6-day forecasts of COVID-19 case counts by country based on a novel epidemiological model that integrates the effect of population behavior changes due to government measures and social distancing.The SIR-X model is described in detail here: Effective containment explains sub-exponential growth in confirmed cases of recent COVID-19 outbreak in Mainland China, B. F. Maier & D. Brockmann, medRxiv, https://doi.org/10.1101/2020.02.18.20024414, (2020)The containment measures implemented in response to the growing pandemic vary drastically by country. Classical epidemiological models fail to capture the impact of such efforts on the spread of the outbreak. Under unconstrained conditions, we would see exponential growth in the number of confirmed cases. However, several graphs below indicate that this is not the case. These insights can be used to evaluate the effectiveness of containment strategies in order to inform further courses of action and future policies.Click a country below to view the forecasts for that country. Move the pointer to display the number of confirmed cases by date.The open dots indicate the total number of confirmed cases over time. The blue bars represent the new confirmed cases per day. The solid line depict the model's fit and subsequent predictions of case count numbers for the next 6 days as well as the expected new cases per day. The grey and red shaded regions represent the 98% and 68% confidence intervals, respectively.

LitCovid: Curated literature hub for tracking up-to-date scientific information about the 2019 novel Coronavirus .t3_fp7epr ._2FCtq-QzlfuN-SwVMUZMM3 { --postTitle-VisitedLinkColor: #9b9b9b; --postTitleLink-VisitedLinkColor: #9b9b9b; } methods and toolshttps://www.ncbi.nlm.nih.gov/research/coronavirus/LitCovid is a curated literature hub for tracking up-to-date scientific information about the 2019 novel Coronavirus. It is the most comprehensive resource on the subject, providing a central access to 1724 (and growing) relevant articles in PubMed. The articles are updated daily and are further categorized by different research topics and geographic locations for improved access. You can read more at Chen et al. Nature (2020) and download our data here.

the incentives are a gallery of pure glory

Moar errors Not just sampling error, but errors of all shapes and colours are likely to increase — affecting study designs and materials, data processing and analyses, and the reporting of results.

Sign errors The true size of that small, marginally significant positive effect you observed, that could save a few lives — it could be negative. It could cost a few lives.

Crisis research, fast and slow

Opportunity cost

In Europe and North America (where most of the English-language literature in psychology is produced), the COVID-19 pandemic started having substantial effects on everyday life — mostly in the form of social distancing — no more than two weeks ago, but psych researchers haven’t been messing about. In this short time, the Psychological Science Accelerator put out a call for “rapid and impactful study proposals on COVID-19”, received 66(!!)[1]Legend: ! = hovering on the brink of significance; !! = decisively significant; !!! = globally significant jQuery("#footnote_plugin_tooltip_1").tooltip({ tip: "#footnote_plugin_tooltip_text_1", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] }); proposals in four(!!!) days, sifted through them, decided to run three of them and started preparing the data collection. Chris Chambers called researchers to sign up as reviewers for rapid-review Registered Reports on COVID-19 at Royal Society Open Science, got over 530(!!) responses within 48(!) hours, and moved the first RR to in-principle acceptance in just 6(!!) days which saw 2(!!!) rounds of review.

avalanche of “crisis papers” come rolling in — from how to combat COVID19-related misinformation to the right type of moralistic finger-wagging to get people to stay the fuck home, to personality traits that predict who’s stealing all your toilet paper.[3]Hint: it’s people who like stealing things. jQuery("#footnote_plugin_tooltip_3").tooltip({ tip: "#footnote_plugin_tooltip_text_3", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] }); Earlier today (26th of March), I counted 20(!) English-language, psychology-related crisis preprints (15 with new data, 4 reviews, and one opinion piece),[4]Method: Searched OSF Preprints on the 26th of March 2020 at ~12:30 CET for “COVID OR coronavirus OR corona” and included all English-language papers that were published on PsyArXiv and all non-PsyArXiV papers that were tagged with “Social and Behavioral Sciences” AND either “Psychology” or a psychology subfield. jQuery("#footnote_plugin_tooltip_4").tooltip({ tip: "#footnote_plugin_tooltip_text_4", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] }); 16 of which have been published in the last two weeks(!!).

time pressure like you’ve never seen before — authors who want to produce research with a real impact on the crisis have to publish now. Literally every day counts to flatten the curve or buffer side effects of social distancing policies.

worried. If the reform movement of the last years has brought us to any kind of consensus, it’s probably that a mix of problematic pressures and incentives should be handled with safety glasses because it has a tendency to explode in your face

a hat tip goes to Flávio Azevedo and his compilation of COVID-19 social-science research as well as the COVID-19 social science project tracker.

A non-expert audience Crisis papers will be read by many more people outside of the field — that’s kind of the point. But it means that a sizable chunk of the readership (and perhaps the most impactful chunk) is poorly prepared to understand methodological details and limitations.

The problem with this argument is that any effect that might save a few lives might just as well cost a few lives. And if we operate in emergency mode, lowering our guard and accepting higher error rates for studies that, if anything, have a higher risk of bias and error than non-crisis research, we exacerbate the risk that what we thought were marginal gains will explode in our faces.

it feels as if we’ve put our guard down rather than up: We understand and accept that a study put together and written up in a few days can’t be as polished and rigorous as one carried out over weeks and months.

one of the best uses of our time right now might be to check, criticise, and help improve the work of others — Farid Anvari’s commentary on Everett et al. being a stellar example.

I started a Zotero library with COVID-19-related psychology preprints and a spreadsheet to track these papers’ publication and modification dates, downloads, whether they were preregistered and when, and if they have open data.

Fast measures ≠ valid measures Measures of behavioural intentions are having a field day. That’s not surprising given that fast research mostly means online research, and measuring actual behaviour is expensive and slow. But shaky proxies don’t magically become more valid because we’re in a pandemic. If you don’t know how your outcome measure relates to the thing you’re actually interested in, then you don’t know what your manipulation will do in the real world.

if there were no crisis papers appearing at all, maybe we would instead go back to the literature and dig out relevant studies that were produced more slowly and cautiously? Max Maier pointed out that a number of papers written in response to the swine flu may be relevant now but largely seem to be getting ignored.

LitCovid is a curated literature hub for tracking up-to-date scientific information about the 2019 novel Coronavirus. It is the most comprehensive resource on the subject, providing a central access to 1737 (and growing) relevant articles in PubMed. The articles are updated daily and are further categorized by different research topics and geographic locations for improved access. You can read more at Chen et al. Nature (2020) and download our data here.

Infotagion seeks to fight the disinformation contagion about COVID-19. Disinformation about this deadly virus can spread just as far and fast as the real thing – harming you and those you love. So we’re fighting back, giving everyone the opportunity to flag false and misleading content, while highlighting trustworthy and sourced information.

is evidence-based;can be rapidly applied;can be regularly applied;is simple and flexible to adjust to the changing situation; andis low cost and cost effective.

trust in health authorities, recommendations and information;risk perceptions;acceptance of recommended behaviours;knowledge;barriers/drivers to recommended behaviours;misperceptions; andstigma.

Don’t trust the psychologists on coronavirus I should know, as I'm one of them. Many of the responses to covid-19 come from a deeply-flawed discipline filled with dubious studies BY Stuart Ritchie

COVID-19 Resource Hub

“Guarding against adversarial disruption /avoiding politicization”

“Funders”

“Proper science without the drag”

Hahn, Lagnado, Lewandowsky, and Chater (pictured below) recently wrote a paper in which they discuss the need to change our community so we can move quickly in response to the COVID-19 crisis. As they noted, we have important information, based on empirical evidence, that can help address COVID-19 related issues, including, using technology to work and learn effectively, reducing the spread of misinformation, the list goes on and on.

 Hahn, U., Lagnado, D., Lewandowsky, S., & Chater, N. (2020). Crisis knowledge management: Reconfiguring the behavioural science community for rapid responding in the Covid-19 crisis. https://psyarxiv.com/hsxdk

“Supporting policy makers: what the wider community can and cannot do”

Creating a forum for Crisis Knowledge Management

COVID-19: What can we do now?

“Breaking down silos”

“Managing expertise”

SPI-B insightson combined behavioural and social interventions

Abstract The present crisis demands an all-out response if it is to be mastered with minimal damage. This means we, as the behavioural science community, need to think about how we can adapt to best support evidence-based policy in a rapidly changing, high-stakes environment. This piece is an attempt to initiate this process. The ‘recommendations’ made are first stabs that will hopefully be critiqued, debated and improved.

Misinformation can amplify humanity's greatest challenges. A salient recent example of this is the COVID-19 pandemic, which has bred a multitude of falsehoods even as truth has increasingly become a matter of life-and-death. Here we investigate why people believe and spread false (and true) news content about COVID-19, and test an intervention intended to increase the truthfulness of the content people share on social media. Across two studies with over 1,600 participants (quota-matched to the American public on age, gender, ethnicity and geographic region), we find support for the idea that people share false claims about COVID-19 in part because they simply fail to think sufficiently about whether or not content is accurate when deciding what to share. In Study 1, participants were far worse at discerning between true and false content when deciding what they would share on social media relative to when they are asked directly about accuracy. Furthermore, participants who engaged in more analytic thinking and had greater science knowledge were more discerning in their belief and sharing. In Study 2, we found that a simple accuracy reminder at the beginning of the study – i.e., asking people to judge the accuracy of a non-COVID-19-related headline – more than doubled the level of truth discernment in participants’ sharing intentions. In the control, participants were equally like to say they would share false versus true headlines at COVID-19 whereas, in the treatment, sharing of true headlines was significantly higher than false headlines. Our results – which mirror those found previously for political fake news – suggest that nudging people to think about accuracy is a simple way to improve choices about what to share on social media. Accuracy nudges are straightforward for social media platforms to implement on top of the other approaches they are currently employing, and could have an immediate positive impact on stemming the tide of misinformation about the COVID-19 outbreak.

10.31234/osf.io/uhbk9

Fighting COVID-19 misinformation on social media: Experimental evidence for a scalable accuracy nudge intervention

Right now the comments on this very thread are full of this sort of thing from (non-trollbot) accounts. This one asserted that medical experts are lying to the public "explicitly for short term political gain at the expense of human lives." and then doubled down when questioned.

I'm astonished that people think doctors, biomedical researchers, and public health professionals—who have devoted their lives to helping people—are lying about #hydrochloroquine treatment for #COVID19 just to hurt Trump, rather than expressing justified skepticism based on data.

Until the past month, medical professionals have been among the most trusted people in America (below). We're seeing a dramatic turn in this, right at the time they are literally risking their lives to help the rest of us.

There is an obvious concern globally regarding the fact about the emerging coronavirus 2019 novel coronavirus (2019‐nCoV) as a worldwide public health threat. As the outbreak of COVID‐19 causes by the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) progresses within China and beyond, rapidly available epidemiological data are needed to guide strategies for situational awareness and intervention. The recent outbreak of pneumonia in Wuhan, China, caused by the SARS‐CoV‐2 emphasizes the importance of analyzing the epidemiological data of this novel virus and predicting their risks of infecting people all around the globe. In this study, we present an effort to compile and analyze epidemiological outbreak information on COVID‐19 based on the several open datasets on 2019‐nCoV provided by the Johns Hopkins University, World Health Organization, Chinese Center for Disease Control and Prevention, National Health Commission, and DXY. An exploratory data analysis with visualizations has been made to understand the number of different cases reported (confirmed, death, and recovered) in different provinces of China and outside of China. Overall, at the outset of an outbreak like this, it is highly important to readily provide information to begin the evaluation necessary to understand the risks and begin containment activities.

10.1002/jmv.25743

Analyzing the epidemiological outbreak of COVID‐19: A visual exploratory data analysis approach

10.1037/xap0000177

A bias for action in cancer screening?

How the world's collective attention is being paid to a pandemic: COVID-19 related 1-gram time series for 24 languages on Twitter

A bias for action in cancer screening?

Lessons from the Spanish flu: social distancing can be good for the economy