Background: A novel coronavirus disease (COVID-19) outbreak has now spread to a number of countries worldwide. While sustained transmission chains of human-to-human transmission suggest high basic reproduction number R0, variation in the number of secondary transmissions (often characterised by so-called superspreading events) may be large as some countries have observed fewer local transmissions than others. Methods: We quantified individual-level variation in COVID-19 transmission by applying a mathematical model to observed outbreak sizes in affected countries. We extracted the number of imported and local cases in the affected countries from the World Health Organization situation report and applied a branching process model where the number of secondary transmissions was assumed to follow a negative-binomial distribution. Results: Our model suggested a high degree of individual-level variation in the transmission of COVID-19. Within the current consensus range of R0 (2-3), the overdispersion parameter k of a negative-binomial distribution was estimated to be around 0.1 (median estimate 0.1; 95% CrI: 0.05-0.2 for R0 = 2.5), suggesting that 80% of secondary transmissions may have been caused by a small fraction of infectious individuals (~10%). A joint estimation yielded likely ranges for R0 and k (95% CrIs: R0 1.4-12; k 0.04-0.2); however, the upper bound of R0 was not well informed by the model and data, which did not notably differ from that of the prior distribution. Conclusions: Our finding of a highly-overdispersed offspring distribution highlights a potential benefit to focusing intervention efforts on superspreading. As most infected individuals do not contribute to the expansion of an epidemic, the effective reproduction number could be drastically reduced by preventing relatively rare superspreading events.

Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China [version 1; peer review: 1 approved]

The pursuit of money from wealthy donors distorts the research process—and yields flashy projects that don’t help and don’t work.

The Problem With Sugar-Daddy Science

“Disinformation starts at home” series: When disinformation is discussed as part of the 2016 elections, the realignment of the global order, or overarching issues connected to adversarial states, it becomes an issue that seems huge and far away for many people. We are all prone to believing that disinformation is something that works on someone else, not on us. But the truth is, it works on all of us. And more importantly, it is at play in the communities around us, and impacting our neighbors, our behavior, how we make decisions, and the requests that we make of government, as well as the decisions that local governments and leaders are making. This series will explore how disinformation is impacting local communities, and why it matters.

Disinformation Starts at Home

A preprint, posted on medRxiv, reports on air pollution in England and COVID-19 severity

expert reaction to preprint on air pollution in England and COVID-19 severity

Accurate antibody testing is a critical tool to determine if the pandemic has slowed enough to begin restarting the economy.

1 in 5 New Yorkers May Have Had Covid-19, Antibody Tests Suggest

Research, published in the International Journal of Clinical Practice, reports on the number of people on the UK people who are likely to have already been infected by the COVID-19 virus.

expert reaction to study reporting the suggestion that 25% of people in the UK are likely to have already been infected by the COVID-19 virus

24. I'm sorry that Dr. Heald felt that I was being being insulting by calling his study "odd". I should have taken high road that he did when questioned about his odd assertion that bonfire smoke is a vector for COVID-19.

23. I do legitimately feel for those risking their own safety to work on the frontline. Though you know what would make their jobs safer and less hectic? Not publishing dangerous, unjustified policy advice such as the notion that it is safest to reopen the hardest-hit areas.

22. And finally, speaking of causal inference and directionality, when I read this paper I can't help but wonder whether science is driving policy aims, or vice versa. /fin

21. The authors end by drawing conclusions about about economic and social factors that they did not even pretend to address in the paper. This sort of thing feels OK to me in an OpEd, but not in a scientific manuscript.

20. The problematic (if implicit) causal inference in this paper leads to a bizarre and I think dangerous conclusion, should anyone take it seriously. This is an extraordinary claim, countering common sense, with immediate relevance. One needs far stronger evidence in my view.

19. In the discussion the authors suggest their regression reveals susceptible depletion. But the historic number of confirmed cases is likely influenced by the same common causes that influence declines in R, e.g. control measures. This is a huge causal inference failure.

18. And then we get to the conclusion: the authors think COVID19 is about 1/5th as deadly as most others think, and about 5-10 times as prevalent. I.e., we're much closer to herd immunity than we thought and the cost of getting there is much lower.

17. 5) Testing effort varies over time and across locations. How does this play into the estimation procedure? 6) To extrapolate in this way implicitly assumes all regions will follow the same trajectory given enough time. How reasonable is that?

16. 3) Why extrapolate to everyone being infected instead of the final epidemic size or even herd immunity threshold? 4) The regression shown accounts for only a modest fraction of the variation. How does the remaining variation impact the predictions?

15. I have SO MANY questions. Just a few of them: 1) What is the causal basis for the relation between R and reported cases? Is this susceptible depletion? Something behavioral? Or is the claim it doesn't matter? 2) Given (1), why can you extrapolate and why linearly?

14. And this turns out to be about 400,000. Yet there are 60M+ in the UK, which gives a scaling of at least 150 cases per reported cases if everyone gets infected.

13. And as always, I welcome corrections from any authors that haven't blocked me already. The idea seems to be to extrapolate to figure out how many cases per capita would be reported by the time you reach R=0.

12. OK, let' try to get back to the paper. I'm really struggling to understand what is going on here. This doesn't look like any infectious disease epidemiological method I've ever seen, and there's no citation given. But I can try to reconstruct the thought process.

11. Oddly enough, I do know a little bit about peer review. In addition to writing a thousand of them or so in my career, I've written a little bit about peer review and what it does and does not guarantee.

10. And I guess now he's falling back on the old "It's been peer reviewed" defense. Well, Adrian, consider this a post-publication peer review.

9. Here was the original post to which I was responding, and which I suppose no longer appears in the first post of my thread.

8. Aside: I haven't even had a chance to explain what is wrong with the study, and I've already been blocked by the author. Friends, this is not an ordinary scientific response to criticism. Especially criticism that hasn't even arrived yet.

7. Now it starts to get really weird.

6. Of the predictors in the regression, only cases/1000 people is predictive, albeit with an r^2 of 0.2. The authors then posit a linear relationship between R and case density: R_ADIR = 1.06 - 0.16 x Current Total Cases/1,000 population. Here's that data.

5. What determines R? In an effort to estimate this, the authors use a regression approach across local regions. There's no underlying mechanistic model of how R changes with time, interventions, etc., nor any temporal analysis. Notice case density is from an April 8th snapshot.

4. But let's get to the science. What did the authors do? They start by estimating a local R value that they call the Average Daily Infection Rate, and estimating its derivative. One could dig into this more deeply, but let's keep going instead.

3. Things get odd right from the very start. The first line of the paper's abstract is not your usual way of beginning a scientific report.

2. The principal claim is that "unreported community infection may be >200 times higher than reported cases", meaning that "29% of the population may already have had the disease." (Most estimates from the US, EU, UK are closer to 10x than 200x) The tabloids are there:

1. An odd research study out of U. Manchester today uses an indirect and, frankly, bizarre method to estimate the incidence in the UK as being vastly higher than that inferred using more direct approaches.

Antibody testing suggests ~15% of NYers (~20% in NYC) have had Covid https://nytimes.com/2020/04/23/nyregion/coronavirus-antibodies-test-ny.html… vs • "I did some back of the envelope extrapolations and found that 83% of NYers have had Covid. Here are my charts from Google Sheets." I know which one I’m going with...

Right on cue, this drops into my inbox

My other tip: follow lots of experts. For me, that means @CT_Bergstrom, @AdamJKucharski, @nataliexdean, @EricTopol, @cmyeaton, @globalhlthtwit, @ActuaryByDay and others. They don’t always agree! And that’s *good*. This stuff is complicated.

We already know from comprehensive research in other countries that the share of people who've had Covid in even hard hit countries, is around 5%. Claims that differ significantly from that require extraordinary evidence

My tip for anyone, fellow journalist or otherwise: weigh any surprising new claims against the balance of evidence already out there on the issue in question.

Yesterday's Manchester paper is a particularly acute case, as the claims in that study concern a critical issue that people will use to justify policy — how many people in the UK may have already had Covid.

It's absolutely vital that as journalists we do all the necessary checks before reporting on highly sensitive issues like this.

Few weeks ago media reported studies saying air pollution levels had big impact on Covid death rates. Problem 1: studies hadn't been peer reviewed. Problem 2: air pollution & pop dens are correlated. Over at SMC, experts pointed out flaws: https://sciencemediacentre.org/expert-reaction-to-preprint-on-air-pollution-in-england-and-covid-19-severity/

And here's a detailed, point-by-point take-down of the same paper by @CT_Bergstrom, including an explanation that even peer review isn't enough to ensure a study's findings are watertight:

Thread: Critical assessment of scientific papers by the media has never been more important than during the pandemic That new Manchester study saying 25% of UK has HD Covid *was* peer reviewed, but has already been comprehensively debunked by many leading epidemiologists.

Dr. Shikha Jain, co-founder of IMPACT, talks about her group's goal of getting trusted information to the public and shaping public policy to aid in awareness of COVID-19.

Chicago doctors come together to dispel misinformation, advise officials on pandemic

Understanding adaptive immunity to SARS-CoV-2 is important for vaccine development, interpreting coronavirus disease 2019 (COVID-19) pathogenesis, and calibration of pandemic control measures. Using HLA class I and II predicted peptide ‘megapools’, circulating SARS-CoV-2−specific CD8+ and CD4+ T cells were identified in ∼70% and 100% of COVID-19 convalescent patients, respectively. CD4+ T cell responses to spike, the main target of most vaccine efforts, were robust and correlated with the magnitude of the anti-SARS-CoV-2 IgG and IgA titers. The M, spike and N proteins each accounted for 11-27% of the total CD4+ response, with additional responses commonly targeting nsp3, nsp4, ORF3a and ORF8, among others. For CD8+ T cells, spike and M were recognized, with at least eight SARS-CoV-2 ORFs targeted. Importantly, we detected SARS-CoV-2−reactive CD4+ T cells in ∼40-60% of unexposed individuals, suggesting cross-reactive T cell recognition between circulating ‘common cold’ coronaviruses and SARS-CoV-2.

Targets of T cell responses to SARS-CoV-2 coronavirus in humans with COVID-19 disease and unexposed individuals

Empathy often feels automatic, but variations in empathic responding suggest that, at least some of the time, empathy is affected by one’s motivation to empathize in any particular circumstance. Here, we show that people can be motivated to engage in (or avoid) empathy-eliciting situations with strangers, and that these decisions are driven by subjective value-based estimations of the costs (e.g., cognitive effort) and benefits (e.g., social reward) inherent to empathizing. Across seven experiments (overall N = 1,348), and replicating previous work (Cameron et al., 2019), we found a robust empathy avoidance effect. We also find support for the hypothesis that individuals can be motivated to opt-in to situations requiring empathy that they would otherwise avoid. Participants were more likely to opt into empathy-eliciting situations if 1) they were incentivized monetarily for doing so (Experiments 1a and 1b), and 2) if a more familiar and liked empathy target was available (Experiments 2a and 2b). Framing empathy as explicitly related to one’s moral character and reputation did not motivate participants to engage in empathy (Experiment 3a and 3c), though these null results may be due to a weak manipulation. These findings suggest that empathy can be motivated in multiple ways, and is a process driven by context-specific value-based decision making.

Motivational effects on empathic choices

How coronavirus research is being weaponized.

A Study Said Covid Wasn’t That Deadly. The Right Seized It.

Objectives: Researchers have hypothesized that using Information and Communications Technology (ICT), such as email and social media, may buffer older adults from normative age-graded declines in psychological adjustment. However, past intervention research has been unable to conclusively evaluate this proposition, and no studies to date have examined this topic using naturalistic large-scale longitudinal methods. Methods: In this pre-registered study, we examined the co-development between three aspects of psychological adjustment (loneliness, satisfaction with life, and depressiveness) and three factor-analytically derived clusters of ICT use (instrumental, social, and media) using a longitudinal representative sample of 2,922 Dutch adults aged 65 and older that contributed data annually from 2012 to 2017. Results: Latent growth curve analyses indicated that ICT use was largely unrelated to psychological adjustment, both cross-sectionally and longitudinally. Of 36 associations tested, three were significant, and only one remained significant after including health and demographic covariates. Specifically, higher levels of media ICT use at baseline predicted steeper declines in satisfaction with life across the study period. Furthermore, results of random-intercept cross-lagged analyses indicated that change in ICT use did not predict future change in psychological adjustment, and vice-versa. Discussion: Results of this study help clarify the mixed results of past intervention research, indicating that effects of ICT use on psychological adjustment tend to be either null or much smaller than can be detected using typical intervention sample sizes. Overall, these results suggest that the association between technology use and psychological adjustment is negligible in older adults.

Log on and prosper: Co-development between technology use and psychological adjustment in older adulthood

Registered Reports Chair Chris Chambers and COS Director of Policy Initiatives David Mellor answer questions about writing, reviewing, editing, and funding studies using the Registered Report format.

Registered Reports Q&A | May 14, 2020

This is a live database of funded research projects across the world related to the current COVID-19 pandemic. By providing an overview of research projects mapped against the priorities identified in the WHO Coordinated Global Research Roadmap: 2019 Novel Coronavirus, we aim to support funders and researchers deliver a more effective and coherent global research response. Last updated: 13 May 2020 It includes: *NEW* Interactive heatmap of these projects against the research priorities set out in the WHO Coordinated Global Research Roadmap: 2019 Novel Coronavirus, March 2020 (see below) Interactive world map to search research projects by research location, funders and by WHO R&D priorities (see below) New research projects funded to date from the dataset sources (in downloadable Excel file) *NEW* The complete clinical trials dataset from the WHO ICTRP, with additional categorisation to allow detailed pivot analysis on DAC list countries (in downloadable Excel file) Supporting information on funding calls (in downloadable Excel file) Links to useful online resources (in downloadable Excel file) *Coming soon* List of COVID-19 Data Repositories (in downloadable Excel file)

COVID-19 Research Project Tracker by UKCDR & GloPID-R

Concurrent advances in information technology infrastructure and mobile computing power in many low and middle-income countries (LMICs) have raised hopes that artificial intelligence (AI) might help to address challenges unique to the field of global health and accelerate achievement of the health-related sustainable development goals. A series of fundamental questions have been raised about AI-driven health interventions, and whether the tools, methods, and protections traditionally used to make ethical and evidence-based decisions about new technologies can be applied to AI. Deployment of AI has already begun for a broad range of health issues common to LMICs, with interventions focused primarily on communicable diseases, including tuberculosis and malaria. Types of AI vary, but most use some form of machine learning or signal processing. Several types of machine learning methods are frequently used together, as is machine learning with other approaches, most often signal processing. AI-driven health interventions fit into four categories relevant to global health researchers: (1) diagnosis, (2) patient morbidity or mortality risk assessment, (3) disease outbreak prediction and surveillance, and (4) health policy and planning. However, much of the AI-driven intervention research in global health does not describe ethical, regulatory, or practical considerations required for widespread use or deployment at scale. Despite the field remaining nascent, AI-driven health interventions could lead to improved health outcomes in LMICs. Although some challenges of developing and deploying these interventions might not be unique to these settings, the global health community will need to work quickly to establish guidelines for development, testing, and use, and develop a user-driven research agenda to facilitate equitable and ethical use.

Artificial intelligence and the future of global health

This article introduces an accessible approach to implementing unmoderated remote research in developmental science—research in which children and families participate in studies remotely and on their own, without directly interacting with researchers. Unmoderated remote research has the potential to strengthen developmental science by: (1) facilitating the implementation of studies that are easily replicable, (2) allowing for new approaches to longitudinal studies and studies of parent-child interaction, and (3) including families from more diverse backgrounds and children growing up in more diverse environments in research. We describe an approach we have used to design and implement unmoderated remote research that is accessible to researchers with limited programming expertise, and describe resources available on a new website to help researchers get started with this approach, http://discoveriesonline.org. We discuss the potential of this method for developmental science and highlight some challenges still to be overcome to harness the power of unmoderated remote research for advancing the field.

Advancing developmental science via unmoderated remote research with children

To make good decisions, we all need good evidence which is clearly communicated. At the Winton Centre we work with institutions and individuals to improve the way that important evidence is presented to all of us.For the latest analysis and charts about deaths during the COVID epidemic, see the following pages:COVID: trends in deaths and excess deaths (Updated 15th May)COVID: daily counts of deaths for different places of death (updated 15th May)COVID: death rates in the population, and comparison with 'normal' risk (updated 15th May) [also appeared as Medium blog What are the risks of COVID? And what is meant by ‘the risks of COVID’?]COVID: Trends in death rates by place of death (updated 15th May)COVID: Analysis of excess deaths (updated 15th May)

Winton Centre for Risk and Evidence Communication

This digital event is focused on conducting research about crisis-related topics and disseminating the findings during the time of a crisis and is set to run from May 17-27 2020.

#researchINcrisis: Research in time of crisis

In this joint commission from The Lancet and Financial Times, we explore how AI and other digital advancements are being harnessed to improve healthcare in the world’s poorest regions

Future of AI and Digital Healthcare

Our weekly round-up of the best psychology coverage from elsewhere on the web

New Norms And Difficult Dogs: The Week’s Best Psychology Links

COVID-19, like many issues, has a politics of knowledge. But in this period of deep uncertainty around health and economic instability, there seems to be an amplification and a multiplication of the ways in which secrecy and ignorance feature. As such, a number of students in the School of Sociology, Politics and International Studies along with members of SPIN have compiled a list of these issues as they have been or are playing out. A summary of some of these issues, along with links to relevant news articles, are included below for further information. 

COVID-19 and the Politics of Knowledge: An Issue and Media Source Primer

Eight ways in which scientists hope to provide immunity to SARS-CoV-2 .

The race for coronavirus vaccines: a graphical guide

As coronavirus continues to spread across the world, billions of people are putting their faith into a vaccine being developed in the hope it will put an end to the pandemic. Medical experts Tammy Hoffman and Paul Glasziou raise their concerns over the lack of contingency planning if a vaccine isn't found, and what alternatives the global community should consider.

Coronavirus: What if a vaccine doesn't work?

We need less extreme and more nuanced recommendations for navigating life during the pandemic

The ‘Just Stay Home’ Message Will Backfire

It will be ‘four or five years’ before Covid-19 is under control, FT’s digital conference told

WHO’s chief scientist offers bleak assessment of challenges ahead

Something beautiful has happened in the last few weeks -- I think we’ve all seen it. In the face of a vicious pandemic, when it would have been so easy for fear and selfishness to rule, we've found our shared humanity again. But there's a danger that as we beat this pandemic, the tenderness of this moment will fade too. We can already see it in the divisions being redrawn for political gain and the conspiracy theories going viral. The spirit of compassion, wisdom, and unity that millions of us have felt amidst this horrific crisis is a fragile thing that needs to be defended. That’s why, with the help of Avaazers across the globe, we've curated ten of the most beautiful stories of this shining new humanity. It's to remind us of who we really are when it matters most, and that we really are capable of meeting the biggest threats we face -- together.

Covid-19:  REASONS TO HOPE

Studies of social networks show that opposition to vaccines is small but far-reaching — and growing.

Anti-vaccine movement could undermine efforts to end coronavirus pandemic, researchers warn

Trust in science and experts is extremely important in times of epidemics to ensure compliance with public health measures. Yet little is known about how this trust evolves while an epidemic is underway. In this paper, we examine the dynamics of trust in science and experts in real-time as the high-impact epidemic of Coronavirus (COVID-19) unfolds in Italy, by drawing on digital trace data from Twitter and survey data collected online via Telegram and Facebook. Both Twitter and Telegram data point to initial increases in reliance on and information-seeking from scientists and health authorities with the diffusion of the disease. Consistent with these increases, using a separately fielded online survey we find that knowledge about health information linked to COVID-19 and support for containment measures was fairly widespread. Trust in science, relative to trust in institutions (e.g. local or national government), emerges as a consistent predictor of both knowledge and containment outcomes. However, over time and as the epidemic peaks, we detect a slowdown and turnaround in reliance and information-seeking from scientists and health authorities, which we interpret as signs of an erosion in trust. This is supported by a novel survey experiment, which finds that those holding incorrect beliefs about COVID-19 give no or lower importance to information about the virus when the source of such information is known to be scientific.

Trust in science and experts during the COVID-19 outbreak in Italy

To see how their models work, we’ll let you tweak your own.

Disease modelers are wary ofreopening the country. Here’show they arrive at their verdict.

When WHO declared the COVID-19 outbreak a Public Health Emergency of International Concern (PHEIC) on Jan 30, 2020, under the provisions of the International Health Regulations (2005) (IHR), it recommended against “any travel or trade restriction”.1WHOStatement on the second meeting of the International Health Regulations (2005) Emergency Committee regarding the outbreak of novel coronavirus (2019-nCoV).https://www.who.int/news-room/detail/30-01-2020-statement-on-the-second-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-outbreak-of-novel-coronavirus-(2019-ncov)Date: Jan 30, 2020Date accessed: May 7, 2020Google Scholar The recommendation was based on data available at the time, evidence from previous outbreaks, and principles underpinning the IHR. It formed an important part of WHO's messaging about how states could effectively respond in a coordinated way. Instead, over the following months, according to WHO, 194 countries adopted some form of cross-border measure—eg, travel restrictions, visa restrictions, border closures, among others—with little reproach from WHO or other actors in the international community.2WHOWeekly update on COVID-19, April 8–15, 2020. Health Emergencies Programme. World Health Organization, Geneva2020Google Scholar This response is a sharp increase from at most 25% of member states that imposed trade and travel restrictions during the 2009 H1N1 influenza pandemic and the 2013–16 outbreak of Ebola virus disease in west Africa.3Worsnop CZ Domestic politics and the WHO's International Health Regulations: explaining the use of trade and travel barriers during disease outbreaks.Rev Int Organ. 2017; 12: 365-395Crossref Scopus (2) Google Scholar Indeed, WHO's recommendation against measures such as travel restrictions and border closures became a point of criticism of the organisation's role at the early stages of the COVID-19 pandemic.4Watts A Stracqualursi V WHO defends coronavirus response after Trump criticism. CNN, April 8, 2020https://www.cnn.com/2020/04/08/politics/who-responds-trump-claims-coronavirus/index.htmlDate accessed: May 7, 2020Google ScholarThe universal adoption of cross-border measures raises fundamental questions about what coordination means during a pandemic, and what role WHO has in facilitating this. Coordinated action among states in an interconnected world underpins effective prevention, detection, and control of disease outbreaks across countries.5National Academy of Medicine Commission on a Global Health Risk Framework for the FutureThe neglected dimension of global security: a framework to counter infectious disease crises. National Academies Press, Washington, DC2016Crossref Google Scholar As parties to the IHR, governments agree that coordination is important to ensure that measures do not unnecessarily disrupt international trade and travel. Thus, during major disease outbreaks, part of WHO's role is to provide evidence-informed guidance on cross-border measures.

Global coordination on cross-border travel and trade measures crucial to COVID-19 response

The emergence of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) has offered the world a crash course in modern epidemiology, starting with lessons in case detection and exponential growth. It has also reminded scientists of the challenges of communicating effectively during uncertainty. The current pandemic has no parallel in modern history, but the new virus is following rules common to other pathogens. Principles derived from influenza virus infections and other infectious diseases offer confidence for two predictions: SARS-CoV-2 is probably here to stay, and the high transmission rate will continue to force a choice between widespread infection and social disruption, at least until a vaccine is available. The difficulty of this choice is amplified by uncertainty, common to other respiratory pathogens, about the factors driving transmission. This pandemic presents a broader opportunity to interrogate how to manage pathogens.

Modeling infectious disease dynamics

The recent outbreak of coronavirus disease 2019 (COVID-19) in mainland China was characterized by a distinctive subexponential increase of confirmed cases during the early phase of the epidemic, contrasting with an initial exponential growth expected for an unconstrained outbreak. We show that this effect can be explained as a direct consequence of containment policies that effectively deplete the susceptible population. To this end, we introduce a parsimonious model that captures both quarantine of symptomatic infected individuals, as well as population-wide isolation practices in response to containment policies or behavioral changes, and show that the model captures the observed growth behavior accurately. The insights provided here may aid the careful implementation of containment strategies for ongoing secondary outbreaks of COVID-19 or similar future outbreaks of other emergent infectious diseases.

Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China

Dr Robert Spencer, a trustee of Dr Edward Jenner's House, Garden and Museum in Berkeley, Gloucestershire, UK, wrote to me last week on the 40th anniversary of the eradication of smallpox (May 8). He was polite but disappointed: “On this day in 1980 the WHO announced the eradication of smallpox from the world. This infection, which probably caused more deaths than any other disease, was finally condemned to the history books. Sorry to see you had no space in this week's edition of The Lancet to commemorate this milestone, especially at a time of COVID-19 pandemic.” Dr Spencer was right to admonish me. To be perfectly honest, this important anniversary had completely passed me by. For weeks, months, now I have been utterly preoccupied by the pandemic we are currently living through—its unfolding around the world, the human catastrophes the virus has wrought, the often criminally negligent responses by many national governments, and the impact lockdown is having on the wellbeing of my own colleagues across the Lancet journals. Smallpox never entered my thoughts. My omission is a sharp reminder not only to me but also to the global health community, and not only about smallpox. What has happened to the litany of issues, campaigns, and debates we were engaged in before this coronavirus struck?

Offline: Don't let COVID-19 divert us completely

This is a brief results report from a manuscript in development, which reports on a study using Right-Wing Authoritarianism and Social Dominance Orientation subfactors to predict reactions to COVID-19 restrictions in Australia.

Using RWASDO Subfactors to Predict Reactions to COVID-19 Restrictions in Australia

In late March, we posed a hypothetical scenario to a sample of Australians, asking if they would download a contact tracing app released by the federal government; 70% responded in favour. But a more recent survey, following the release of COVIDSafe, revealed only 44% of respondents had downloaded it. The Australian government’s COVIDSafe app aims to help reduce the spread of COVID-19 and let us all return to normal life. But this promise depends on how many Australians download and use the app. The minimum required uptake has been variously estimated at 40-60% of the population. Our ongoing research, led by the Complex Human Data Hub of the University of Melbourne’s School of Psychological Sciences, surveyed the Australian public to understand their opinions and use of the COVIDSafe app, and other possible government tracking technologies. Our research is helping us understand the conditions under which Australians will accept these technologies, and what’s holding them back.

70% of people surveyed said they’d download a coronavirus app. Only 44% did. Why the gap?

One of the most concerning notions for science communicators, fact-checkers, and advocates of truth, is the backfire effect. This is when a correction leads to an individual increasing their belief in the very misconception the correction is aiming to rectify. There is currently a debate in the literature as to whether backfire effects exist at all, as recent studies have failed to find the phenomenon, even under theoretically favorable conditions. In this review, we briefly summarize the current state of the worldview and familiarity backfire effect literatures. We subsequently examine barriers to measuring the backfire phenomenon, approaches to improving measurement, and we conclude with recommendations for fact-checkers. We suggest that backfire effects are not a robust empirical phenomenon, and more reliable measures, powerful designs, and stronger links between experimental design and theory, could greatly help move the field ahead.

Searching for the backfire effect: Measurement and design considerations