduce the chances of transferring the virus from our hands to our respiratory systems. The audio from our interview was used for the podcast. But we also had video, which gave me the perfect opportunity to count the number of times both of us touched our faces. Over the course of our 20-minute chat, Jonathon touched his face a respectable 3 times. I, on the other hand, touched my face a whopping 22 times!

The ongoing coronavirus pandemic is one of the biggest health crises of our time. In response to this global problem, various institutions around the world had soon issued evidence-based prevention guidelines. However, these guidelines, which were designed to slow the spread of COVID-19 and contribute to public well-being, are deliberately disregarded or ignored by some individuals. In the present study, we aimed to develop and test a multivariate model that could help us identify individual characteristics that make a person more/less likely to comply with COVID-19 prevention guidelines. A total of 617 participants took part in the online survey and answered several questions related to socio-demographic variables, political conservatism, religious orthodoxy, conspiracy ideation, intellectual curiosity, trust in science, COVID-19 risk perception and compliance with COVID-19 prevention guidelines. The results of structural equation modeling (SEM) show that COVID-19 risk perception and trust in science both independently predict compliance with COVID-19 prevention guidelines, while the remaining variables in the model (political conservatism, religious orthodoxy, conspiracy ideation and intellectual curiosity) do so via the mediating role of trust in science. The described model exhibited an acceptable fit (χ2(1611) = 2485.84, p < .001, CFI = .91, RMSEA = .032, SMR = .055). These findings thus provide empirical support for the proposed multivariate model and underline the importance of trust in science in explaining the different levels of compliance with COVID-19 prevention guidelines.

Modeling compliance with COVID-19 prevention guidelines: The critical role of trust in science

Flocking in complex environments—Attention trade-offs in collective information processing

The ability of biological and artificial collectives to outperform solitary individuals in a wide variety of tasks depends crucially on the efficient processing of social and environmental information at the level of the collective. Here, we model collective behavior in complex environments with many potentially distracting cues. Counter-intuitively, large-scale coordination in such environments can be maximized by strongly limiting the cognitive capacity of individuals, where due to self-organized dynamics the collective self-isolates from disrupting information. We observe a fundamental trade-off between coordination and collective responsiveness to environmental cues. Our results offer important insights into possible evolutionary trade-offs in collective behavior in biology and suggests novel principles for design of artificial swarms exploiting attentional bottlenecks.

Redefining vulnerability in the era of COVID-19

What does it mean to be vulnerable? Vulnerable groups of people are those that are disproportionally exposed to risk, but who is included in these groups can change dynamically. A person not considered vulnerable at the outset of a pandemic can become vulnerable depending on the policy response. The risks of sudden loss of income or access to social support have consequences that are difficult to estimate and constitute a challenge in identifying all those who might become vulnerable. Certainly, amid the COVID-19 pandemic, vulnerable groups are not only elderly people, those with ill health and comorbidities, or homeless or underhoused people, but also people from a gradient of socioeconomic groups that might struggle to cope financially, mentally, or physically with the crisis.

The coronavirus disease 2019 (COVID-19) outbreak, which started in the Hubei province of China in 2019, has now spread to all continents, affecting 177 countries by March 27, 2020.1Dong E Du H Gardner L An interactive web-based dashboard to track COVID-19 in real time.Lancet Infect Dis. 2020; (published online Feb 19.)https://doi.org/10.1016/S1473-3099(20)30120-1Google Scholar Successful efforts in containing the COVID-19 virus in Asia resulted in WHO declaring Europe as the epicentre of the disease on March 13.2World Health OrganisationMedia briefing on COVID-19.https://www.pscp.tv/w/1LyxBNlZOAyxNDate: March 13, 2020Date accessed: March 22, 2020Google Scholar Whether warmer temperatures will slow the spread of the COVID-19 virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been a point of much speculation. This hypothesis has led some European countries to produce initial policies relying on decreased transmission rates during the summer months,3Topping A Coronavirus: medical chief says UK hopes to delay any outbreak until summer. The Guardian.https://www.theguardian.com/world/2020/feb/13/coronavirus-medical-chief-says-uk-hopes-to-delay-any-outbreak-until-summerDate: Feb 13, 2020Date accessed: March 23, 2020Google Scholar and the belief that African countries will face smaller epidemics than their European counterparts. However, no strong evidence base exists for such claims; SARS-CoV-2 might have simply arrived later to warmer countries.

COVID-19 pandemic in west Africa

Impact of school closures for COVID-19 on the US health-care workforce and net mortality: a modelling study

The coronavirus disease 2019 (COVID-19) pandemic is leading to social (physical) distancing policies worldwide, including in the USA. Some of the first actions taken by governments are the closing of schools. The evidence that mandatory school closures reduce the number of cases and, ultimately, mortality comes from experience with influenza or from models that do not include the effect of school closure on the health-care labour force. The potential benefits from school closures need to be weighed against costs of health-care worker absenteeism associated with additional child-care obligations. In this study, we aimed to measure child-care obligations for US health-care workers arising from school closures when these are used as a social distancing measure. We then assessed how important the contribution of health-care workers would have to be in reducing mortality for their absenteeism due to child-care obligations to undo the benefits of school closures in reducing the number of cases.

School closures are likely to have a relatively small impact on the spread of Covid-19 and should be weighed against their profound economic and social consequences, particularly for the most vulnerable children, according to a UK study.

School closures likely to have little impact on spread of coronavirus, study finds

Why inequality could spread COVID-19

Pandemics rarely affect all people in a uniform way. The Black Death in the 14th century reduced the global population by a third, with the highest number of deaths observed among the poorest populations.1Duncan CJ Scott S (2005). What caused the black death?.Postgrad Med J. 2005; 81: 315-320Crossref PubMed Scopus (48) Google Scholar Densely populated with malnourished and overworked peasants, medieval Europe was a fertile breeding ground for the bubonic plague. Seven centuries on—with a global gross domestic product of almost US$100 trillion—is our world adequately resourced to prevent another pandemic?2Roser M The short history of global living conditions and why it matters that we know it.https://ourworldindata.org/a-history-of-global-living-conditions-in-5-chartsDate: 2019Date accessed: March 23, 2020Google Scholar Current evidence from the coronavirus disease 2019 (COVID-19) pandemic would suggest otherwise.

School closure and management practices during coronavirus outbreaks including COVID-19: a rapid systematic review

In response to the coronavirus disease 2019 (COVID-19) pandemic, 107 countries had implemented national school closures by March 18, 2020. It is unknown whether school measures are effective in coronavirus outbreaks (eg, due to severe acute respiratory syndrome [SARS], Middle East respiratory syndrome, or COVID-19). We undertook a systematic review by searching three electronic databases to identify what is known about the effectiveness of school closures and other school social distancing practices during coronavirus outbreaks. We included 16 of 616 identified articles. School closures were deployed rapidly across mainland China and Hong Kong for COVID-19. However, there are no data on the relative contribution of school closures to transmission control. Data from the SARS outbreak in mainland China, Hong Kong, and Singapore suggest that school closures did not contribute to the control of the epidemic. Modelling studies of SARS produced conflicting results. Recent modelling studies of COVID-19 predict that school closures alone would prevent only 2–4% of deaths, much less than other social distancing interventions. Policy makers need to be aware of the equivocal evidence when considering school closures for COVID-19, and that combinations of social distancing measures should be considered. Other less disruptive social distancing interventions in schools require further consideration if restrictive social distancing policies are implemented for long periods.

COVID-19 HSRM

The Health System Response Monitor (HSRM) has been designed in response to the COVID-19 outbreak to collect and organize up-to-date information on how countries are responding to the crisis. It focuses primarily on the responses of health systems but also captures wider public health initiatives. This is a joint undertaking of the WHO Regional Office for Europe, the European Commission, and the European Observatory on Health Systems and Policies.

COVID-19 will not leave behind refugees and migrants

Never has the “leave no one behind” pledge felt more urgent. As nations around the world implement measures to control the spread of SARS-CoV-2, including lockdowns and restrictions on individuals’ movements, they must heed their global commitments. When member states adopted the UN 2030 Agenda for Sustainable Development, they promised to ensure no one will be left behind. Chief among the world's most vulnerable people are refugees and migrants. The COVID-19 crisis puts these groups at enormous risk. Yet global pandemic efforts have so far failed in their duty of care to refugees and migrants.

The outbreak of COVID-19 and subsequent initiatives and policy measures trigger many crucial privacy and data protection law issues. Many of them are of direct interest for LSTS researchers, most notably in the context of the Brussels Privacy Hub (BPH) work on data protection in humanitarian action. In this page we aim to share useful resources on these matters.

Data protection law and the COVID-19 outbreak

The Cognitive Science Society is conducting an informal survey of any researchers (at any career stage) in related disciplines (cognitive science, psychology, philosophy, computer science, linguistics, education, anthropology, etc.) to gather and share back opportunities for cognitive scientists to respond to the Covid-19 pandemic.

Cognitive Science and Covid-19

On the importance of trip destination for modeling individual human mobility patterns

Understanding human mobility patterns and reproducing them accurately is crucial in a wide range of applications from public health, to transport and urban planning. Still the relationship between the effort individuals will to invest in a trip and its purpose importance is not taken into account in the individual mobility models in the literature. Here, we address this issue by introducing a model hypothesizing a relation between the importance of a trip and the distance traveled. In most practical cases, quantifying such importance is undoable. We overcome this difficulty by focusing on shopping trips (for which we have empirical data) and by taking the price of items as a proxy. Our model is able to reproduce the long-tailed distribution in travel distances empirically observed and to explain the collapse of the curves for different price ranges. Our results show the presence of a genuine scaling relation controlled only by the mean distance traveled connected, as hypothesized, to the item value.

2004.01435

Our analysis of the social distancing measures implemented in Santiago (up to March 18th) in terms of mobility patterns. In line with observations in other countries, in the initial phase, variations are visible but not drastic. Traffic and patterns are similar to the weekends.

Outbreaks of infectious diseases present a global threat to human health and are considered a major health-care challenge. One major driver for the rapid spatial spread of diseases is human mobility. In particular, the travel patterns of individuals determine their spreading potential to a great extent. These travel behaviors can be captured and modelled using novel location-based data sources, e.g., smart travel cards, social media, etc. Previous studies have shown that individuals who cannot be characterized by their most frequently visited locations spread diseases farther and faster; however, these studies are based on GPS data and mobile call records which have position uncertainty and do not capture explicit contacts. It is unclear if the same conclusions hold for large scale real-world transport networks. In this paper, we investigate how mobility patterns impact disease spread in a large-scale public transit network of empirical data traces. In contrast to previous findings, our results reveal that individuals with mobility patterns characterized by their most frequently visited locations and who typically travel large distances pose the highest spreading risk.

How mobility patterns drive disease spread: A case study using public transit passenger card travel data

Evaluating COVID-19 contact tracing apps? Here are 8 privacy questions we think you should ask.

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

Covid Economics: Vetted and Real-Time Papers

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

Pandemics

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

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

Protecting the psychological wellbeing of healthcare staff during the coronavirus pandemic

“We don’t just want to sit on the couch”

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

Viewpoint: It’s time to coordinate the global COVID-19 research effort

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

Join the PSA’s Rapid-Response COVID-19 Project