Suddenly the CDC admits that 75% of virus deaths were people with 4 or more comorbidities. Seems the “science” now sees the looming 2022 midterm election consequences for lockdown Dems…

Here is @CortesSteve showing that he did not read the article.

BREAKING: CDC Director admits that over 75% of COVID deaths are in people with ‘at least 4 comorbidities.’

here is @jennybethm showing that she did not read the article.

Researchers found a 'striking similarity' between neurological impacts of COVID and the cancer treatment side effect known as 'chemo brain'

Even Mild COVID Infections Can Have Lasting Impacts Like ‘Chemo Brain,' Study Finds

You stupid idiots. You stand in the way of public health. You peabrains do not understand there is no virtue in Omicron-based immunity and mutagenesis. One mutation from Histidine could spell natural disaster you stupid frauds. You are brainless.

T cell immunologist getting very upset at people arguing that high levels of transmission are a good thing

https://travellingtabby.com/scotland-coronavirus-tracker/… Today is the first time in almost three weeks that the positivity rate has been under 20%! Although it is also the first time in about 11 months that we've had over 1,500 people in hospital with the virus. #covid19scotland #DailyCovidUpdate

THIS SURVEY EXPLORES CANADIANS’ AND AMERICANS’ PERSPECTIVES ON THE SPREAD OF THE OMICRON VARIANT AND VACCINE EFFECTIVENESS.

The Spread of the Omicron Variant and Vaccine Effectiveness – January 11, 2022

Refusing the COVID-19 vaccine comes with a price

The 'health contribution' announced by Premier Legault would become the first such tax in Canada

Quebec plans to hit unvaccinated with a 'significant' tax

10.31234/osf.io/v6fc9

Misinformation on social media is a key challenge to effective and timely public health responses. Existing mitigation measures include flagging misinformation or providing links to correct information but have not yet targeted social processes. Here, we examine whether providing balanced social reference cues in addition to flagging misinformation leads to reductions in sharing behavior. In 3 field experiments (N=817, N=322, and N=278) on Twitter, we show that highlighting which content others within the personal network share and, more importantly, not share combined with misinformation flags significantly and meaningfully reduces the amount of misinformation shared (Study 1-3). We show that this reduction is driven by change in injunctive social norms (Study 2) but not social identity (Study 3). Social reference cues, combined with misinformation flags, are feasible and scalable means to effectively curb sharing misinformation on social media.

Social reference cues can reduce misinformation sharing behaviour on social media

Having an incidence of >4000/100.000/14 days (Geneva) is scary & just unbelievable - every day I learn of several friends & colleagues that are positive or were exposed. I wonder how long our household will manage to stay #COVID19 #SARSCoV2 free...

But But But Sweden......... @afneil maybe Tegnall was wrong eh mate

Supermarkets say they are experiencing delivery delays, not panic buyingUp to half of logistics companies' workers are off on any given dayThe truckers' union wants the federal government to provide rapid tests to road transport workers

Supermarkets short of supply as up to half of truck drivers absent due to COVID

But looking at the wider UK context, Derry is currently looking at 4.5% of everyone *in the entire county* testing positive for COVID in the past week, which is absolutely bonkers. South Wales, the Scottish Central Belt and NW England not too far behind.

Fantastic reassurance for parents. Australian kids it’s time for your jab!!!!Quote TweetEric Topol@EricTopol · 5 JanRemarkably low rate of myocarditis among children mRNA (Pfizer) vaccinated age 5-11 Total of 12 cases among 8.7 million doses given https://cdc.gov/vaccines/acip/meetings/downloads/slides-2022-01-05/02-COVID-Su-508.pdf… Rate of 4/million in boys is <1/10th the rate in teens

10.31234/osf.io/fjqpb

Background: The COVID-19 pandemic might affect mental health. Data from population-representative panel surveys with multiple waves including pre-COVID data investigating risk and protective factors are still rare. Methods: In a stratified random sample of the German household population (n=6,684), we conducted survey-weighted multiple linear regressions to determine the association of various psychological risk and protective factors with changes in psychological distress (PD; measured via PHQ-4) from pre-pandemic (average of 2016 and 2019) to peri-pandemic (both 2020 and 2021) time points. Control analyses on PD change between two pre-pandemic time points (2016 and 2019) were conducted. Regularized regressions were computed to inform on which factors were statistically most influential in the multicollinear setting. Results: PHQ-4 in 2020 (M=2.45) and 2021 (M=2.21) was elevated compared to 2019 (M=1.79). Several risk factors (catastrophizing, neuroticism, asking for instrumental support) and protective factors (perceived stress recovery, positive reappraisal, optimism) were identified for the peri-pandemic outcomes. Control analyses revealed that in pre-pandemic times, neuroticism and optimism were predominantly related to PD changes. Regularized regression mostly confirmed the results and highlighted perceived stress recovery as most consistent influential protective factor across peri-pandemic outcomes. Conclusions: We identified several psychological risk and protective factors related to PD outcomes during the COVID-19 pandemic. Comparison to pre-pandemic data stress the relevance of longitudinal assessments to potentially reconcile contradictory findings. Implications and suggestions for targeted prevention and intervention programs during highly stressful times such as pandemics are discussed.

Coping with COVID: Risk and Resilience Factors for Mental Health in a German Representative Panel Study

10.31234/osf.io/28yc5

Science communication is changing. It is increasingly directed not only at peers but at the public in general. Accordingly, understanding the circumstances under which audience members engage with scientific content is crucial to improving science communication. In this article, we investigate the role of affect on audience engagement with a modern form of science communication: TED talks. We examined how affect valence---a net positive or negative affect---and density---the proportion of affective words---are associated with a talk's popularity---reflecting views and likes---and polarity---reflecting dislikes and comments. We found that the valence of TED talks was associated with both popularity and polarity, with positive valence being linked to higher talk popularity and lower talk polarity. Density, on the other hand, was only associated with popularity, with higher affective density being linked to higher popularity---even more so than valence---but not polarity. Moreover, we observed that the association between affect and engagement was partially moderated by talk topic. Specifically, whereas higher density was related to higher popularity across most topics, valence seemed to particularly impact the popularity and polarity of TED talks on social topics, which regularly discuss polarizing issues such as race or political conflicts. We discuss implications of our findings for increasing the effectiveness of science communication.

Affect in science communication: A data-driven analysis of TED talks

10.31234/osf.io/mg7dn

For computationally limited agents such as humans, perfectly rational decision-making is almost always out of reach. Instead, people may rely on computationally frugal heuristics that usually yield good outcomes. Although previous research has identified many such heuristics, discovering good heuristics and predicting when they will be used remains challenging. Here, we present a machine learning method that identifies the best heuristics to use in any given situation. To demonstrate the generalizability and accuracy of our method, we compare the strategies it discovers against those used by people across a wide range of multi-alternative risky choice environments in a behavioral experiment that is an order of magnitude larger than any previous experiments of its type. Our method rediscovered known heuristics, identifying them as rational strategies for specific environments, and discovered novel heuristics that had been previously overlooked. Our results show that people adapt their decision strategies to the structure of the environment and generally make good use of their limited cognitive resources, although they tend to collect too little information and their strategy choices do not always fully exploit the structure of the environment.

Discovering Rational Heuristics for Risky Choice

Cities are typical dynamic complex systems that connect people and facilitate interactions. Revealing universal collective patterns behind spatio-temporal interactions between residents is crucial for various urban studies, of which we are still lacking a comprehensive understanding. Massive cellphone data enable us to construct interaction networks based on spatio-temporal co-occurrence of individuals. The rank-size distributions of hourly dynamic population of locations are stable, although people are almost constantly moving in cities and hotspots that attract people are changing over time in a day. A larger city is of a stronger heterogeneity as indicated by a larger scaling exponent. After aggregating spatio-temporal interaction networks over consecutive time windows, we reveal a switching behavior of cities between two states. During the "active" state, the whole city is concentrated in fewer larger communities; while in the "sleeping" state, people are scattered in more smaller communities. Above discoveries are universal over diversified cities across continents. In addition, a city sleeps less, when its population grows larger. And spatio-temporal interaction segregation can be well approximated by residential segregation in smaller cities, but not in larger ones. We propose a temporal-population-weighted-opportunity model by integrating time-dependent departure probability to make dynamic predictions on human mobility, which can reasonably well explain observed patterns of spatio-temporal interactions in cities.

Revealing spatio-temporal interaction patterns behind complex cities

Indeed.

R-value for Omicron across England is currently 5.5 (C.I 4.7-6.4) Doubling time 2.04 days (CI 1.87 - 2.23) 22% of TaqPath processed specimens from 11th December are SGTF

COVID rates are heading even higher - the last three days in Lambeth are equivalent to a weekly rate of 2.2% The kinds of numbers we've normally only seen in small areas (MSOAs) with a University residence or a prison But it's the whole Borough https://archive.uea.ac.uk/~e130/Latest.html

10.1101/2021.12.14.21267755

Recent surveillance has revealed the emergence of the SARS-CoV-2 Omicron variant (BA.1/B.1.1.529) harboring up to 36 mutations in spike protein, the target of vaccine-induced neutralizing antibodies. Given its potential to escape vaccine-induced humoral immunity, we measured neutralization potency of sera from 88 mRNA-1273, 111 BNT162b, and 40 Ad26.COV2.S vaccine recipients against wild type, Delta, and Omicron SARS-CoV-2 pseudoviruses. We included individuals that were vaccinated recently (<3 months), distantly (6-12 months), or recently boosted, and accounted for prior SARS-CoV-2 infection. Remarkably, neutralization of Omicron was undetectable in most vaccinated individuals. However, individuals boosted with mRNA vaccines exhibited potent neutralization of Omicron only 4-6-fold lower than wild type, suggesting that boosters enhance the cross-reactivity of neutralizing antibody responses. In addition, we find Omicron pseudovirus is more infectious than any other variant tested. Overall, this study highlights the importance of boosters to broaden neutralizing antibody responses against highly divergent SARS-CoV-2 variants.

mRNA-based COVID-19 vaccine boosters induce neutralizing immunity against SARS-CoV-2 Omicron variant

England’s school inspectorate finds effect on attendance and mental health higher among disadvantaged

Persistent Covid-related absence leaves pupils lagging – Ofsted

10.31234/osf.io/e5bn7

Just how big of a difference will deliberated thinking make in the digital age when judging whether the headline of a digital article is true or fake? Misinformation plagued the Chinese internet space, and fake news, especially related to health tips, often went viral on the internet with rapid speed. A previous study 1 was previously conducted on political articles measuring the influence of partisanship on thinking deliberately. In this paper, we conducted a study on how deliberation influenced the accuracy of Chinese netizens distinguishing real and fake news headlines, using a similar experiment procedure from the above mentioned study. We found that deliberation reduces the possibility of these readers being misguided by fake health-related headlines. A similar trend of accuracy was observed when participants thought deliberately compared to the original study, despite using different topics on a different population of participants.

Deliberation in health-related headlines

Researchers are rapidly sequencing the genomes of virus samples worldwide, but shortcomings in the global surveillance system make the task a challenge.

Omicron blindspots: why it’s hard to track coronavirus variants

10.1136/bmj.n2862

What’s the vaccine uptake in pregnancy?Some 80 000 pregnant women in England had received two doses of the covid-19 vaccine up to 31 October, up from 65 000 at the end of August, says the UK Health Security Agency.1 It’s not possible to say what proportion this is of all pregnant women, as England doesn’t collect data linking vaccinations, pregnancies, and births. But data from Public Health Scotland2 showed that only 15% (615/4069) of women who gave birth in August 2021 were fully vaccinated. Only 23% (165/704) of women aged 35-39 who delivered their baby in August 2021 had received two vaccine doses, compared with 71% of all adults aged 30-39 in the general population.

Covid-19 and pregnancy: vaccine hesitancy and how to overcome it

If you have any doubts about the benefits of #COVIDVaccination, than please look at the graph below on patients admitted to intensive care unit with #COVID19 #VaccinesSaveLives https://icnarc.org/our-audit/audits/cmp/reports… @fascinatorfun

Another view of rapid sequence evolution, here in the S1 domain of the spike protein.

Quite a long branch here leading to B.1.1.529 / 21K / Omicron. Lots of (probably within-host?) evolutionary change along this branch.

The bad news, of course, is that B.1.1.529 is increasing in South Africa at a time when Delta has been is decreasing. *If* the turn-around is due to increased transmissibility, instead of other e.g. behavior factors, it's going to a rough winter. Obviously we'll know more soon.

In the same thread, John provided this graph as well. There were far more cases of previous strains around when Alpha and Delta took off, so they had further to go, so to speak, to reach high prevalence.

I'd be careful not to overinterpret the following graph from @jburnmurdoch. Yes, the fraction of B.1.1.529 is increasing faster. But I think that is that is largely due to different denominators.

Given everything we know at this stage, I'd be very surprised if the current mRNA vaccines did not continue to offer strong protection against severe disease and death from B.1.1.529. I'm more concerned about it sweeping through areas that have not been able to acquire vaccines.54230970

While some degree of immune escape is possible, and the presence of the E484K mutation is suggestive of change in that direction, immune escape alone cannot explain the rapid the increase in prevalence. If that's not some of founder effect, it must have higher transmissibility.

People are asking what I think about the B.1.1.529 variant that is rapidly increasing in frequency in South Africa. It certainly merits close monitoring. That said: I believe the hockey-stick graphs from yesterday are in error. We are nowhere near the delta peak prevalence.205461.6K

OMICRON VARIANT CONFIRMED IN NSW CASES

10.1038/s41586-021-04280-x

Immunological memory is a hallmark of adaptive immunity and facilitates an accelerated and enhanced immune response upon re-infection with the same pathogen1,2. Since the outbreak of the ongoing coronavirus disease 19 (COVID-19) pandemic, a key question has focused on which severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific T cells stimulated during acute infection give rise to long-lived memory T cells3. Using spectral flow cytometry combined with cellular indexing of transcriptomes and T cell receptor (TCR) sequencing we longitudinally characterize individual SARS-CoV-2-specific CD8+ T cells of COVID-19 patients from acute infection to one year into recovery and find a distinct signature identifying long-lived memory CD8+ T cells. SARS-CoV-2-specific memory CD8+ T cells persisting one year after acute infection express CD45RA, interleukin-7 receptor α (CD127), and T cell factor-1 (TCF1), but they maintain low CCR7, thus resembling CD45RA+ effector-memory T (TEMRA) cells. Tracking individual clones of SARS-CoV-2-specific CD8+ T cells, we reveal that an interferon signature marks clones giving rise to long-lived cells, whereas prolonged proliferation and mammalian target of rapamycin (mTOR) signaling are associated with clonal disappearance from the blood. Collectively, we describe a transcriptional signature that marks long-lived, circulating human memory CD8+ T cells following an acute virus infection.

Signature of long-lived memory CD8+ T cells in acute SARS-CoV-2 infection

10.1056/NEJMoa2115624

BackgroundThe emergence of the B.1.617.2 (delta) variant of severe acute respiratory syndrome coronavirus 2 and the reduced effectiveness over time of the BNT162b2 vaccine (Pfizer–BioNTech) led to a resurgence of coronavirus disease 2019 (Covid-19) cases in populations that had been vaccinated early. On July 30, 2021, the Israeli Ministry of Health approved the use of a third dose of BNT162b2 (booster) to cope with this resurgence. Evidence regarding the effectiveness of the booster in lowering mortality due to Covid-19 is still needed. MethodsWe obtained data for all members of Clalit Health Services who were 50 years of age or older at the start of the study and had received two doses of BNT162b2 at least 5 months earlier. The mortality due to Covid-19 among participants who received the booster during the study period (booster group) was compared with that among participants who did not receive the booster (nonbooster group). A Cox proportional-hazards regression model with time-dependent covariates was used to estimate the association of booster status with death due to Covid-19, with adjustment for sociodemographic factors and coexisting conditions. ResultsA total of 843,208 participants met the eligibility criteria, of whom 758,118 (90%) received the booster during the 54-day study period. Death due to Covid-19 occurred in 65 participants in the booster group (0.16 per 100,000 persons per day) and in 137 participants in the nonbooster group (2.98 per 100,000 persons per day). The adjusted hazard ratio for death due to Covid-19 in the booster group, as compared with the nonbooster group, was 0.10 (95% confidence interval, 0.07 to 0.14; P<0.001). ConclusionsParticipants who received a booster at least 5 months after a second dose of BNT162b2 had 90% lower mortality due to Covid-19 than participants who did not receive a booster.

BNT162b2 Vaccine Booster and Mortality Due to Covid-19

New measures to tackle Omicron are weeks late – while the UK is lagging on vaccinating children and a balanced approach to travel

Plan B shows Britain is still chasing Covid. Here’s how we can get ahead of it

Estimates of COVID-19 vaccine uptake in the US based on large surveys that are used to guide policy-making decisions tend to overestimate the number of vaccinated individuals, according to research published in @Nature. https://go.nature.com/3EBQPOh

This means that a more transmissible variant with no difference in immune escape properties *or* a variant with no difference in transmissibility, but with more immune escape properties could explain available evidence. 30/

... all of this is also ignoring other factors that influence Rt, including contact patterns, duration of infectiousness, NPI usage, etc. 29/

Here lies the problem, because while we can reasonably estimate Rt, we don't know whether an increase is due to R0 being higher (e.g., increased transmission) or x being higher (e.g., increased immune evasion). 28/

Immunity will lower the fraction of susceptibles (x), leading to lower Rt. A more transmissible variant will increase R0, increasing Rt. But, Rt will also increase if a variant is capable of eroding immunity in the population - R0 stays the same, but x is now higher. 27/

This is the difference between estimating R0 (the basic reproductive number) vs Rt (the effective reproductive number, which is Rt = R0*x, where x = fraction of susceptible). https://healthknowledge.org.uk/public-health-textbook/research-methods/1a-epidemiology/epidemic-theory… 26/

The reason why we can't yet distinguish the two, is because a scenario in which Omicron has no immune escape, but is more transmissible can explain what we're observing. The same for a very 'escapy' VOC with the same (or lower) transmission. https://twitter.com/trvrb/status/1465364300936085506?s=20… 25/

Here's where it gets sketchy - because we really don't have any good data to make any firm conclusions on what/which factor(s) we think contribute the most. Remember Oli's advice: 24/

Combine the rapid rise of Omicron with the observation from genetic data that it appears to be relatively young, what best explains that? 1. Increased immune evasion 2. Increased transmission 3. Combination of both 23/

3. Cases are rising rapidly in places where Omicron seems to be dominating https://twitter.com/Tuliodna/status/1463911554538160130?s=20… 22/Quote Tweet

2. It's been observed in travel associated cases originating from multiple locations, including a single flight with ~10% of passengers being positive for COVID-19 (caveat - not yet clear if they were all Omicron): https://washingtonpost.com/world/2021/11/27/amsterdam-omicron-covid-variant-lockdown/… 21/

How did Omicron become widespread so quickly? We believe Omicron has increased rapidly in frequency for a few reasons: 1. It appears to be displacing Delta in South Africa .. https://youtube.com/watch?v=Vh4XMueP1zQ&t=5s… 20/

Many articles have already concluded that Omicron came from an immunocompromised person ("HIV" is often mentioned), but I disagree with that conclusion. While that's certainly possible, we don't have any data showing that's the case. Let's keep all hypotheses open. 19/

3. Several of the mutations in Omicron have been observed in animals, including rodents: https://virological.org/t/mutations-arising-in-sars-cov-2-spike-on-sustained-human-to-human-transmission-and-human-to-animal-passage/578/11… 18/

I slightly favor reverse zoonosis for a few reasons: 1. The lineage is old and undetected circulation in immunocompromised patient(s) for this long seems unlikely 2. SARS-CoV-2 is a generalist virus and we have seen human>animal>human transmission happen in e.g., mink .. 17/13111450

I don't believe #1 is likely, leaving evolution in immunocompromised patient(s) or a reverse zoonosis, followed by a new zoonosis (human>animal>human) as the two hypotheses I find most plausible - although I have no confidence in either. 16/

We believe (a) the lineage leading to Omicron branched off a long time ago, (b) Omicron is young, but (c) is already widespread in parts of Africa. So what led to Omicron? Three main hypotheses: 1. Undetected circulation 2. Immunocompromised patient(s) 3. Animal reservoir 15/

For how long has Omicron been circulating in humans? We can estimate that based on the diversity in sampled genomes and most estimates land ~mid October (with wide uncertainty), so we believe it's relatively young. Good thread from @trvrb here: https://twitter.com/trvrb/status/1464353224417325066?s=20… 14/

It's clear that the lineage leading to Omicron is old - possibly mid-2020, but there's a huge amount of uncertainty in exactly when and where. We also don't know from what basal lineage this branched off and convergent evolution makes this tricky. https://nextstrain.org/groups/neherlab/ncov/21K.Omicron… 13/

Emergence and reservoir So where did Omicron come from? And when? We don't know... But let's take a look at some of the data and hypotheses. When it comes to age, we can answer two primary questions: 1. When did the lineage brach off? 2. How old is Omicron itself? 12/

Omicron also has two indels around position 631 that look to be the result of copy-choice recombination, potentially with host, not virus, origin. Recently described in this excellent study: https://virological.org/t/putative-host-origins-of-rna-insertions-in-sars-cov-2-genomes/761… 11/

Compare the mutations seen in Omicron vs those in Delta - it's a big difference: https://twitter.com/COGUK_ME/status/1465455990204252160?s=20… 10/Quote Tweet

What is clear though, is that many of these mutations are on the surface of the spike, where, for example, they may be involved in: - Optimization to human ACE2? - Optimization to non-human ACE2? - Alternative/additional receptor? - Immune evasion? 9/

So what are these mutations doing? We don't know - and remember, what really matters here is the combination of mutations and not any single mutation on its own. Good article here: https://nytimes.com/2021/11/29/health/omicron-covid-mutation-epistasis.html… 8/

Many of the mutations have been seen before in other VOCs, but others have not - and some are 'private' to Omicron (not seen in the larger SARSr-CoV phylogeny). Interestingly, many of the mutations are basic R or K with a lot of N>K (pos: 440, 478, 547, 679, 764, 856, 969): 7/

The recently identified BANAL-52 virus only has ~20 differences to Hu-1 (16 AA substitutions + no furin cleavage site). In nucleotide space, however, that switches - 61 for Omicron vs 206 for BANAL-52. Why the difference? Selection: https://twitter.com/sergeilkp/status/1465134289603858435?s=20… 6/

Mutation profile Omicron has a lot of mutations - more so than previous VOCs. In the spike it has ~40 differences to the original Hu-1 virus (33 AA substitutions, 3 deletions, and 1 insertion). You can compare Omicron to other VOCs here: https://outbreak.info/compare-lineages… 5/

.. and read this great article from @kakape on some of the early observations. https://science.org/content/article/patience-crucial-why-we-won-t-know-weeks-how-dangerous-omicron… 4/

Before we start, please heed this advice from @EvolveDotZoo because it really is important to understand - there are so many different scenarios that can explain what we're observing. So we can discuss "possible" scenarios, but not assign meaningful likelihoods yet. 3/

Gut feeling: Timing: recent, deep roots (high confidence) Emergence: animals (very low confidence) Immune escape: significant (medium-high confidence) Transmission: increased (low confidence) Virulence: similar (low confidence) Details

The key questions I'll address in descending order of confidence in currently available evidence: 1. Mutation profile 2. Emergence 3. Immune escape properties 4. Transmission fitness 5. Virulence 2/

Omicron - on a scale from 1-10, how bad is this going to be? This one's a weirdo, so I'm a 3, a 10, or anything in-between. A thread below with my take on some of the key questions. https://outbreak.info/situation-reports/omicron… 1/

Covid conspiracy theories born in the U.S. are having a deadly impact around the world. @BrandyZadrozny takes us to Romania. Watch NOW: http://nbcnews.com/NOW

Overall, this study has little value at the current time except confirming the ONS data are robust which of course suggests death rates have increased in children- are much higher than other childhood illnesses, & will increase further unless action is taken.734163

Of course the study doesn't look at the most common impacts on children which are long COVID related persistent symptoms (increasing rapidly over time), impact from being orphaned, living with someone with long COVID, and educational disruption from lack of mitigations.

Just for comparison, 30 children died because of flu in 2019 (no lockdowns or mitigations)- comparatively we've already seen many more deaths with COVID-19, despite 3 lockdowns - more recently as mitigations were removed. https://ons.gov.uk/aboutus/transparencyandgovernance/freedomofinformationfoi/deathsfrominfluenzaonlyin2019and2020intheuk

And of course children could get infected again and again, so this isn't even necessarily an upper bound estimate of what could happen. So the infection fatality rate of 5 per 100,000 may seem small, but it's not that small when you allow almost all kids to get infected.

Anyone re-assured that so far the death rate in kids has been 6/million *population* (current ONS) with lots of potential to rise? If all children were infected this would be 700 deaths as per the papers IFR estimate (even Whitty said most children will inevitably get infected)

Indeed, if you look at deaths from COVID-19 based on the latest ONS data this would be ~3 times higher, even with outdated data. They then compare with *all other causes of deaths*, as if this is in any way a meaningful comparison- all with population denominators!

What I find most egregious and misleading in the paper is putting down SARS-CoV-2 deaths with a population denominator - suggesting the rate is 2/million. This is wholly incorrect, because the no. of deaths depend on exposure, especially in a pandemic & this number isn't constant

Obesity and trauma are also included. For PIMS-TS- which is likely to have been underestimated, 2 out of 3 children were deemed not to have an 'underlying condition'