a rather concise argument...

Should you get the COVID19 vaccine? Doctor @AnnaBlakney explains why it is so important. #teamhalo

Check out "Campaign for Social Science Annual SAGE Lecture 2020" https://eventbrite.co.uk/e/campaign-for-social-science-annual-sage-lecture-2020-tickets-129009513929?utm-medium=discovery&utm-campaign=social&utm-content=attendeeshare&aff=estw&utm-source=tw&utm-term=listing… @EventbriteUK

Science of Behavioral Change Capstone Conference: Celebrating Accomplishments and Looking to the Future Register now for this Feb. 22-23 NIH virtual event https://commonfund.nih.gov/sobc-capstonemeeting/registration

Thank you to all who attended the SOBC Capstone Conference on February 22-23, 2021. A recording of the meeting and a summary report will be available on this page in the coming weeks. Check back soon!

SOBC Capstone Conference - Registration

A summary of the session is now available here.

We are inviting suggestions, comments, and other discussion points for a workshop session on interfacing with policy, to be chaired by u/StephanLewandowsky.In this session, we seek to understand how the wider science community can be policy-relevant by asking questions such as:What formats do policy makers and practitioners require?What kind of outputs can we provide?What ways could we crowd source expertise to synthesise, critique, and distill existing and new knowledge?How do we tackle the challenge of short time frames in the policy cycle?How do we avoid being ‘too political’ when communicating research?You can register for the SciBeh Virtual Workshop here.

Ideas for discussion: how can research interface well with policy?

We are inviting suggestions, comments, and other discussion points for a workshop session on tools for online research curation, to be chaired by u/stefanherzog.In this session, we bring together experts in machine tools to tackle the problem of knowledge retrieval, aggregation, and evaluation. We look at what has been done in the past year to aggregate and quality-check new information using machine learning and NLP techniques, and ask what is the next step in delivering robust knowledge to those who need it.Some of our questions include:What are the pros and cons of the various search and filter systems created now?* What design features do researchers, policy-makers, and the public need in a COVID-19 knowledge base?How can we adapt the tools we have to improve the curation of crisis-relevant knowledge?You can register for the SciBeh Virtual Workshop here.

Ideas for discussion: tools for online research curation

We are inviting suggestions, comments, and other discussion points for a workshop session on managing online research discourse, to be chaired by u/UHahn.In this session, we address the issue of building sustainable, transparent, and constructive online discourse among researchers as well as between researchers and the wider public. Some of the questions we ask are: What levels of discourse support quality assurance in research? Why should researchers discuss work in online spaces, with each other and with the public?How should researchers engage in online research discourse to combat misinformation?

Ideas for discussion: how to manage online research discourse?

Great presentation by Cooper Smout on @proFOK for trying to overcome the collective action problem of open science #scibeh2020

Fun fact: this EXACT model was reviewed and appraised (first paper above) and then externally validated (second paper), showing it was very poor, months ago.... w/ @laure_wynants @Rishi_K_Gupta

Now it has become prominent for COVID19? Got to be kidding @EricTopol https://bmj.com/content/369/bmj.m1328… https://medrxiv.org/content/10.1101/2020.07.24.20149815v1

10.1101/2020.07.24.20149815

Background The number of proposed prognostic models for COVID-19, which aim to predict disease outcomes, is growing rapidly. It is not known whether any are suitable for widespread clinical implementation. We addressed this question by independent and systematic evaluation of their performance among hospitalised COVID-19 cases.Methods We conducted an observational cohort study to assess candidate prognostic models, identified through a living systematic review. We included consecutive adults admitted to a secondary care hospital with PCR-confirmed or clinically diagnosed community-acquired COVID-19 (1st February to 30th April 2020). We reconstructed candidate models as per their original descriptions and evaluated performance for their original intended outcomes (clinical deterioration or mortality) and time horizons. We assessed discrimination using the area under the receiver operating characteristic curve (AUROC), and calibration using calibration plots, slopes and calibration-in-the-large. We calculated net benefit compared to the default strategies of treating all and no patients, and against the most discriminating predictor in univariable analyses, based on a limited subset of a priori candidates.Results We tested 22 candidate prognostic models among a cohort of 411 participants, of whom 180 (43.8%) and 115 (28.0%) met the endpoints of clinical deterioration and mortality, respectively. The highest AUROCs were achieved by the NEWS2 score for prediction of deterioration over 24 hours (0.78; 95% CI 0.73-0.83), and a novel model for prediction of deterioration <14 days from admission (0.78; 0.74-0.82). Calibration appeared generally poor for models that used probability outcomes. In univariable analyses, admission oxygen saturation on room air was the strongest predictor of in-hospital deterioration (AUROC 0.76; 0.71-0.81), while age was the strongest predictor of in-hospital mortality (AUROC 0.76; 0.71-0.81). No prognostic model demonstrated consistently higher net benefit than using the most discriminating univariable predictors to stratify treatment, across a range of threshold probabilities.Conclusions Oxygen saturation on room air and patient age are strong predictors of deterioration and mortality among hospitalised adults with COVID-19, respectively. None of the prognostic models evaluated offer incremental value for patient stratification to these univariable predictors.

Systematic evaluation and external validation of 22 prognostic models among hospitalised adults with COVID-19: An observational cohort study

We investigate the effects of large group meetings on the spread of COVID-19 by studyingthe impact of eighteen Trump campaign rallies. To capture the effects of subsequent contagionwithin the pertinent communities, our analysis encompasses up to ten post-rally weeks for eachevent. Our method is based on a collection of regression models, one for each event, thatcapture the relationships between post-event outcomes and pre-event characteristics, includingdemographics and the trajectory of COVID-19 cases, in similar counties. We explore a totalof 24 procedures for identifying sets of matched counties. For the vast majority of thesevariants, our estimate of the average treatment effect across the eighteen events implies thatthey increased subsequent confirmed cases of COVID-19 by more than 250 per 100,000 residents.Extrapolating this figure to the entire sample, we conclude that these eighteen rallies ultimatelyresulted in more than 30,000 incremental confirmed cases of COVID-19. Applying county-specific post-event death rates, we conclude that the rallies likely led to more than 700 deaths(not necessarily among attendees).

The Effects of Large Group Meetings on the Spread ofCOVID-19: The Case of Trump Rallies

next step- combine with COVID mortality and prevalence..

Hey @chrisdc77, is there a way to cite your modified pyramid of evidence?

one thing that seems incredibly hard to distinguish in COVID sceptic Twitter is whether someone is a bad faith actor or whether there are pockets where understanding still just is that limited. So should we drop distinction in trying to deal with people?

"If Whitty and Vallance had taken questions, I hope someone would have asked them what the projected number of cases would be on 13th Oct if you discount the 91% of “cases” that are false positives." Tweet, (now deleted), 21 September 2020

"Some lockdown enthusiasts pick out a handful of examples where lockdowns have coincided with a fall in Covid deaths but that’s not a scientific approach." The Critic, 11 January 2021

"If you compare mortality in December of 2020 with average December mortality over the the last five years, there doesn't as far as I can see appear to be any increase at all" "London Calling" podcast, 4 January 2020

"A winter bed crisis in the NHS is an annual event... According to PHE, there was no statistically significant excess all-cause mortality in England in the final week of 2020" The Critic, 11 January 2021*

But sadly, he hasn't learned anything in 2021...

"Even if we accept the statistical modelling of Dr Neil Ferguson’s team... which I’ll come to in a minute, spending £350 billion to prolong the lives of a few hundred thousand mostly elderly people is an irresponsible use of taxpayers’ money." The Critic, 31 March 2020

"I’m going to go out on a limb and predict there will be no “second spike” – not now, and not in the autumn either. The virus has melted into thin air. It’s time to get back to normal." Telegraph, 25 June

"The country is well on its way to acquiring herd immunity and the much-ballyhooed “peak” that we’re trying to avoid by locking ourselves down won’t materialise." The Critic, 31 March 2020

"Is the case fatality rate really as high as Professor Neil Ferguson and his team at Imperial College would have us believe? Dr John Ioannidis of Stanford University has speculated that it may end up being 0.05 per cent, lower than seasonal flu." Telegraph, 3 April

"The choice is between switching to mitigation or maintaining the lockdown indefinitely... It’s inevitable that we’re going to have to abandon the suppression strategy before we develop a vaccine out of sheer economic necessity" The Critic, 2 April

"What happened to the British people’s bulldog spirit, , our instinctive libertarianism? ... It’s tempting to think the feminisation of British culture has left us bereft of manly virtues. We have become men without chests, to use CS Lewis’s phrase." Telegraph, 18 April

"I was sent a paper by Mikko Paunio, a key scientific adviser to the Finnish Government, estimating that the infection fatality rate of Covid-19 is around 0.13 per cent – roughly the same as seasonal flu." Telegraph, 18 April

And even if you catch the disease, it it may not be much more deadly than a bad bout of seasonal influenza... We should dispense with silly, over-cautious social-distancing rules... Let’s just get back to the way things were." Telegraph, 29 May

"in some parts of the country – such as London – the virus is expected to have completely vanished by the end of next month." Telegraph, 29 May

"The decision to place the entire country in suspended animation on March 23 will end up costing more lives than the pandemic... one of the worst decisions in our history." Telegraph, 12 June

The scientists predict a massive resurgence of Covid-19 infections if we don’t “get on top of things” and that part of the report is unconvincing... the paper seems pessimistic about the level of immunity that the UK population has already acquired." Telegraph, 14 July

I may... convene a public inquiry of my own. The experts I’ll invite to sit on the panel won’t be the usual hacks with an axe to grind... They’ll be [like] Sunetra Gupta, the Oxford epidemiologist who believes we may have achieved herd immunity already" Spectator, 25 July

The check-in process at Heathrow took at least four times longer than usual and having to wear a face nappy for the entire journey was a pain in the bum. But... in Venice things started to look up – I’d finally escaped Gulag Britain." Telegraph, 12 August

"we were told... the number of infected people was on the rise again... the rise was due to a combination of increased testing and false positives." Telegraph, 14 August 2020

"as we sceptics are fond of pointing out, almost no one has the virus any more." Spectator, 15 August

"Should we be worried about the uptick in cases? Almost certainly not. It’s due to recent increases in testing capacity... Given the over-sensitivity of the PCR test, the rise in new cases is telling us just how many people have had Covid-19 in the past" Telegraph, 7 September

As the UK tragically hits a record number of Covid-19 deaths, Covid-sceptic-in-chief @toadmeister appears to have deleted all his tweets from last year. That's not surprising. Here are some of the things he claimed over the last year - a thread.

Another view on uncertainty associated based on Pfizer's results. Even if you were highly skeptical about MRNA vaccines (many are [were?]) with 50% prior belief that VE ~ 0, based on an 8:86 vax:placebo case split, the posterior probability that VE > 75% is ~ 1.

The chief medical officer of health takes the worst of a scathing auditor general's report on the province's pandemic response

Dr. David Williams is called out—and Doug Ford doubles down on him

More on Canada's data mess with great quotes from @JPSoucy who has been scraping covid data to keep track of issues. For example when BC pulled their case data Monday night to replace it next day with dramatically revised daily case counts for the weekend.

Meanwhile, BC is still refusing to share occupation data of covid cases with the Public Health Agency of Canada, actively blocking the ability to paint a comprehensive national picture.Quote Tweet

Relevant, high quality, and timely data matters. So does having a government that analyzes the data and acts on it.

The transit agency has published the first ridership map since the start of the pandemic. While the breakdown of stations and routes clearly show the impact of the health crisis on ridership, it also demonstrates the work that has never stopped to keep essential travellers moving throughout the threat. See how your route has fared.

How COVID has impacted transit – Metrolinx releases ridership map covering all GO Transit rail routes

Yep, I hope there will be a public inquiry at some point.

alongside dubious relationships with parties that in other contexts would require declarations of interest or that have independent hallmarks of being bad faith actors

indeed! I suspect also, though, that for the most egregious cases of harm caused such an inquiry will be able to identify what are clear failings by *scientific standards* - such as cherry-picked data, selective reporting, unwillingness to admit error etc...

good question, though I think anyone who genuinely believes they are acting for the greater good should welcome a public inquiry to make their case.

this question I wonder about a lot myself- how could and should science hold those accountable that have caused harm? do we have suitable ethical rules and guidelines? Or rules for distinguishing legitimate scientific debate from dangerous propaganda?

10.1136/bmj.a2338

Objectives To evaluate whether happiness can spread from person to person and whether niches of happiness form within social networks.Design Longitudinal social network analysis.Setting Framingham Heart Study social network.Participants 4739 individuals followed from 1983 to 2003.Main outcome measures Happiness measured with validated four item scale; broad array of attributes of social networks and diverse social ties.Results Clusters of happy and unhappy people are visible in the network, and the relationship between people’s happiness extends up to three degrees of separation (for example, to the friends of one’s friends’ friends). People who are surrounded by many happy people and those who are central in the network are more likely to become happy in the future. Longitudinal statistical models suggest that clusters of happiness result from the spread of happiness and not just a tendency for people to associate with similar individuals. A friend who lives within a mile (about 1.6 km) and who becomes happy increases the probability that a person is happy by 25% (95% confidence interval 1% to 57%). Similar effects are seen in coresident spouses (8%, 0.2% to 16%), siblings who live within a mile (14%, 1% to 28%), and next door neighbours (34%, 7% to 70%). Effects are not seen between coworkers. The effect decays with time and with geographical separation.Conclusions People’s happiness depends on the happiness of others with whom they are connected. This provides further justification for seeing happiness, like health, as a collective phenomenon.

Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study

The effect of social distance measures on deaths and peak demand for hospital services in England, 3 March 2020 https://gov.uk/government/publications/the-effect-of-social-distance-measures-on-deaths-and-peak-demand-for-hospital-services-in-england-3-march-2020… Coronavirus: action plan A guide to what you can expect across the UK, 3 March 2020 https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/869827/Coronavirus_action_plan_-_a_guide_to_what_you_can_expect_across_the_UK.pdf

The UK coronavirus plan offers no estimate of how many may die, warning only that "there could well be an increase in deaths". But on the same day as the plan's launch, a SAGE paper reports that even 12 weeks of measures would only reduce the death toll to around 290,000

"There was no need to panic or exaggerate, was the message. Most striking today was the way the experts, fully backed by the PM, played down a lot of the worst-case scenario options… Reason, aided by the right reassuring tone, ruled the day"

3 Mar 2020 Boris Johnson launches the UK Coronavirus Action Plan at a Downing St press conference "We have a fantastic NHS, fantastic testing systems and fantastic surveillance of the spread of disease. Our country remains extremely well prepared" https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/869827/Coronavirus_action_plan_-_a_guide_to_what_you_can_expect_across_the_UK.pdf

In 2020, covid-19 lockdowns saw global carbon emissions plummet by a record 5.6 per cent, according to one estimate by the Global Carbon Project. But the reprieve is looking increasingly short-lived. A monthly breakdown by the International Energy Agency (IEA) today shows that worldwide emissions in December 2020 were up 2 per cent on December 2019. China was the only major economy in which emissions grew for 2020 as a whole, up 0.8 per cent on 2019 levels, or 75 …

Global CO2 emissions have already rebounded above pre-pandemic levels

10.1016/S2666-5247(21)00029-X

Early projections of the COVID-19 pandemic prompted federal governments to action. One critical report, published on March 16, 2020, received international attention when it predicted 2 200 000 deaths in the USA and 510 000 deaths in the UK without some kind of coordinated pandemic response.1Ferguson NM Laydon D Nedjati-Gilani G et al.Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand.https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdfDate: March 16, 2020Date accessed: December 1, 2020Google Scholar This information became foundational in decisions to implement physical distancing and adherence to other public health measures because it established the upper boundary for any worst-case scenarios.However, the authors derived these projections from best available estimates at the time. The evolving nature of empirical knowledge about COVID-19 provides current estimates with more accurate information than what would have been available merely weeks after first discovery of the virus—plus the benefit of hindsight. For example, asymptomatic transmission has been said to be the Achilles' heel of public health strategies to control the pandemic,2Gandhi M Yokoe DS Havlir DV Asymptomatic transmission, the Achilles' heel of current strategies to control Covid-19.N Engl J Med. 2020; 382: 2158-2160Crossref PubMed Scopus (315) Google Scholar and several factors about asymptomatic cases remained uncertain during the early days. The report assumed that asymptomatic individuals were 50% as infectious as symptomatic cases,1Ferguson NM Laydon D Nedjati-Gilani G et al.Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand.https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdfDate: March 16, 2020Date accessed: December 1, 2020Google Scholar whereas the current US Centers for Disease Control and Prevention (CDC) estimates suggest a 75% infectiousness rate for asymptomatic individuals.3Centers for Disease ControlCOVID-19 pandemic planning scenarios.https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.htmlDate: Sept 10, 2020Date accessed: December 1, 2020Google Scholar A more important difference is the infection fatality ratio as originally projected in the Imperial College London (London, UK) report1Ferguson NM Laydon D Nedjati-Gilani G et al.Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand.https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdfDate: March 16, 2020Date accessed: December 1, 2020Google Scholar versus current estimations. A high ratio of asymptomatic individuals might have inflated the perceived mortality of the disease given the limited testing supplies and attention to symptomatic cases.

Revisiting the initial COVID-19 pandemic projections

10.31234/osf.io/7c5nf

Objective: To combat the wide-spread transmission of COVID-19, many countries, including the United Kingdom, have imposed nationwide lockdowns. Little is known about how these public health safety measures affect pregnant mothers and their offspring. This study aimed to explore the impact of COVID-19 public health safety measures on births in Scotland. Study Design: Cohort Study Methods: Using routinely collected health data on pregnancy and birth in Scotland, this study compares all births (N = 11220) between March and May 2020 to births in the same period in 2018 (N = 12428) to investigate the potential negative effects of public health safety measures introduced in Scotland in spring 2020. Birth outcomes were compared using Mann-Whitney-U tests and chi-square tests. Results: Mothers giving birth during the pandemic tended to combine breastfeeding and formula-feeding rather than exclusively breastfeed or exclusively formula-feed, stayed in hospital for fewer days and more often had an epidural or a spinal anaesthetic compared to women giving birth in 2018. Conclusion: Overall, results suggest little impact of public health safety measures on birth outcomes. Further research is needed to explore the longer-term impacts of being born in the pandemic on both maternal mental health and child development.

Effects of COVID-19 Public Health Safety Measures on Births in Scotland between March and May 2020

Source: https://coronavirus.data.gov.uk/details/interactive-map… (today's map is specimen date ending 16 Jan)

So now the worst hit area of Oxford is 18 times as bad as the best off area #healthInequality #COVID19

So remember: Hospitalizations are not static thing When hospitals get full, by definition you can’t hospitalize all the folks you'd like to hospitalize And many who would benefit from hospitalization suffer Because only the very sickest get a bed Everyone else goes home Fin

So what's the bottom line? We can see in data that we are likely admitting far fewer COVID patients than we would have just 2 months ago Likely due to very full hospitals Because political leaders have politicized mask wearing, social distancing Its a travesty 16/17

This is not Doctors being cruel Its that with fewer and fewer beds, bar for hospitalizing anyone is rising And likely means more people are suffering, getting worse, or even dying at home All because our hospitals are really full 15/17

Everything -- COVID and non-COVID is affected So borderline admission for heart failure? Person now goes home Pt with infected leg where you’d prefer IV antibiotics in hospital? They go home with oral antibiotics This is what is happening in hospitals across America right now

The evidence suggests that many of these pts now being sent home will likely do worse at home Some may be OK -- but others will come back sicker or even die at home And one more thing When hospitals fill up, threshold for admission for everything goes up

Best guess? we're admitting half the COVID pts we would have admitted October 1 Is this a problem? If those people don’t need hospitalization, is this bad? Well, on Oct 1, we would have said that they need hospitalization So “need” is fluid But here's the other key issue

And that’s what appears to be happening Here's the bottom line: 1 in 3 people who would have been admitted on October 1 aren’t being admitted by November 22 That’s a big change! And given big rise in test positivity – its likely much higher 11/15

Of course, critically ill patients always get admitted But over time, marginal admissions start disappearing And as hospitals get fuller, what is defined as “marginal” keeps changing Until you only admit the sickest folks 10/12

So what’s happening? What you’d expect – doctors’ threshold for admission is likely going up In early Oct, with plenty of beds, a COVID patient who is clinically borderline likely gets admitted to ensure they do OK By Nov 15, beds in short supply, that person likely goes home

May be more testing means we're picking up more cases? No % test + is rising from 5.1% (Oct 1) to 12.1% (Nov 15) So number of cases being missed is climbing Proportion of pts being admitted from identified cases should be RISING That 3.5%? Should go up. Instead, its falling

But in November, that number starts falling Initially to 3.2% by Nov 8 By Nov 15, drops to 3.0% By Nov 22, drops to 2.5% And by Nov 29, down to 2.1% So what’s going on? What does this mean? 7/12

I’m ignoring data from last week b/c Thanksgiving messiness though this effect gets much more dramatic But here’s the fact: Over much of September and October, you could look at cases today and predict that 3.5% of that number gets hospitalized 7 days later 6/10

On Nov 1, there are 80K new cases. On Nov 8, you’d expect 2804 new hospitalizations (80K*0.035) What was it actually? 2604. A little less. But fine On Nov 15, there are 146K new cases. On Nov 22, expect 5111 new hospitalizations (146K*0.035) But there are only 3670 5/10

So let's look at data On October 15, there are 53K new cases On Oct 22, you’d expect 1844 hospitalizations (53K*0.035). What was it actually? 1855 So far so good 4/10

LOTS of caveats to this formula Not all states report new hospitalizations (formula takes that into account) Could build a 10 day lag formula (3.7% hospitalized by 10 days) All data I report are 7-day moving avgs from @COVID19Tracking 3/10

So what do I mean "proportion of COVID pts hospitalized falling"? For months, you could reliably predict new hospitalizations How? By taking cases 7 days prior, multiplying by 3.5% That is 3.5% (1 in 29) of those diagnosed today will be hospitalized about 7 days later 2/10

There is something funny happening with COVID hospitalizations Proportion of COVID pts getting hospitalized falling A lot Just recently My theory? As hospitals fill up, bar for admission rising A patient who might have been admitted 4 weeks ago may get sent home now Thread

Why I care about replication studies

The 20% Statistician A blog on statistics, methods, philosophy of science, and open science. Understanding 20% of statistics will improve 80% of your inferences.

so there seems ample room to me for claiming that the Spectator headline is *correctly* labelled as 'misinformation' - all thoughts appreciated

and finally one does not even need to take speaker intention to be constitutive of 'misinformation' (which I take to be the Twitter/FB approach, please correct me if wrong..)

which one has every reason to believe will not be the chosen interpretation the idea that one might 'lie' with natural language pragmatics where utterances are semantically accurate is an example of that and there is wider debate on this

the wider point being that it seems uncontroversial to me that one can "misinform" by exaggeration and/or source misattribution and, more controversial, but right that one can intentionally "misinform" by choosing a statement correct on some non-preferred reading

for which a more informative rendition would be something like: "Expert thinks equivocal study findings suggest limited utility for masks" or some such...

'significance' in a looser, non-statistical sense (which lay readers are also likely to impute), any such inferences are interpretive and not properties of the study itself

if that's not what it says. So it seems also that the likely reading of this will be a) (i.e. a telegraphic rendition thereof). and while one might be able to argue about what a lack of statistical significance does and does not show or imply, last but not least about ...

as in a) "study established/indicated face masks don't have significant effects" or b) "study failed to find significant effects of face masks" We know also even when faced with b) many (most?) lay people will take that to imply that they don't have significant effects, even

The critical question is whether the summary "study shows face masks have no significant effect" is 'misinformation'. First off, the title is ambiguous. It could be paraphrased in two different ways: a) is telegraphic version of a longer sentence "study showed *that* face..

Here was the 'offending' Heneghan Tweet/FB post:

interesting opinion in an important wider debate, but I think the Heneghan 'censure' is more complicated than I think the opinion makes out - a thread on interpretation and misinformation

Here's what happened when I posted our latest @spectator article to Facebook - I'm aware this is happening to others - what has happened to academic freedom and freedom of speech? There is nothing in this article that is 'false'

A head-to-head comparison of lab and rapid coronavirus tests drew mixed reactions from experts, who raised concerns about accuracy.

A Rapid Virus Test Falters in People Without Symptoms, Study Finds

Professor Karl Friston's re-run of his dynamic causal model differs from the gloomy predictions. It predicts a peak in deaths in the next 7-10 days, 4 weeks enough time to get back down to 5000 cases/d for test, trace, isolate to work, and R already below one. Hope he is right.

10.1101/2020.10.30.20222935

Background Routine testing for SARS-CoV-2 in the community is essential for guiding key epidemiological decisions from the quarantine of individual patients to enrolling regional and national preventive measures. Yet, the primary testing tool, the RT-qPCR based testing, is notoriously known for its low sensitivity, i.e. high risk of missed detection of carriers. Quantifying the false-negative rate (FNR) of the RT-qPCR test at the community settings and its dependence on patient demographic and disease progression is therefore key in designing and refining strategies for disease spread prevention.Methods Analyzing 843,917 test results of 521,696 patients, we identified false-negative (FN) and true-positive (TP) results as negative and positive results preceded by a COVID-19 diagnosis and followed by a later positive test. Regression analyses were used to determine associations of false-negative results with time of sampling after diagnosis, patient demographics and viral loads based on RT-qPCR Ct values of the next positive tests.Findings The overall FNR was 22.8%, which is consistent with previous studies. Yet, this rate was much lower at the first 5 days following diagnosis (10.7%) and only increased in later dates. Furthermore, the FNR was strongly associated with demographics, with odds ratio of 1.74 (95% CI: 1.58-1.90) for women over men and 1.36 (95% CI: 1.34-1.39) for 10 years younger patients. Finally, FNR was associated with viral loads (p-value 0.0005), with a difference of 1.50 (95% CI: 0.70-2.30) between the average Ct of the N gene in a positive test following a false-negative compared to a positive test following a true-positive.Interpretation Our results show that in the first few days following diagnosis, when results are critical for quarantine decisions, RT-qPCR testing is more reliable than previously reported. Yet the reliability of the test result is reduced in later days as well as for women and younger patients, where the viral loads are typically lower.

Association of COVID-19 RT-qPCR test false-negative rate with patient age, sex and time since diagnosis

10.15626/MP.2018.884

Meta-analyses are susceptible to publication bias, the selective publication of studies with statistically significant results. If publication bias is present in psychotherapy research, the efficacy of interventions will likely be overestimated. This study has two aims: (1) investigate whether the application of publication bias methods is warranted in psychotherapy research on posttraumatic stress disorder (PTSD) and (2) investigate the degree and impact of publication bias in meta-analyses of the efficacy of psychotherapeutic treatment for PTSD. A comprehensive literature search was conducted and 26 meta-analyses were eligible for bias assessment. A Monte-Carlo simulation study closely resembling characteristics of the included meta-analyses revealed that statistical power of publication bias tests was generally low. Our results showed that publication bias tests had low statistical power and yielded imprecise estimates corrected for publication bias due to characteristics of the data. We recommend to assess publication bias using multiple publication bias methods, but only include methods that show acceptable performance in a method performance check that researchers first have to conduct themselves.

Publication Bias in Meta-Analyses of Posttraumatic Stress Disorder Interventions

We've had years of this on climate change. "Later, we'll be speaking to someone who has spent their life researching climate science, whose work has to be rigorously assessed. But first, let's hear from a man in a tin-foil hat, who has very real concerns that this is a hoax".

Why is @BBCr4today playing vox pops with random members of the public speculating that the Covid vaccine might be unsafe? It's a *news* programme, not a phone-in. If there's informed, scientific disagreement, then air it. But this is just irresponsible.

Platform governance and the 2020 Presidential Election

‘Be the Twitter that you want to see in the world’

Exclusive: study of low-risk individuals finds impairments four months after infection

Damage to multiple organs recorded in 'long Covid' cases

10.1038/s41562-020-01009-0

Assessing the effectiveness of non-pharmaceutical interventions (NPIs) to mitigate the spread of SARS-CoV-2 is critical to inform future preparedness response plans. Here we quantify the impact of 6,068 hierarchically coded NPIs implemented in 79 territories on the effective reproduction number, Rt, of COVID-19. We propose a modelling approach that combines four computational techniques merging statistical, inference and artificial intelligence tools. We validate our findings with two external datasets recording 42,151 additional NPIs from 226 countries. Our results indicate that a suitable combination of NPIs is necessary to curb the spread of the virus. Less disruptive and costly NPIs can be as effective as more intrusive, drastic, ones (for example, a national lockdown). Using country-specific ‘what-if’ scenarios, we assess how the effectiveness of NPIs depends on the local context such as timing of their adoption, opening the way for forecasting the effectiveness of future interventions.

Ranking the effectiveness of worldwide COVID-19 government interventions