10.3390/vaccines8020276

Childhood vaccination has been a milestone in the control of infectious diseases. However, even in countries offering equal access to vaccination, a number of vaccine-preventable diseases have re-emerged. Suboptimal vaccination coverage has been called into question. The aim was to explore socioeconomic inequalities in vaccine hesitancy and outright refusal. Families with at least one child aged between 3 months and 7 years were involved through an online survey. Families were classified as provaccine, hesitant, or antivaccine. The association between socioeconomic determinants and hesitancy/refusal was investigated with a logistic-regression model. A total of 3865 questionnaires were collected: 64.0% of families were provaccine, 32.4% hesitant, and 3.6% antivaccine. Rising levels of perceived economic hardship were associated with hesitancy (adjusted odds ratio (AOR) from 1.34 to 1.59), and lower parental education was significantly associated with refusal (AOR from 1.89 to 3.39). Family economic hardship and parental education did not move in parallel. Economic hardship was a determinant of hesitancy. Lower education was a predictor of outright refusal without affecting hesitancy. These findings may serve as warnings, and further explanations of socioeconomic inequities are needed even in universal healthcare systems. Insight into these factors is necessary to improve convenience and remove potential access issues.

Socioeconomic Determinants in Vaccine Hesitancy and Vaccine Refusal in Italy

A century after the 1918 pandemic, South America’s largest country has passed Britain to claim the world’s second-highest death toll

'The country is adrift': echoes of Spanish flu as Brazil's Covid-19 catastrophe deepens

Health-care workers have been on the job throughout the pandemic. What can they teach us about the safest way to lift a lockdown?

Amid the Coronavirus Crisis, a Regimen for Reëntry

live now! join us for an #IMPACTfulChats event with advisors @MS_Martinez_MD interviewing 7 y/o Olivia Tyler who is helping get masks out to the the homeless in Chicago and beyond! @IMPACT4HC

I am more convinced than ever that behavioral scientists have a role to play to help stem the pandemic. As many localities begin to lift lockdown restrictions, it will be more important than ever to adhere to social distancing and hygiene recommendations from the CDC and WHO. Our success in preventing a resurgence of COVID-19 cases and deaths depends on changing human behavior.

Mobilizing behavioral scientists to respond to COVID-19

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

COVID-19

A 56-second overview of the first 5 months of 2020.

On Monday, May 18, at 12:30 PM ET, John Cochrane joined the Princeton Bendheim Center for Finance for a webinar on re-opening the economy after COVID-19. Cochrane is a Senior Fellow at the Hoover Institution at Stanford University and author of The Grumpy Economist blog. The webinar began with introductions by Markus Brunnermeier, Director of Princeton's Bendheim Center for Finance.

John Cochrane on COVID 19 and re-opening the economy

Richard Horton describes the management of the outbreak as ‘the greatest science policy failure of a generation’

Lancet editor attacks UK government for 'catastrophic' handling of Covid-19 pandemic

Third lecture (seminar) of the Net-COVID online series: Understanding and Exploring Network Epidemiology in the Time of Coronavirus. Seminar by Sam Scarpino of Northeastern University. See go.umd.edu/net-covid for more information about the online series.

Net-COVID Session3A: Human mobility and control measures in the COVID-19 epidemic by Sam Scarpino

The Royal Society is currently using its convening power to support efforts to model the Coronavirus (COVID-19) pandemic and guide the UK’s response.  The Rapid Assistance in Modelling the Pandemic (RAMP) initiative is bringing modelling expertise from a diverse range of disciplines to support the pandemic modelling community already working on Coronavirus (COVID-19). 

Rapid Assistance in Modelling the Pandemic: RAMP

SPI-M gives expert advice to the Department of Health and Social Care and wider UK government on scientific matters relating to the UK’s response to an influenza pandemic (or other emerging human infectious disease threats). The advice is based on infectious disease modelling and epidemiology.

Scientific Pandemic Influenza Group on Modelling (SPI-M)

This site is dedicated to the understanding of risk taking behaviour, risk compensation and risk homeostasis..   People alter their behaviour in response to the implementation of health and safety measures, but the riskiness of the way they behave will not change, unless those measures are capable of motivating people to alter the amount of risk they are willing to incur.   Multiple researchers across the globe have participated in acquiring an understanding of this fascinating behavioural phenomenon.Your interest and participation in this on-going research will contribute greatly to finding ways in decrease risk-taking behaviour that results in injury and death.

Welcome to the Risk Homeostasis and Risk Compensation Resource Centre

Select Committees work in both Houses. They check and report on areas ranging from the work of government departments to economic affairs. The results of these inquiries are public and many require a response from the government.

Select Committees

POST is a bicameral body that bridges research and policy. One of our key activities is writing short, impartial, and accessible evidence syntheses. These briefings, commonly known as POSTnotes, tackle a range of topics in science, technology and the social sciences. POSTnotes are designed for Members of Parliament. But they are also used by many other stakeholders, such as government, the media and the third sector. To make sure they are comprehensive and balanced, we need insights from the expert community. This includes experts from academia, industry, government, the third sector and beyond. Could that be you?

Contributing to a POSTnote as an expert

This site tracks and lists ggplot2 extensions developed by R users in the community. The aim is to make it easy for R users to find developed extensions.

ggplot2 extensions

If you have been tracking the numbers for the COVID-19 pandemic, you must have looked at dozens of models and tried to make some comparisons. Even under the best of situations it is difficult to compare models, and this is especially true if you don’t have sufficient domain knowledge. Experts tend to leave out assumptions and background material that they know other experts will take for granted. This leaves newcomers pretty much on their own. It has been my experience that a good way for an R literate person to begin to acquire knowledge in a new field is to find some appropriate packages, study the vignettes, work through the examples, and read whatever source material they may reference. So, this post shows how one might go about finding those appropriate packages. Also, I thought it would be interesting to see what kind of special resources are available to epidemiologists working in R beyond the basic statistical infrastructure and packages for data manipulation and visualization.

An R View into Epidemiology

Thailand says it had no new virus infections or deaths over the past day - the first time in almost three weeks there were no new cases. It's also been 17 days since a local transmission was recorded. All other recent cases had been imported by people returning from abroad. The country's total stands at 3,125 confirmed infections, while 58 deaths have been linked to Covid-19.

Thailand reports zero cases, zero deaths

The Centers for Disease Control and Prevention recommends social distancing (We use the term social distancing in concordance with the CDC, but note the call to shift to the use of physical distancing as a more appropriate term) as a prevention strategy for COVID-19; however, there is evidence this recommendation has not been taken seriously by adolescent and young adult (AYA) populations. One public example of this was the participation of many students in traditional Spring Break activities at a time when the World Health Organization had already declared COVID-19 a pandemic and the U.S. had 1,000 confirmed cases. Critical reflection on why AYAs have not taken the threat more seriously is necessary to more effectively engage them in future efforts to slow the spread of the virus. The public health messages aimed at AYAs need to be evaluated for the extent to which they follow best practices in message design such as being grounded in theory and developed in a participatory manner. To date, COVID-19 messaging for AYA populations has been deficient in both these areas.

10.1016/j.jadohealth.2020.05.013

Critical Reflections on COVID-19 Communication Efforts Targeting Adolescents and Young Adults

Social network based applications have experienced exponential growth in recent years. One of the reasons for this rise is that this application domain offers a particularly fertile place to test and develop the most advanced computational techniques to extract valuable information from the Web. The main contribution of this work is three-fold: (1) we provide an up-to-date literature review of the state of the art on social network analysis (SNA); (2) we propose a set of new metrics based on four essential features (or dimensions) in SNA; (3) finally, we provide a quantitative analysis of a set of popular SNA tools and frameworks. We have also performed a scientometric study to detect the most active research areas and application domains in this area. This work proposes the definition of four different dimensions, namely Pattern & Knowledge discovery, Information Fusion & Integration, Scalability, and Visualization, which are used to define a set of new metrics (termed degrees) in order to evaluate the different software tools and frameworks of SNA (a set of 20 SNA-software tools are analyzed and ranked following previous metrics). These dimensions, together with the defined degrees, allow evaluating and measure the maturity of social network technologies, looking for both a quantitative assessment of them, as to shed light to the challenges and future trends in this active area.

10.1016/j.inffus.2020.05.009

The Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools

2005.07952

Understanding the characteristics of public attention and perception is an essential prerequisite for appropriate crisis management during adverse health events. This is even more crucial during a pandemic such as COVID-19, as primary responsibility of risk management is not centralized to a single institution, but distributed across society. While numerous studies utilize Twitter data in descriptive or predictive context during COVID-19 pandemic, causal modeling of public attention has not been investigated. In this study, we propose a causal inference approach to discover and quantify causal relationships between pandemic characteristics (e.g. number of infections and deaths) and Twitter activity as well as public sentiment. Our results show that the proposed method can successfully capture the epidemiological domain knowledge and identify variables that affect public attention and perception. We believe our work contributes to the field of infodemiology by distinguishing events that correlate with public attention from events that cause public attention.

Causal Modeling of Twitter Activity During COVID-19

2005.12423

The spread of COVID-19 has sparked racism, hate, and xenophobia in social media targeted at Chinese and broader Asian communities. However, little is known about how racial hate spreads during a pandemic and the role of counterhate speech in mitigating the spread. Here we study the evolution and spread of anti-Asian hate speech through the lens of Twitter. We create COVID-HATE, the largest dataset of anti-Asian hate and counterhate spanning three months, containing over 30 million tweets, and a social network with over 87 million nodes. By creating a novel hand-labeled dataset of 2,400 tweets, we train a text classifier to identify hate and counterhate tweets that achieves an average AUROC of 0.852. We identify 891,204 hate and 200,198 counterhate tweets in COVID-HATE. Using this data to conduct longitudinal analysis, we find that while hateful users are less engaged in the COVID-19 discussions prior to their first anti-Asian tweet, they become more vocal and engaged afterwards compared to counterhate users. We find that bots comprise 10.4% of hateful users and are more vocal and hateful compared to non-bot users. Comparing bot accounts, we show that hateful bots are more successful in attracting followers compared to counterhate bots. Analysis of the social network reveals that hateful and counterhate users interact and engage extensively with one another, instead of living in isolated polarized communities. Furthermore, we find that hate is contagious and nodes are highly likely to become hateful after being exposed to hateful content. Importantly, our analysis reveals that counterhate messages can discourage users from turning hateful in the first place. Overall, this work presents a comprehensive overview of anti-Asian hate and counterhate content during a pandemic. The COVID-HATE dataset is available at this http URL.

Racism is a Virus: Anti-Asian Hate and Counterhate in Social Media during the COVID-19 Crisis

10.1590/0102-311X00088120

This essay discusses the proliferation of discourses about the COVID-19 pandemic, presenting the challenges both to science and public policies that such an information overload present, having Collins’ sociology of expertise as a theoretical framework.

Trying to make sense out of chaos: science, politics and the COVID-19 pandemic

CRediT (Contributor Roles Taxonomy) is high-level taxonomy, including 14 roles, that can be used to represent the roles typically played by contributors to scientific scholarly output. The roles describe each contributor’s specific contribution to the scholarly output.

CRediT – Contributor Roles Taxonomy

2006.06446

The exposure and consumption of information during epidemic outbreaks may alter risk perception, trigger behavioural changes, and ultimately affect the evolution of the disease. It is thus of the uttermost importance to map information dissemination by mainstream media outlets and public response. However, our understanding of this exposure-response dynamic during COVID-19 pandemic is still limited. In this paper, we provide a characterization of media coverage and online collective attention to COVID-19 pandemic in four countries: Italy, United Kingdom, United States, and Canada. For this purpose, we collect an heterogeneous dataset including 227,768 online news articles and 13,448 Youtube videos published by mainstream media, 107,898 users posts and 3,829,309 comments on the social media platform Reddit, and 278,456,892 views to COVID-19 related Wikipedia pages. Our results show that public attention, quantified as users activity on Reddit and active searches on Wikipedia pages, is mainly driven by media coverage and declines rapidly, while news exposure and COVID-19 incidence remain high. Furthermore, by using an unsupervised, dynamical topic modeling approach, we show that while the attention dedicated to different topics by media and online users are in good accordance, interesting deviations emerge in their temporal patterns. Overall, our findings offer an additional key to interpret public perception/response to the current global health emergency and raise questions about the effects of attention saturation on collective awareness, risk perception and thus on tendencies towards behavioural changes.

Collective response to the media coverage of COVID-19 Pandemic on Reddit and Wikipedia

Transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently in marked decline in many countries in Europe, North America, and parts of Asia, following unprecedented governmental interventions aiming to substantially reduce travel and physical contact between individuals. There are two possible and very different explanations for this decline.First, the observed declines in cases and deaths could be due to lockdowns (taken to include public orders to stay at home, bans on public gatherings with less than ten people, and curfew of all age groups), social distancing, and other interventions. This would imply that the epidemic is still at a relatively early stage and that a large proportion of the population therefore remain susceptible. Under such a scenario, there is a high risk of renewed transmission if interventions or behavioural modifications are completely relaxed. This first explanation also is consistent with a high infection fatality ratio (IFR) in order to explain the number of deaths that have occurred to date.• View related content for this articleSecond, the observed declines in cases and deaths could be due to the achievement of herd immunity. This would imply that a large proportion of the population are now protected from infection, either through acquisition of immunity following previous infection or through other natural means (such as cross protection from other coronaviruses). Under such a scenario, further declines in cases and deaths are to be expected even in the absence of interventions or behavioural modifications. If one assumes that a large proportion of the population has been infected, this explanation implies a very low IFR to explain the number of deaths that have occurred to date.Identifying the most probable explanation is key to any future plans to lift social distancing and travel restrictions. It is also critical when considering subsequent public health responses aimed at reducing morbidity and mortality, especially in the context of the wider economic and health impacts of COVID-19 mitigation and suppression strategies.

10.1016/S0140-6736(20)31357-X

Have deaths from COVID-19 in Europe plateaued due to herd immunity?

COVID-19 was declared a pandemic by WHO on March 11, 2020, the first non-influenza pandemic, affecting more than 200 countries and areas, with more than 5·9 million cases by May 31, 2020. Countries have developed strategies to deal with the COVID-19 pandemic that fit their epidemiological situations, capacities, and values. We describe China's strategies for prevention and control of COVID-19 (containment and suppression) and their application, from the perspective of the COVID-19 experience to date in China. Although China has contained severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and nearly stopped indigenous transmission, a strong suppression effort must continue to prevent re-establishment of community transmission from importation-related cases. We believe that case finding and management, with identification and quarantine of close contacts, are vitally important containment measures and are essential in China's pathway forward. We describe the next steps planned in China that follow the containment effort. We believe that sharing countries' experiences will help the global community manage the COVID-19 pandemic by identifying what works in the struggle against SARS-CoV-2.

10.1016/S0140-6736(20)31278-2

Active case finding with case management: the key to tackling the COVID-19 pandemic

Epidemiologists offer eight tips for reading mathematical models

A Guide to Understanding Pandemic Predictions

"What are the behavioural implications of moving to a new, more shorter distance rule?" What impacts (positive or negative), concerns, and side effects do you foresee? Give your answers here: https://reddit.com/r/BehSciAsk/comments/h0zaco/scibehs_first_policy_problem_challenge_relaxing/… or in a reply to this tweet!

Introducing "Horizon Scanning" - a new http://scibeh.org activity designed to help identify policy relevant concerns and evidence that lie ahead for upcoming, future issues. One form this will take is as a recurring "Policy Problem Challenge". Our first one is this:

10.3758/s13414-011-0170-2

There is controversy over the existence, nature, and cause of error in egocentric distance judgments. One proposal is that the systematic biases often found in explicit judgments of egocentric distance along the ground may be related to recently observed biases in the perceived declination of gaze (Durgin & Li, Attention, Perception, & Psychophysics, in press), To measure perceived egocentric distance nonverbally, observers in a field were asked to position themselves so that their distance from one of two experimenters was equal to the frontal distance between the experimenters. Observers placed themselves too far away, consistent with egocentric distance underestimation. A similar experiment was conducted with vertical frontal extents. Both experiments were replicated in panoramic virtual reality. Perceived egocentric distance was quantitatively consistent with angular bias in perceived gaze declination (1.5 gain). Finally, an exocentric distance-matching task was contrasted with a variant of the egocentric matching task. The egocentric matching data approximate a constant compression of perceived egocentric distance with a power function exponent of nearly 1; exocentric matches had an exponent of about 0.67. The divergent pattern between egocentric and exocentric matches suggests that they depend on different visual cues.

The underestimation of egocentric distance: evidence from frontal matching tasks

Researchers found that keeping a distance of more than one metre from other people was associated with a much lower risk of infection.

Coronavirus: reducing distance to one metre increases transmission risk

BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 and is spread person-to-person through close contact. We aimed to investigate the effects of physical distance, face masks, and eye protection on virus transmission in health-care and non-health-care (eg, community) settings.MethodsWe did a systematic review and meta-analysis to investigate the optimum distance for avoiding person-to-person virus transmission and to assess the use of face masks and eye protection to prevent transmission of viruses. We obtained data for SARS-CoV-2 and the betacoronaviruses that cause severe acute respiratory syndrome, and Middle East respiratory syndrome from 21 standard WHO-specific and COVID-19-specific sources. We searched these data sources from database inception to May 3, 2020, with no restriction by language, for comparative studies and for contextual factors of acceptability, feasibility, resource use, and equity. We screened records, extracted data, and assessed risk of bias in duplicate. We did frequentist and Bayesian meta-analyses and random-effects meta-regressions. We rated the certainty of evidence according to Cochrane methods and the GRADE approach. This study is registered with PROSPERO, CRD42020177047.FindingsOur search identified 172 observational studies across 16 countries and six continents, with no randomised controlled trials and 44 relevant comparative studies in health-care and non-health-care settings (n=25 697 patients). Transmission of viruses was lower with physical distancing of 1 m or more, compared with a distance of less than 1 m (n=10 736, pooled adjusted odds ratio [aOR] 0·18, 95% CI 0·09 to 0·38; risk difference [RD] −10·2%, 95% CI −11·5 to −7·5; moderate certainty); protection was increased as distance was lengthened (change in relative risk [RR] 2·02 per m; pinteraction=0·041; moderate certainty). Face mask use could result in a large reduction in risk of infection (n=2647; aOR 0·15, 95% CI 0·07 to 0·34, RD −14·3%, −15·9 to −10·7; low certainty), with stronger associations with N95 or similar respirators compared with disposable surgical masks or similar (eg, reusable 12–16-layer cotton masks; pinteraction=0·090; posterior probability >95%, low certainty). Eye protection also was associated with less infection (n=3713; aOR 0·22, 95% CI 0·12 to 0·39, RD −10·6%, 95% CI −12·5 to −7·7; low certainty). Unadjusted studies and subgroup and sensitivity analyses showed similar findings.InterpretationThe findings of this systematic review and meta-analysis support physical distancing of 1 m or more and provide quantitative estimates for models and contact tracing to inform policy. Optimum use of face masks, respirators, and eye protection in public and health-care settings should be informed by these findings and contextual factors. Robust randomised trials are needed to better inform the evidence for these interventions, but this systematic appraisal of currently best available evidence might inform interim guidance.

10.1016/S0140-6736(20)31142-9

Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis

Reversing lockdown is perilous, but we cannot sacrifice our children’s education indefinitely

The cost of keeping schools closed will be dreadful

Objective: The aim of the study was to compare uni- and multidimensional models of social isolation to improve the specificity of determining associations between social isolation and frailty. Methods: The study included participants aged ≥60 years from the English Longitudinal Study of Ageing assessed for social isolation and frailty (frailty index and Fried phenotype) over a 4-year period. Factor analysis assessed whether social isolation was multidimensional. Multiple regression analysis was used to assess specificity in associations between social isolation and frailty over time. Results: Social isolation comprises social isolation from nuclear family, other immediate family, and wider social networks. Over time, social isolation from a wider social network predicted higher frailty index levels, and higher frailty index and Fried phenotype levels predicted greater social isolation from a wider social network. Discussion: Social isolation is multidimensional. The reciprocal relationship between social isolation from wider social networks and accumulating frailty deficits, and frailty as a clinical syndrome influencing social isolation from social networks is discussed.

10.1177/0898264320923245

Frailty and Social Isolation: Comparing the Relationship between Frailty and Unidimensional and Multifactorial Models of Social Isolation

Coronavirus was brought into the UK on at least 1,300 separate occasions, a major analysis of the genetics of the virus shows.

Coronavirus came to UK 'on at least 1,300 separate occasions'

Today, we’re going to use a lot of data and charts to answer these questions:What is happening in Sweden?How bad is the virus, really? How many people does it infect? Hurt? Kill?Who does it affect? Can we just protect the weak?What’s best for the economy?Here’s what you’re going to learn:Sweden is suffering tremendously in cases and deaths. Yet few people have been infected yet. They are a long way from Herd Immunity.Between 0.5% to 1.5% of infected die from the coronavirus.Left uncontrolled, it can kill between 0.4% and 1% of the entire population.Many more suffer conditions we don’t yet understand.Unfortunately, that death and sickness toll is far from having bought us Herd Immunity anywhere in the world.Only protecting those most at risk sounds great. It’s a fantasy today.Even if Sweden’s economy has remained mostly open, it has still suffered as much as others.From now on, it might start doing worse.Sweden now has regrets. But not enough. It can control the virus without a lockdown if it acknowledges its mistakes and takes the right measures.Other countries, like the US or the Netherlands, are toying with a Herd Immunity strategy. It will only cause more economic loss and death.

Coronavirus: Should We Aim for Herd Immunity Like Sweden?

Background: Insights from epidemiologic models have helped to guide and improve understanding of mitigation policies for coronavirus disease 2019 (COVID-19) across the globe. As the pandemic progresses, models can be used to quantify what may unfold when such measures are relaxed.Objective: To explore the effect of physical distancing measures on COVID-19 transmission in the population of Ontario, Canada.Methods and Findings: We previously described a transmission model of COVID-19, stratified by age and health status, in the Canadian province of Ontario (1). It evaluated nonpharmaceutical interventions to control the COVID-19 pandemic and preserve intensive care unit (ICU) capacity. The model found that physical distancing effectively mitigated spread but needed to be applied for long durations in either a sustained manner or with periodic dialing up and down of restrictions to prevent resurgence of infections and keep the number of cases requiring ICU care below ICU capacity.

10.7326/M20-2945

Risk for COVID-19 Resurgence Related to Duration and Effectiveness of Physical Distancing in Ontario, Canada