10.31234/osf.io/nfm6k

The repetition-induced truth effect refers to a phenomenon where people rate repeated statements as more likely true than novel statements. In this paper we document qualitative individual differences in the effect. While the overwhelming majority of participants display the usual positive truth effect, a minority are the opposite – they reliably discount the validity of repeated statements, what we refer to as negative truth effect. We examine 8 truth-effect data sets where individual-level data are curated. These sets are composed of 1,105 individuals performing 38,904 judgments. Through Bayes factor model comparison, we show that reliable negative truth effects occur in 5 of the 8 data sets. The negative truth effect is informative because it seems unreasonable that the mechanisms mediating the positive truth effect are the same that lead to a discounting of repeated statements' validity. Moreover, the presence of qualitative differences motivates a different type of analysis of individual differences based on ordinal (i.e., Which sign does the effect have?) rather than metric measures. To our knowledge, this paper reports the first such reliable qualitative differences in a cognitive task.

The truth revisited: Bayesian analysis of individual differences in the truth effect

10.31234/osf.io/6mjbz

Across four studies, we examined the effectiveness of misinformation debunks created by CrossCheck France during the 2017 French election. We measured both memory for the article and belief in the debunked rumor. In both US and French samples, reading the debunk decreased belief in the false information, even one week later. However, the debunks were much more effective in the US sample, who lacked relevant prior knowledge and political beliefs. Participants failed to remember many of the details from the article, but retrieval practice was beneficial in reducing forgetting over a one-week delay. We saw no difference in debunk efficacy based on the type of headline (question vs negation) or the number of newsroom logos present around the article (one, four, or seven). In addition, informative design features such as an icon identifying the type of misinformation debunked were ignored by readers. Overall, misinformation debunks can be effective at reducing belief in false information, but readers tend to forget the details and ignore peripheral information.

Debunking rumors around the French election: The memorability and effectiveness of misinformation debunks

After neutralizing SARS-CoV-2 in cell culture, scientists at the University of Costa Rica are proceeding with human testing

Costa Rica Readies Horse Antibodies for Trials as an Inexpensive COVID-19 Therapy

10.1038/s41562-020-00943-3

It has long been known that advocating for a cause can alter the advocate’s beliefs. Yet a guiding assumption of many advocates is that the biasing effect of advocacy is controllable. Lawyers, for instance, are taught that they can retain unbiased beliefs while advocating for their clients and that they must do so to secure just outcomes. Across ten experiments (six preregistered; N = 3,104) we show that the biasing effect of advocacy is not controllable but automatic. Merely incentivizing people to advocate altered a range of beliefs about character, guilt and punishment. This bias appeared even in beliefs that are highly stable, when people were financially incentivized to form true beliefs and among professional lawyers, who are trained to prevent advocacy from biasing their judgements.

The automatic influence of advocacy on lawyers and novices

10.1038/s41562-020-00949-x

Twitter announced on 18 June 2019 that it would remove the precise geotagging feature in tweets. In addition to protecting the location privacy of users, this change also affects human behaviour studies based on geotagged tweets. We discuss the potential impact of Twitter’s decision and how researchers can respond to this change.

Understanding the removal of precise geotagging in tweets

A powerful image. These girls are sitting their university entrance exams in Afghanistan ...under a heat of 37C...on the ground...social distant...during a pandemic...because they know that only education will make them free

In response to the pandemic of COVID-19 and in lack of pharmaceutical solutions, many countries have introduced social and physical distancing regulations to contain the transmission of the virus. These measures are effective insofar as they are able to quickly change people’s habits. This is achieved by changing the monetary incentives of rule violators but also by shifting people’s perception regarding the appropriateness of socialization. We studied the effect of introducing, and then lifting, distancing regulations on the perceived norm regarding social encounters. We conducted an online incentivized experiment in France where we elicited the same participants’ perceived norm and social distancing behavior every week for three months. We found that people shifted behavior and norm perception as soon as the government introduced or removed distancing measures. This effect was fast acting and long lasting. This is informative for future interventions, especially in light of a possible COVID-19 recurrence.

Perceived Social Norm and Behavior Quickly Adjusted to Legal Changes During the COVID-19 Pandemic

Objectives: To investigate factors associated with adherence to self-isolation and lockdown measures due to COVID-19 in the UK. Design: Online cross-sectional survey. Setting: Data were collected between 6th and 7th May 2020. Participants: 2240 participants living in the UK aged 18 years or over. Participants were recruited from YouGov's online research panel. Main outcome measures: Having gone out in the last 24 hours in those who reported symptoms of COVID-19 in their household. Having gone out shopping for items other than groceries, toiletries or medicines (non-essentials), and total number of outings, in the last week in those who reported no symptoms of COVID-19 in their household. Results: 217 people (9.7%) reported that they or someone in their household had symptoms of COVID-19 (cough or high temperature / fever) in the last seven days. Of these people, 75.1% had left the home in the last 24 hours (defined as non-adherent). Factors associated with non-adherence were being male, less worried about COVID-19, and perceiving a smaller risk of catching COVID-19. Adherence was associated with having received help from someone outside your household. Results should be taken with caution as there was no evidence for associations when controlling for multiple analyses. Of people reporting no symptoms in the household, 24.5% had gone out shopping for non-essentials in the last week (defined as non-adherent). Factors associated with non-adherence and with a higher total number of outings in the last week included decreased perceived effectiveness of Government "lockdown" measures, decreased perceived severity of COVID-19, and decreased estimates of how many other people were following lockdown rules. Having received help was associated with better adherence. Conclusions: Adherence to self-isolation is poor. As we move into a new phase of contact tracing and self-isolation, it is essential that adherence is improved. Communications should aim to increase knowledge about actions to take when symptomatic or if you have been in contact with a possible COVID-19 case. They should also emphasise the risk of catching and spreading COVID-19 when out and about and the effectiveness of preventative measures. Using volunteer networks effectively to support people in isolation may promote adherence.

10.1101/2020.06.01.20119040

Factors associated with adherence to self-isolation and lockdown measures in the UK; a cross-sectional survey

18. Disclosure: There are IHME doubters and disciples. I’m neither. I am a long-time ally of the GBD, which is vital work that's improved by transparency and honest criticism. In my view the IHME COVID-19 models don't yet meet that standard, but I'd like to see them to get there.

17. Bottom line: This model is getting lots of attention, from the public and (presumably) policy-makers. Trust in science and public health has been badly damaged during this pandemic. Transparency and good communication are essential to any hope for a science-driven response.

16. Provide more scenario analyses that allow some unpacking of the forecasts. What if the expected effects of seasonality are less pronounced than in the base model? How about running the best-fitting predictive model that excludes seasonal effects, for comparison?

15. Make it easier to find these methods. They should be one click away from the forecasts here: https://covid19.healthdata.org/united-states-of-america… Publish the forward projections of all predictors. The forecasted trends are driven by these. People need to see them.

14. So, hoping @IHME can confirm whether I've got all this right. I’ll leave it to others to weigh in on the strength of evidence for seasonal effects on SARS-CoV-2 transmission. Meantime, I have some suggestions for things that @IHME could do to elicit constructive input.

13. Which brings us to seasonality. Seasonality is captured using weekly, state-specific vital statistics data on pneumonia mortality from 2013 to 2019. So, as far as I can surmise, the estimated rise from 900 to 2900 daily deaths derives entirely from this seasonal effect.

12. It’s not behavior. The main projection expects distancing behavior to improve in response to a worsening epidemic. It’s the purple dotted line diving down in the figure, meaning reduced contacts. It’s not mask use, which is held constant ... …nor testing, which goes up.

11. So, this is the key: projections for the next four months depend on projections of the independent variables. In the main projection, daily deaths rise from <900 now to almost 2900 by December 1. What in the model drives things to get much worse?

10. Step 5: Make forward projections of the predictors, which leads to forward projections of beta. These are then plugged into the SEIR model to produce forecasts of all the other outcomes including cases and deaths.

9. Step 4: It’s all about that beta. Here’s the heart of the projection model: a linear regression of beta on a bunch of covariates, including several time varying ones: - social distancing mandates - changes in mobility - testing per capita - mask use - pneumonia seasonality

8. Step 3: More back-calculation! Here, a pretty standard deterministic SEIR model is fit to the estimated series on new infections. Key output in this step is estimated time series on beta, the transmission rate. [Aside: yep, this is a LOT of estimates on estimates ...]

7. Step 2: Back-calculate a time series on new infections from the smoothed death time series, based on … - assumed age pattern of mortality - assumed lag from infection to death - assumed age-specific infection fatality rates

6. Step 1: Estimate smoothed mortality time series using splines. First, cases and hospitalizations used as leading indicators to predict deaths. Then, a 2nd model synthesizes deaths from direct observation with the death series predicted from cases & hospitalizations.

5. The current approach is described as a hybrid ‘mortality spline + SEIR’ model. Let’s have a look at the components. Here’s the schematic from the July preprint. I believe it’s pretty self-explanatory. [Narrator: It isn’t.]

4. So, what’s in the model? There have been a few major renovations. The last one, I believe, was rolled out in July. All remnants of the much-maligned mortality CurveFit were excised in the July release. This CurveFit-ectomy was a welcome advance.

3. After some digging, I think the current IHME forecast model is described here: https://medrxiv.org/content/10.1101/2020.07.12.20151191v1… @IHME: if this is the current model, every estimates update and the FAQ need to point here, and not just to the March and April preprints.

2. First observation: the methods really need to be easier to find and vet. Optimally, publish all code. At least, have every update point clearly to the technical document with full model details. Right now, the FAQ and results updates point to old model versions.

1. Did the latest @IHME mortality forecasts making the media rounds - 410,000 deaths - seem high to you? Yeah, me too. I wanted to understand what’s driving projections of 220K more deaths by New Years. So, I tried to peek under the hood, as best I could. Buckle in. A thread.

The majority (68%) in ICU care had one or more underlying condition considered as one of the risk groups, most prevalent being hypertension (37%), diabetes (25%), chronic pulmonary heart disease (24%), chronic respiratory disease (14%) and chronic cardiovascular disease (11%).

Interesting numbers when I used the death certificate audit data from two Swedish regions where C-19 was established as a direct cause of death and excluded C-19 as a minor contributing factor only.

COVID-19 Projections

Covid Projections Tracker is a tool that allows experts to easily track projection accuracy as well as changes in projections over time. Projectons may change as new data is incorporated or when model parameters or frameworks are updated.

COVID Projections Tracker

The United States (US) has not been spared in the ongoing pandemic of novel coronavirus disease. COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), continues to cause death and disease in all 50 states, as well as significant economic damage wrought by the non-pharmaceutical interventions (NPI) adopted in attempts to control transmission. We use a deterministic, Susceptible, Exposed, Infectious, Recovered (SEIR) compartmental framework to model possible trajectories of SARS-CoV-2 infections and the impact of NPI at the state level. Model performance was tested against reported deaths from 01 February to 04 July 2020. Using this SEIR model and projections of critical driving covariates (pneumonia seasonality, mobility, testing rates, and mask use per capita), we assessed some possible futures of the COVID-19 pandemic from 05 July through 31 December 2020. We explored future scenarios that included feasible assumptions about NPIs including social distancing mandates (SDMs) and levels of mask use. The range of infection, death, and hospital demand outcomes revealed by these scenarios show that action taken during the summer of 2020 will have profound public health impacts through to the year end. Encouragingly, we find that an emphasis on universal mask use may be sufficient to ameliorate the worst effects of epidemic resurgences in many states. Masks may save as many as 102,795 (55,898-183,374) lives, when compared to a plausible reference scenario in December. In addition, widespread mask use may markedly reduce the need for more socially and economically deleterious SDMs.

10.1101/2020.07.12.20151191

COVID-19 scenarios for the United States

The Centers for Disease Control and Prevention hasn’t drastically reduced the number of deaths attributable to COVID-19, but posts making that bogus claim have been circulating widely — with the help of President Donald Trump, who retweeted one such claim on Aug. 30.

CDC Did Not ‘Admit Only 6%’ of Recorded Deaths from COVID-19

#India has passed #Brazil to move into the # 2 spot in the world.

Oh, so THAT'S how you describe this feeling.

Merchants of Doubt: How a Handful of Scientists Obscured the Truth on Issues from Tobacco Smoke to Global Warming is a 2010 non-fiction book by American historians of science Naomi Oreskes and Erik M. Conway. It identifies parallels between the global warming controversy and earlier controversies over tobacco smoking, acid rain, DDT, and the hole in the ozone layer. Oreskes and Conway write that in each case "keeping the controversy alive" by spreading doubt and confusion after a scientific consensus had been reached was the basic strategy of those opposing action.[1] In particular, they show that Fred Seitz, Fred Singer, and a few other contrarian scientists joined forces with conservative think tanks and private corporations to challenge the scientific consensus on many contemporary issues.[2]

Merchants of Doubt

The SARS-CoV-2 coronavirus has proven difficult to control not onlybecause of its high transmissibility, but because those who are infectedreadily spread the virus before symptoms appear, and because some in-fected individuals, though contagious, never exhibit symptoms. Proactivetesting of asymptomatic individuals is therefore a powerful, and proba-bly necessary, tool for preventing widespread infection in many settings.This paper explores the effectiveness of alternative testing regimes, inwhich the frequency, the accuracy, and the delay between testing and re-sults determine the time path of infection. For a simple model of diseasetransmission, we present analytic formulas that determine the effect oftesting on the expected number of days during which an infectious indi-vidual is exposed to the population at large. This allows us to estimatethe frequency of testing that would be required to prevent uncontrolledoutbreaks, and to explore the trade-offs between frequency, accuracy, anddelay in achieving this objective. We conclude by discussing applicationsto outbreak control on college and university campuses.

Frequency and accuracy of proactive testing forCOVID-19

To mitigate SARS-CoV-2 transmission risks from international travellers, many countries currently use a combination of up to 14 days of self-quarantine on arrival and testing for active infection. We used a simulation model of air travellers arriving to the UK from the EU or the USA and the timing of their stages of infection to evaluate the ability of these strategies to reduce the risk of seeding community transmission. We find that a quarantine period of 8 days on arrival with a PCR test on day 7 (with a 1-day delay for test results) can reduce the number of infectious arrivals released into the community by a median 94% compared to a no quarantine, no test scenario. This reduction is similar to that achieved by a 14-day quarantine period (median 99% reduction). Shorter quarantine periods still can prevent a substantial amount of transmission; all strategies in which travellers spend at least 5 days (the mean incubation period) in quarantine and have at least one negative test before release are highly effective (e.g. a test on day 5 with release on day 6 results in a median 88% reduction in transmission potential). Without intervention, the current high prevalence in the US (40 per 10,000) results in a higher expected number of infectious arrivals per week (up to 23) compared to the EU (up to 12), despite an estimated 8 times lower volume of travel in July 2020. Requiring a 14-day quarantine period likely results in less than 1 infectious traveller each entering the UK per week from the EU and the USA (97.5th percentile). We also find that on arrival the transmission risk is highest from pre-symptomatic travellers; quarantine policies will shift this risk increasingly towards asymptomatic infections if eventually-symptomatic individuals self-isolate after the onset of symptoms. As passenger numbers recover, strategies to reduce the risk of re-introduction should be evaluated in the context of domestic SARS-CoV-2 incidence, preparedness to manage new outbreaks, and the economic and psychological impacts of quarantine.

10.1101/2020.07.24.20161281

Strategies to reduce the risk of SARS-CoV-2 re-introduction from international travellers

Airport coronavirus testing options should be explored as a way of cutting the number of travellers who must spend two weeks in quarantine, says Labour.

Coronavirus: Labour demands airport testing review to cut quarantine

Despite the COVID-19 pandemic, many U.S. workers will eventually return to their offices.But when they do, their big-city workplace will not only have a smaller footprint and operational strategy, it might be in a different town altogether, according to a recent TechCrunch survey of top real estate and proptech investors.

9 top real estate and proptech investors: Cities and offices still have a future

The coronavirus outbreak has pushed millions of Americans, especially young adults, to move in with family members. The share of 18- to 29-year-olds living with their parents has become a majority since U.S. coronavirus cases began spreading early this year, surpassing the previous peak during the Great Depression era.

A majority of young adults in the U.S. live with their parents for the first time since the Great Depression

A lot has gone wrong in the response to COVID. I would have done things differently in economic policy too. But overall the massive response from the Federal Reserve and even more importantly from Congress has been working and should be continued as long as needed.

Action should be based on circumstances. The $600 a week boost to weekly unemployment checks may have made sense when the economy has shutdown but with an UR of 8.4% it should change. The President's $400 is reasonable--but the Senate needs to actually pass it for it to be real.

Further action is needed. We're still in a bad recession. State/local job creation has been weak/negative in recent months & will get worse without aid. And households will run through their cushions soon, consumption growth will start to slow, and that will take a toll on jobs.

The fiscal response has now ended. The "cliff" was at the end of July but given that most households saved a lot in the spring & had healthier balance sheets than pre-crisis they have some ability to smooth. So I would not expect bad macro impacts until Sep or Oct.

The shock to the economy from COVID has in many ways been much larger than the shock that precipitated previous recessions. At the same time, the policy response has also been MUCH larger this time than previous times with a discretionary fiscal stimulus 5X the previous record.

In the double dip recession in the early 1980s the unemployment rate was 8.5% or above for 24 straight months. In the financial crisis it was 34 straight months. This time it will have been 4 months (although it could tick up again).

An unemployment rate of 8.4% is much lower than most anyone would have thought it a few months ago. It is still a bad recession but not a historically unprecedented event or one we need to go back to the Great Depression for comparison.

Employment in industries where people can work from home is still down 3.9%, with little recovery. That's equal to peak-to-trough drop in jobs in those industries during Great Recession. Big red flag for longer-term recovery.