5) Science matters but has to be broader,inclusive, transparent. How we do things matters as much as what we do. Research culture & environment matters.6)Science does not exist in a vacuum, it is sustained by communities & society it must be an integral part of not detached from.

3) Multilateralism matters, will & can deliver. We have looked into the abyss of nationalism, & chose another path. 4) Global & national institutions, well governed, transparent & trusted - matter. We need to challenge them yes, but also protect & value them.

I believe that means 1) Investment prior to crisis contributes to capacity to prevent, prepare & respond. A shift from efficiency to resilience 2) Partnerships matter, partnerships forged over years, inclusive & equitable and providing value & benefits before a crisis.

Need to understand & address all these drivers and the consequences, not just the symptoms.

Such challenges impact across four concentric circles - 1) Direct Health consequences. 2) All other health concerns. 3) Inequality, Jobs, Livelihoods, Trust, Economies, trust between governed & the governing. Amplifying fault lines in all societies.

These are & will remain common in the 21stC hence these challenges have and will be more frequent, more complex & inter-related.

We have had multiple warnings from Nipah in 1999 to SARS-1, H5N1, Ebola, MERS, Zika, Chik, H1N1 & others. Many common drivers-Ecological change, changes in human:animal interactions, urbanisation, travel & trade. And in times of neglect of public health & geopolitical tensions.

This is the reality of COVID19 & will continue to be so. Focus on the drivers and perspective from the last 20 years.

People will be writing,discussing,considering COVID19 in 100yrs as we discuss pandemic 1918 now. We are living through history as it is being made. When read history books can seem romantic. It never is. It is tragic, confused,trade offs,painful,difficult & frightening. As now.

On this second day of #OAWeek2020 we like to declare a second #OpenScienceChampion: @MicheleNuijten, (co)developer of Statcheck, an open source statistics tool now used to check Covid-19 preprints for statistical errors, who advocates Open Science whenever possible at #TilburgU

Ar scáth a chéile a mhairimíd. #COVID19ireland #LockdownIreland #Level5

Good morning. Today the APPG on #Coronavirus will be hearing evidence on local lockdowns and exit strategy. Tune in at 11.30 #rapidinquiry Witnesses will include: @devisridhar @AliceWiseman11 @ReicherStephen @davidnabarro @Kevin_Fong

@BristolUni have put the lives of students and staff at risk by bringing students to halls, so we're calling a rent strike. These are our demands

Damage to non-covid hospital business is a result of coronavirus overloading the hospitals, not a result of measures to control community transmission of the virus. Low community transmission means more cancer patients getting treatment. How is that simple fact getting twisted?

or: "Masks have been determined to be unnecessary even in surgical settings, and of no benefit in preventing infections.7" on basis of 1981 study with a single "trial" observing no significant rise in operating theatre infections in a 6 month period in 1981, in 1 hostpital

such as this on danger of cloth masks: "Healthcare workers wearing cloth masks had significantly higher rates of influenza-like illness after four weeks of continuous on-the-job use, when compared to controls. (39)" where 'control' turns out to be *medical masks*

the piece claims: "Masks have been shown through overwhelming clinical evidence to have no effect against transmission of viral pathogens." citation for this is a blog post on a naturopath website bu one of the authors. That blog post includes tendentious misrepresentations

Researchgate is hosting (and "featuring") pseudoscience that is appearing as 'science' in anti-lockdown, anti-mask Twitter feeds I have no idea about scale of this, as just only stumbled on it myself. see, e.g.,

Our Covid-19 Task Force comments on the importance of scientific scrutiny of Covid-19 tests

On the same day Victoria recorded 723 cases back in July, the UK recorded 763. Today, Victoria records 1 case and 0 deaths, the UK records 15,650 and 136 deaths (43429 total) Who still thinks we should have opened up like the UK and ‘learned to live’ with COVID-19’ ?

Here is my latest heatmap showing how cases are travelling through the age groups. It is very concerning.

.conflating a heterogeneously motived "outcome" with a psychological construct is a terrible example of conflating redescription with "theory" (a cardinal sin in psych. even where those outcomes are, unlike here, very narrowly defined) ..rant over...

those include changed risk perception, lack of trust, changed societal cost, divergent views on best policy - everything! that's as much (and as little) a behavioural science concept as "beer sales have dropped" across countries 3/4

"pandemic fatigue" (wording matters!) is simply defined here as the resultant *outcome* (behaviour) namely: "demotivation to follow recommended protective behaviours, emerging gradually over time and affected by a number of emotions, experiences and perceptions" 2/4

2 issues: 1) the behavioural scientists involved at the time have since clarified they never pushed 'behavioural fatigue" as a thing at the time, so whatever this is, it should not serve to rewrite that history and 2) this is not your standard behavioural science concept... 1/4

In 2017 @USATODAY reported that Jared Kushner & Ivanka Trump were routing personal and government emails through a private server hosted by the Trump Organization. @nytimes did run one front-page article on this, before the story was eclipsed by other news

Consequently other publications also wrote up this story and Clinton's IT Security became the most consumed & remembered issue of the election.

In 2016 @nytimes published 10 front-page articles on Secretary Clinton’s IT Security in a 6 day period from 10/29-11/3/16. NYT was signaling this is an important story, that people should pay attention to this story above and beyond other possible topics about the 2016 election.

BAME citizens (a relevant characteristic under the Act). So where is the discussion of this issue with regards to policy measures in the public debate? any lawyers out there who can leap in? https://legislation.gov.uk/ukpga/2010/15/section/149… 2/2

more musings on 'where is the law in shaping COVID response?': under 2010 UK equalities act, any public policy must be scrutinised for direct and indirect (inadvertent) discriminatory impact. a central thing we've learned about C-19 is its disproportionate impact on .. 1/2

(RETWEET) This may be the most incredible data visualization I've ever seen. It shows COVID-19 transforming from a blue-state scourge (spring 2020) to a red-state one (summer/fall 2020)—with the clear implication being that Trump led his cultists to death.

Haste to publish has been part of pandemic culture, but at what cost? Scientists should not act in isolation from the societies in which they live. Being the 'first' may be tremendously exciting, but have consequences that open a Pandora's box nigh impossible to close. End/

Others with more commercially minded aims have seized on the uncertainty as an opportunity... 7/

Our response was submitted within 5 days of publication. Others sent letters too. It took 5 months to publish. Damage done by national policies of separating mothers from infants, and the doubt placed in mothers' minds, are becoming clear. 6/

More papers have since supplanted this report. Breastfeeding has been consistently considered safe by the @WHO and milk can contain protective antibodies. But this case report was published in The Lancet. It has been cited over 70 times already. 5/

And the paper showed high Ct values, well over 30, suggesting from the outset that fragmentation was also the case for SARS-CoV-2 - part of human milk's evolutionary selected antiviral mechanisms. And tellingly, two of the four cited papers were non-peer reviewed preprints. 4/

This would have been unlikely - other coronaviruses, including MERS and SARS, are fragmented by the lactating breast's innate and adaptive immune mechanisms. The baby in the report had fed at the breast while symptomatic just before sampling making contamination highly likely. 3/

The original article was flawed in methodology and conclusions, while not overtly written as such, indicated to the global health community and policymakers that #covid19 could be transmitted through breastfeeding. 2/

I never saw a future that I'd be sad to see my article published in @TheLancet. But in many ways this feels too late. When the original paper came out in May, my coauthors and I were stunned. 1/

NY went into weekend w/good news & leaves it on same path. Manhattan back down to 0.4%. No counties above 5%. Testing excellent. It may be optimistic to believe these clusters are tamped down, but the efforts of the last 2 weeks may well have worked. #MaskUp #NewYorkStrong

scibeh's goals. So, all comments and thoughts welcome- both here and on the reddits, both on argument itself and the meta-science issues raised. It would be good to continue moving forward debate on the role of behavioural scientists in this crisis.

finally, in the interest of full disclosure: @profnfenton is a much valued collaborator of mine (Ulrike Hahn writing here), adding extra complexity to public exchange of argument. But the piece was put out on Twitter presumably to prompt public debate, and such debate is one of

AND- rises have (indirect) public health implications if contact tracing and testing reaches breaking point (taking away ability for targeted stopping of transmission)

but I feel I can evaluate argument itself. My concern: Even if 1 & 2 were true, unless the proportion of asymptomatic cases and FPs are rising disproportionally, a rise must mean *other cases* are also going up and *these* will cause illness, death and further spread

1 and 2, from my own reading of the literature are wrong, but equally outside my own core competence

To me, it makes multiple statements outside the core expertise of the behavioural scientist: 1. 'almost all of those will not show any symptoms of a C-19 illness' 2. 'they will not 'spread the virus' 3. implication that rise in numbers doesn't suggest increasing population risk

– as can be seen from the university student ‘cases’ – are either asymptomatic or false positives., i.e. they do not – and will not – show any symptoms of a ‘COVID-19 illness’. Nor will they ‘spread the virus’ to others." ('expertise?')

it has the central argument: "the massive increase in ‘new cases’ is almost completely explained by factors that have nothing to do with an increasing population health risk. New cases are simply the count of those who get a positive test result. But almost all of those

So, the tweet itself: it highlights a piece published with the authors' academic affiliations on a website started by a divisive, U.K. commentator - Toby Young (for those outside the UK, wikipedia or simply Google will let you form your own opinion), ('too political'?)

which is even more difficult to receive when public. None of these issues are resolved, and all merit further debate. So one motivation in highlighting (and critiquing) this tweet is not just to engage and present a particular view, but also to reinvigorate those debates:

fashion that promotes good science, and good evidence for policy. For that to work, it is crucial that we find a tone that will not lead people to withdraw or silence voices with other perspectives. But this is far from easy, given that such exchanges may involve critique...

a further, pressing, concern given scibeh's goals of developing a transparent, online community suitable for scientific exchange building from extant social media has been how to conduct discourse and build an environment in which debate can happen in a constructive

and what kind of "expertise" behavioural scientists have in this pandemic, and how that should shape scientists role

key early concerns were where we might draw the line (if at all) at scientists being "too political"

SciBeh started with attempts to prompt discussion on how behavioural scientists should best respond to the crisis. https://psyarxiv.com/hsxdk/ which led to the present environment of @SciBeh, our Reddits and the SciBeh database of behavioural science relevant COVID material

A (long!) thread about a tweet that raises many of the questions that have been central to http://SciBeh.org's concerns

To sum up: this is a very bold article that I’m not convinced can really back up its claims. Does’t mean it’s wrong. But I don’t think the evidence is nearly as strong as it is made out to be. STILL. Govs can still choose to put kids over bars and help schools open. /Fin

To me this seems like basic aresol transmission control so I find it really odd that someone claiming expertise is making such an obvious mistake. Stones and glass houses and all... But claiming business are not mass spreaders is way off base. See: bars & eat-in restaurants. 7/

And as overconfident as the article comes off to me, this statementis maybe the most problematic. Reminder: Crowding transmission. Limiting school to kids in need crowding. Limited students IS safer vs full school attendance. Add everyone & the risk changes. 6/

Elementary and secondary schools don’t have the resources & testing anywhere near the level of colleges or some summer camps. It’s not that schools CAN’T be safely opened. It’s that the resources aren’t there for many schools so caution is warranted. 5/

Studies based on contact tracing in a local area would be much more informative than high level stats. These could help us understand the effects on the broader community & staff. Staff are an important target, and there have been deaths of teachers. 4/

One thing to keep in mind is that schools within states have different policies. The data we see are not that of opening all schools - some are fully virtual, some partly, some not at all. Not all kids in these analyses are in school. This will underestimate any effect. 3/

I’m all for a data-driven approach & I definitely applaud the work needed to pull this data together! But. Epidemics & outbreaks are local. To me, pooling data across all states-or even within a state-is asking the wrong question. Especially since testing in kids is low. 2/

A few thoughts on this... (Other folks who know much more than me should definitely chime in! Looking at you @Theresa_Chapple @JasonSalemi @COVKIDProject @EpiEllie!) Mini thread. 1/n

A few examples of what epidemics/response can look like: - South Korea: https://imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-25-south-korea/… - Germany: https://ourworldindata.org/covid-exemplar-germany… - Sweden: https://bbc.co.uk/sounds/play/m000mqpv… - Hong Kong: https://researchsquare.com/article/rs-34047/v1… - Vietnam: https://ourworldindata.org/covid-exemplar-vietnam… - Japan: https://sciencemag.org/news/2020/05/japan-ends-its-covid-19-state-emergency… 2/2

It’s a straw argument to claim our COVID options are ‘majority back to normal’ vs ‘lockdown’ (i.e. stay-at-home orders, school closures). There are now many international examples available to learn from. When outlining options, we need sound research, not simplistic rhetoric. 1/

Next week we'll launch the mobility dashboard. In the meantime, our intro video tells the story of the full initiative, supported by a Mitacs Research Trainee Award and produced by research assistant extraordinaire, Katherine Hovdestad

We had hoped to access publicly-collected mental health data-however, it's not available yet though researchers are working on it. The pandemic has revealed the importance of data, and yet despite quantity of data collected, making that data useable & accessible is a challenge.

The dashboard highlights the current status of increasing COVID-19 cases/hospitalizations amidst the context of other public health measures: few school outbreaks, surgical backlogs, shortened ER waits, fewer visits to supervised injection sites + more overdose-related deaths

Toronto After the First Wave: Tracking Toronto's experience and response to COVID-19 in Fall 2020, our first dashboard focuses on measures of public health https://torontoafterthefirstwave.com/dashboards/health/

3. and how is this not going to end in a decade of law suits? 4. finally (really first): get well soon!

never seen before in my life time... 2. I continue to struggle to understand what has happened to the legal frameworks that have governed working in higher education for the last decade. How did we get from health & safety assessments for lab based psych exp. to F2F teaching now

point being made here that is relevant more generally not just to high stakes decisions involving the well-being of others, but to paths we need to pursue in order to improve the quality of our public discourse, in particular on social media where 'talk is cheap' in ways I've ..

1. as someone who worked on argumentation in the 'beforetime' (speaking as Ulrike Hahn here), I have been very interested in the role of *tying claims to own behaviour* in the context of argument quality and argument evaluation and I think there is a deep..

retweeting this because it raises many wider issues: 1/5

Just had a positive coronavirus diagnosis. I was infected *in* a seminar. Open invitation: any university “leader” who still thinks F2F teaching is safe, come round mine. We can sit 2m apart in my bedroom, I’ll pop a mask on, and we can discuss it for two hours. Any takers?

Outside Parliament, an orchestra of 400 musicians perform 20% of Holst's 'Mars' to highlight the lack of support offered by the government to freelancers within creative industries during the COVID-19 crisis.

Numbers need context. People don’t normally think in numbers. When forced to put a number on risks of people dying from COVID-19 if they caught it, they over-estimate (and the effect of the number format they are asked to use is evident). But they do know the major risk factors.

People liked ‘positive framing’ (how many likely survive), but the numbers were rated a bit harder to understand. Probably safest to stick with familiar ‘likelihood of dying’. A visual scale helps: despite the difficulties of a linear scale, it was more trusted over log scale.

hen our advice on how to communicate the numbers: Percentages seem clearest format, but also convey the smallest feeling of risk. For balance maybe include an ‘x out of 1000’ translation, which gives a larger feeling of risk. Do not describe as ‘your risk’ – maybe ‘risk result’

2) What are you trying to communicate? The risk of catching it and dying from it, or the risk of dying from it if you catch it? Whichever it is, be very clear 3) Who are your audience? UK public keen for this information in our surveys, and GPs don’t want to be ‘gatekeepers’

Preprint out – our work on communicating personal risks from COVID-19: https://doi.org/10.1101/2020.10.05.20206961… In summary, for those developing personal risk calculators: 1) Are you trying to change behaviour or simply inform people? It changes your communication approach. UK public opinion

Can you look at the age groups Scotland publishes to avoid the 15-19 hiding the school kids. They had data up to the 27th Sept 0-4, 5-11, 12-17 and the whole group 2-17 but it’s hard to find.

A couple of charts showing the age of the new positive cases in England/Scotland over the past week, per 1m pop Englands figures are quite high in comparison because of the backlog issue, but it looks like 15-24 year olds are behind the bulk of the new cases (uni outbreaks)

These data indicated how much more severe COVID19 is for those over 35 when compared to flu.

It rather looks likely that 10k cases (likely markedly more) cannot have been notified to the individuals OR referred in to T&T for contact tracing and isolation. Much more information would be v. helpful indeed. Which groups of people affected? Schools? Students? Care Homes

Steady climb upwards for patients in hospital. 2329 for England alone, (up 135 since Sat & up 334 since Thursday) Expect c 450 more A slower for ventilation (to be expected especially as it is not being used as first defence any more, favouring CPAP and drug intervention)

Today we only have data for England and Wales for admissions and that was for Friday..and these data often lag. 465. The last day when we had all 4 nations report was last Tuesday (422) The numbers are not shooting up. Uncertain to what extent that is lag or a steadying.

Then there is the question of what happens to tests too old to process, given there appears to be a log jam? How many are voided or unclear? Or how many were processed bu not recorded as such? We really need to know.

Checking positives by specimen date (ie when swabbed). Remember these tend to be quite a lag on these, esp Pillar 2. So expect the most recent days to increase quite a bit. But we can see 8.5k already on Thursday. 9.5k on Wednesday. C 8.5k Mon and Tues of last week

. Brace yourself. 22,961 new cases. More later.

We need discussions about what measures should look like, and what is feasible/sustainable. But we also need to frame any discussions around the actual dynamics of SARS-CoV-2 as a contagious disease, not under simplistic assumptions about control vs cases. 6/6

First, it means less COVID burden in terms of hospitalisations and deaths. And second, it means more capacity to use targeted measures (e.g. test & trace) to keep transmission down, which in turn could allow other types of measures to be relaxed. 5/

If control measures are keeping cases flat at 10k per day (for example), those same measures would also keep things flat if cases were at lower level. In fact, given a choice of R=1 and a high or low infection level, there are two benefits to going for the low option... 4/

But of course, this isn’t how infectious diseases work. If control measures are relaxed so that R is above 1, we’d expect cases - and hospitalisations - to continue to grow and grow until something changes (e.g. control reintroduced, behaviour shifts, immunity accumulated). 3/

If discussions are framed around the assumption of a simple inverse relationship between control and cases, it can lead to erroneous claims that if cases/hospitalisations are low, control measures can be relaxed and case counts will simply plateau at some higher level. 2/

I often see the misconception that control measures directly scale COVID case numbers (e.g. “hospitalisations are low so measures should be relaxed”). But in reality, measures scale *transmission* and transmission in turn influences cases. Why is this distinction important? 1/

The virus is real, it's nasty, it's killing people, and common humanity ought to mean that we respect that. A majority of people in every country are doing their best to stay safe. The people who blithely wheel out the "99.5% will survive" argument can get in the sea. /15 /end

I don't know what governments should do about COVID-19. I don't think we can continue with lockdowns indefinitely, and perhaps we will end up having to live with it as best we can. That's not my point here. /14

And 99.5% is just for your _first_ bout of COVID-19. We don't know how long immunity lasts, and just as important, we don't know if subsequent infections will be milder (better immune response) or worse (exacerbating previous organ damage that perhaps wasn't obvious). /13

Non-white people also seem to be at higher risk, at least in Western countries. But I get the impression that a certain subset of the people telling us that "99.5% survival is fine" probably don't really care too much about that. If you get my drift. /12

About half of the 50-year-old population of most Western countries has a comorbidity that places them at higher risk. They have perfectly normal lives apart from their diabetes or hypertension. They don't want to take a 1 in 200 chance of dying, let alone a higher one. /11

99.5% seems to be the average number quoted by these trolls. For some people, the risk is a lot more than 1 in 200. And these are not just 90 year olds with dementia and cancer. /10

Think about 1,000 people accepting this deal. They're gathered in a room. They step up to the stage, one by one. They take the envelope and roll the dice. 995 people get cheered. 5 get led away. A muffled shot can be heard from behind the curtain. /9

What would I have to offer you for you to take that bet? I bet it would be a lot more than your student loan debt. /8

Now let's play a game. I offer you a deal. It can be a bottle of champagne, or your phone bill paid for a year, or your student loan debt wiped off, or your mortgage paid, or a massive yacht. You get that, but you roll three dice. If they all come up 6, you die, right there. /7

Or think of something you do every day, and get wrong twice a year. Put your t-shirt on the wrong way round. Leave the keys on the counter when you go to get the car out. That sort of thing. Not once in a blue moon events, but they don't affect you 99.5% of the time. /6

Or consider Risk. You are defending. Your opponent throws three sixes. Oops. One chance in 216. /5

In Monopoly, if you throw doubles (any doubles) three times in a row, you go to jail. One chance in 216 at the start of any turn. Anyone who has played Monopoly more than once has seen this. It's common enough that they made a rule for it, after all. /4

99.5% survival is one chance in 200 of dying. What does 1 chance in 200 look like? Let's start with board games. /3

First, this ignores the other costs of COVID-19 infection, including #LongCovid. But let's pretend that the only outcomes are death or a full recovery. 99.5% is (are?) not odds of survival that most people would take. /2

In a crowded field, my least favourite COVID-19 minimisation troll tactic is the claim that "99.5% of people survive". An angry thread. /1

COVID-19 test positive and died in hospitals in England (up to 2nd Sep) - Under 60 with no pre-existing conditions 305 deaths in a popn of 56 Million.

9) Household studies can be very useful here, where we follow the household contacts of infected vaccinated individuals and infected unvaccinated individuals, and compare how frequently they are infected. Pioneering work from my mentor @betzhallo. http://courses.washington.edu/b578a/readings/PreziosiHalloranVaccine2003.pdf…

10) Finally, vaccine efficacy vs. effectiveness? We like to reserve "efficacy" for estimates from randomized trials, where everyone receives the vaccine as intended (proper cold chain, no missed doses). We distinguish this idealized measure from real-world "effectiveness." END

8) A vaccine that prevents infection entirely provides indirect protection to others. If I can't get infected, I can't infect you. But it is possible to have a vaccine that prevents disease but individuals can still be infectious.

7) So far I have talked about how well a vaccine directly protects the vaccinated individual. Another important type of vaccine effect is the ability of a vaccine to reduce infectiousness to others. This is known as indirect protection, and is related to herd immunity.

6) Preventing infection entirely is the hardest to achieve. And of course a vaccine that prevents infection will also prevent disease and severe disease. But we can have vaccines where people are still infected but their disease severity is lessened.Quote Tweet

5) Most Phase 3 trials are measuring efficacy to prevent disease as the primary analysis, with efficacy against infection and against severe disease as secondary analyses.Quote Tweet

4) Though we talk about vaccine efficacy as a single number, there are actually several different types of vaccine efficacy, such as: - Efficacy to prevent infection (sterilizing immunity) - Efficacy to prevent disease - Efficacy to prevent severe disease

3) Vaccine efficacy of 50% roughly means you have a 50% reduced risk of becoming sick compared to an otherwise similar unvaccinated person. Or you have a 50% chance of becoming sick given that you were exposed to enough infectious virus to make an unvaccinated person sick.

2) Vaccine efficacy (VE) measures the relative reduction in infection/disease for the vaccinated arm versus the unvaccinated arm. A perfect vaccine would eliminate risk entirely, so VE = 1 or 100%. This can be calculated from the risk ratio, incidence rate ratio, or hazard ratio.

VACCINE EFFICACY 101: A biostatistician's primer Ten tweets to cover: - How is vaccine efficacy calculated? - Distinguishing between infection, disease, & severe disease. - Measuring reduced infectiousness. - Vaccine efficacy vs. effectiveness!

This thread is based on our new paper with @JuliaLMarcus Caroline Buckee and @aetiology Accepted in CID - preprint version can be accessed here: (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3692807…)

Environment: Contact pattern also depends on the setting of the encounter. Contact tracing studies suggest an almost 20x higher risk of transmission indoors compared with outdoor environments. (10/n) (https://ft.com/content/2418ff87-1d41-41b5-b638-38f5164a2e94…)

void crowded indoor poorly ventilated environments. Spend more time outdoors. Maintain your distance (more is better but 2 metre is not a panacea). Improve ventilation: open windows/doors. Wear a mask indoors. Wash hands. (27/n) (https://vox.com/science-and-health/2020/5/22/21265180/cdc-coronavirus-surfaces-social-distancing-guidelines-covid-19-risks…)

Policymakers and health experts can help the public differentiate between lower-risk and higher-risk activities and environments and public health messages could convey a spectrum of risk to the public to support engagement in alternatives for safer interaction (26/n)

There are many things that could be done within families to decrease transmission. We need to provide clear instructions, and means of support to enable those with symptoms/positive test and their contacts to isolate. (25/n) https://abc.net.au/news/2020-09-15/coronavirus-swept-through-jos-house.-heres-how-he-dodged-it/12660218

Early viral load peak in the disease course indicates that preventing onward transmission requires immediate self-isolation with symptom onset (for a min of 5 days). Messages should prioritise isolation practices, and policies should include supported isolation. (24/n)

In summary: The disproportionate impact of COVID-19 on households living in poverty, and the racial and ethnic disparities observed in many countries, emphasize the need to urgently update our definition of "vulnerable" populations for COVID-19 & address these inequities. (22/n)

A real overlap in the causes of mortality and deprivation can be seen here. The age-standardised rate of deaths involving COVID-19 in the most deprived quintile was more than double (2.3 times higher) than in the least deprived quintile in Scotland. (21/n) https://nrscotland.gov.uk/files/statistics/covid19/covid-deaths-report-week-19.pdf

Covid-19 could now be endemic in some parts of England that combine severe deprivation, poor housing and large BAME communities, national lockdown in these parts of the north of England had little effect in reducing the level of infections (20/n) (https://theguardian.com/world/2020/sep/05/covid-19-could-be-endemic-in-deprived-parts-of-england//…)

Previous research suggests that although social distancing during the 2009 H1N1 pandemic was effective in reducing infections, this was most pronounced in households w greater socioeconomic advantage. Similar findings are emerging for COVID-19. (19/n) (https://pnas.org/content/117/33/19658//…)

In Madrid, 37 neighbourhoods are seeing the highest incidence, 4 x the Spanish average. Common factors: these areas are poorer, denser and have a high proportion of immigrant population. (18/n) (https://elpais.com/sociedad/2020-09-19/como-son-las-zonas-restringidas-en-madrid-mas-densas-con-mas-inmigrantes-y-sobre-todo-mas-pobres.html…)

PHE surveillance report shows that while the number of infections is increasing mainly in 20-29, 30-39 ages in England, SARS-CoV-2 is spreading most in highly deprived areas - where people are in poorly paid work and can't afford to isolate. (17/n) https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/919676/Weekly_COVID19_Surveillance_Report_week_38_FINAL_UPDATED.pdf

Households in socioeconomically deprived areas are more likely to be overcrowded, increasing the risk of transmission within the household. These disparities also shape the strong geographic heterogeneities observed in the burden of cases and deaths. (16/n)

People in lower-paid occupations are often classified as essential workers who must work outside the home and may travel to work on public transport. These occupations often involve greater social mixing, exposure risk due to prolonged working hours and job insecurity. (15/n)

The largest clusters of cases observed in the USA have all been associated with prisons or jails. In the largest meat packing plant in Germany, while the common point of potential contact was workplace, risk was higher for a single shared apartment, bedroom and carpool. (13/n)

Much worryingly the largest outbreaks from across the world are reported in long term care facilities such as nursing homes, homeless shelters, prisons, and meat-packing plants where many people spend several hours working, living together, and share communal spaces. (12/n)

Prolonged indoor contact in a crowded and poorly ventilated environment increases the risk of transmission substantially. But decreasing occupancy and improving ventilation through opening windows/doors can lower the risk. (11/n)

Environment: Contact pattern also depends on the setting of the encounter. Contact tracing studies suggest an almost 20x higher risk of transmission indoors compared with outdoor environments. (10/n) (https://ft.com/content/2418ff87-1d41-41b5-b638-38f5164a2e94…)

Prolonged indoor contact in a crowded and poorly ventilated environment increases the risk of transmission substantially. But decreasing occupancy and improving ventilation through opening windows/doors can lower the risk. (11/n)

hen we look at the viral load dynamics & contact tracing studies, those who are infected are very infectious for a short window, likely 1-2 days before and 5 days following symptom onset. No transmission documented so far after the first week of symptom onset. (7/n)

Individual factors: Many ppl either do not infect anyone or infect a single person, and a large number of secondary cases are caused by a small # of infected ppl. Although this also is related to other factors, individual variation in infectiousness plays a major role.(6/n)

Have yet to see better summary...

Members of host communities are also part of this experiment, of course. (We should have mentioned this above.) University leaders haven't sought their consent either.

But university leaders, including at @uniofleicester, are neither informing us of the risks, nor seeking our consent to participate in this experiment. (This important point was just made by a member in our emergency general meeting.)

Universities are conducting an experiment, an experiment that involves human beings (university staff and students) and a life-threatening virus. But experimental subjects must give informed consent. (That's basic research ethics.)

Finally, I’d like to correct the ByLine article where it mentions me (in relation to the March claims by Prof Gupta above): I’m not the head of the UCL Institute for Global Health, though I do work there. 8/8

I am a signatory of the other letter: https://twitter.com/martinmckee/status/1308043860652830721?s=20… One the positive side both letters do agree that the NHS should be kept open for all The disagreement is in how much of a risk Covid is and how we deal with it. 7/8

b)It doesn’t acknowledge the risks of long-Covid c)It claims no link between restrictions and mortality 6/8

So I question the scientific judgement of the Sikora-Gupta-Heneghan letter, and probably should just stick to that: a)The letter offers no practical way to separate low and high risk given the reality of multi-generation households, the workforce and society more broadly 5/8

With others I challenged that at the time as dangerous: https://ft.com/content/ebab9fcc-6e8d-11ea-9bca-bf503995cd6f… 4/8

There is relevant history with many of the authors of the letter including Prof Gupta who claimed in March without any evidence that 50% of the UK had already been infected: https://ft.com/content/5ff6469a-6dd8-11ea-89df-41bea055720b… 3/8

I think the issues raised in the article are worth discussing and not comparable with Gates “mind-control vaccines” and am glad that some people have engaged with them. 2/8

Some reflections on this: Firstly, I can see the article uses inflammatory language so apologise for that and any offence caused. They were genuine questions in my Tweet though and I was not expecting to be called deranged for posting it. 1/8

Coronavirus may have infected half of UK population — Oxford study

Below is a 3 group WAIFW with different Pr of connections in each group (by color) and between groups. Essentially WAIFW is a stratified random mixing model

Because I did a bad job of defining WAIFW: it is the Who Acquires Infection From Whom matrix. Rather than random edges between persons, WAIFW generates edges random at \beta_w for connections in the group and \beta_o for outside

Ultimately, the (possibly) unobserved network matters. If your model only assumes some random mixing parameter for broad groups of people, your model is probably way too simple to say anything meaningful about what policies we should actually consider

If your strategy is instead to vaccinate the persons with household-only contacts then you will have to go above the threshold

If your strategy is to vaccinate the between-household contacts, you can be lower than the threshold

Network 4: Household Network This last network is variation on the previous clustered network. It instead places people into households. Depending on the vaccination strategy used, we can go above or below the 'threshold'

This network is closer to how human interactions are actually structured. This is also closer to WAIFW setups (but WAIFW won't capture the power-law for degree within clusters that the above network has)

Network 3: Clustered Network In a clustered network, we can go above the herd immunity threshold but still have outbreaks. The following network has 75% vaccinated but an outbreak would occur

"Paul, all you have shown is that you can effectively go below the threshold. That doesn't negate the whole concept. We might instead say we need *at least* the threshold" Bad news my imaginary interlocutor

Network 2: Mesh Network Well it looks like a big ole net. We can guarantee no transmission with only 50% vaccinated. Less than the supposed threshold

Network 1: Random Mixing When someone talks about a threshold for herd immunity, this is the underlying network of what they are generally talking about (setting aside WAIFW for the moment). The threshold calculation applies normally

In all the following networks, the elements of R_0 are held constant. The network structure will change; but the \beta look the same despite that Our R_0 will be 3, meaning the herd immunity threshold is 0.67 (but everything applies to other R_0 > 1)

To show this, let's talk about a perfect vaccine. If you get this vaccine you are perfectly protected from the infection and thus cannot transmit it (everything also applies to imperfect vaccines but it's messier) Blue circles are vaccinated individuals and red are unvaccinated

where \beta is the effective contact rate, N is the number of individuals, and r is the inverse of the duration The threshold says if are above that level the disease will disappear / we expect no outbreaks of disease. However, that threshold is neither sufficient nor necessary

Herd immunity is a far squishier concept then many seem to be describing in their "shielding" or "stratified herd immunity" plans. Here is the formula for herd immunity threshold for a SIR model

SciBeh is organising a workshop on "Building an online information environment for policy relevant science" Mark the date, Nov. 9/10, 2020, join us, contact us with thoughts and suggestions, and RT!

The U.K. map gets redder as daily cases reach 14000 and R=1.4 . Our data are over double govt figures based solely on testing. Worrying to see London cases accelerating for the first time since April and student mobility not going to help. Keep logging!

RT @AlecStapp: New research finds that fake news comprises "only 0.15% of Americans' daily media diet." Researchers argue that ordinary ne…

some of them are just kids being kids, making stuff up and playing into taboos. but unfortunately deep state conspiracy theories have become so centralized and weaponized, that intentions don’t matter, the content is all one big soup of viral misinformation

Even where the stuff they are sharing is found somewhere else, they jump on it fast. I think I first saw the topographical map sea floor Epstein tunnels conspiracy on it, b/c some young conspiracy entrepreneur was just on top of that.

One thing to put things in perspective on this -- I dig through a lot of conspiracy professionally. It's hard to introduce me to new ones. But I flipped through tiktok for an hour (w/ a little bit of algo training via bookmarking) and was finding stuff I'd never seen.

Amidst many college outbreaks are a slew of very successful schools. My employer, @Yale, is among them: I hope they stay that way. Congrats to the students, faculty, staff, & our community for working together to achieve ZERO positive results in last 7 days. (9,425 tests).

Amidst many college outbreaks are a slew of very successful schools. My employer, @Yale, is among them: I hope they stay that way. Congrats to the students, faculty, staff, & our community for working together to achieve ZERO positive results in last 7 days. (9,425 tests).

Diagnosis of COVID at admission is made by clinicians, now experienced in what the disease looks like. PCR tests are used to support (or otherwise) their clinical diagnosis. The rise is new cases coming into hospital is very real and being seen across the country.

This needs to be accounted for in some reporting figures, particularly when patients are readmitted to hospital (see , for example, our piece on this from June http://bit.ly/3hSmlvc). But, this is NOT the reason for increasing cases or admissions. 2/3

There's a fair bit of misinformation doing the rounds today. One relates to a clear misunderstanding of PCR testing and false positives. It is true that some of those with COIVD can have positive PCR tests for weeks, sometimes at low levels, long after they're better. 1/3

older generations spreading conspiracy theories and misinformation on facebook has been reported for years. what people don’t realize is the same issues persist with kids today on tiktok, despite the widespread narrative that they’re immune to fake news from growing up online

i wrote a longform article about how conspiracy culture became so viral today. no demographic is immune. we all have a little conspiratorial thinking in us—that’s what makes ideological cults about the “deep state” so appealing and terrifying

gen z will make a conspiracy theory out of ANYTHING

the state of conspiracy theories on tiktok cannot be understated. these kids believe ANYTHING and spread misinformation like wildfire

Without a deeper understanding of the connection between contradiction and truth, and some basic appreciation of reasonable discourse rules, we cannot possibly have meaningful critical exchange between scientists and general public. and that won't happen over night... 3/3

and, in fact, seem often to view the fact that an exchange about their views has backed them into a contradiction as indication that one is just "interested in winning the argument" as opposed to getting at truth. 2/3

having spent a few days looking at 'debate' about COVID policy on lay twitter (not the conspiracy stuff, just the "we should all be Sweden" discussions), the single most jarring (and worrying) thing I noticed is that posters seem completely undeterred by self contradiction 1/3

Because Science is a horrible journal that doesn't even let you read the abstract, here it is.

No Imperial model points to 85,000 deaths in Sweden. This cites work by other researchers. Imperial had no role in developing this model, its assumptions or its parameters.

Imperial’s COVID-19 Response Team reports and tools, including models, can be accessed on our website

We’ve dealt with similar misleading claims before

Neil Ferguson and Imperial did not produce a model for Sweden pointing to 85,000 deaths

The pandemic proves we all should know ‘psychological first aid.’ Here are the basics. /lifestyle/wellness/pandemic-psychological-first-aid-anxiety/2020/09/21/7c68d746-fc23-11ea-9ceb-061d646d9c67_story.html?tid=ss_tw

Additional note on French data. The challenge (and one that's always an issue in real time) is that the lags resulting in updates to earlier data also mean that recent numbers may be revised upwards in future.

Quick clarification (thanks for flagging @marbin2050): the report of 239 deaths in Spain was from a day earlier (https://english.elpais.com/society/2020-09-17/spain-reports-239-new-coronavirus-deaths-the-highest-figure-so-far-during-the-second-wave-of-the-epidemic.html…) – 162 were reported yesterday (https://english.elpais.com/society/2020-09-18/spain-reports-11291-new-coronavirus-cases-and-162-deaths.html…)

I presume model is based on this recent paper, which predicted a second peak at ~30 deaths per day (https://medrxiv.org/content/10.1101/2020.09.01.20185876v1…). For context, there were 27 COVID deaths reported in UK yesterday: https://coronavirus.data.gov.uk/deaths

Spain reported 239 COVID deaths yesterday & France 154. So seems odd for this model to predict that if UK continues on same trajectory, then adds some not-particularly-good TTI in 8 weeks, deaths will peak at ~50 per day (unless I've misunderstood plot...)