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So no you still not acknowledging that these technologies are unrelated and therefore carry different risks. So there appears no balls for a meaningful discussion here. Basically it is the same as trying to bin the risks of nuclear power to combustion engines. Also your desire to downplay the impact of b the disease is nothing short of ridiculous. Some of the worst flu seasons in the US killed about 60k folks, which was a huge deal for the medical community. COVID-19 killed 500k. It is more than obvious that the mortality rate means little if everyone is susceptible.

I think that is the crux of the matter. If everyone would make perfect decisions, it would not matter on which level things are done. But whether a centralized or decentralized approach works better depends on the likelihood of bad decisions along the way (and their respective impact) as well as structural limitations, such as e.g. level of coordination required. For disease outbreaks decentralized responses tend to be result in worse outcomes as a lot of coordination is required, ranging from building supply chains for e.g. testing and PPEs, travel restrictions (including between provinces) contact tracing and so on. Now, the big question here is whether that also applies to minimum wage.

It is going to be off-topic a bit, but I think COVID-19 is not a good case. While you could state that a decentralized approach has been good for Nova Scotia, the precise opposite can be said about, say Quebec. An actual comparison would need to be done between countries with decentralized vs centralized approaches and recent reviews from Canadian researchers (e.g. Hansen and Amelie, 2020 JMIR Pub Health Surveill). Showed that the decentralized Canadian approach resulted in worse outcomes compared to countries with a stronger, centralized strategy, even if they had worse infrastructure and other issues to deal with.

For starters, an mRNA vaccine has no relation with CRISPR/CAS gene editing strategies. The former is a simple encapsulated mRNA which neither replicates nor can it be integrated into the genome. In fact, one of the main challenges of developing a vaccine was to keep the mRNA stable enough. So, considering that the vaccine has no relationship with the rest of your argument, is there something else you might want to discuss?

I think the concerns are mostly in the area of contact sports and related injury risk, but again much of it is based on a certain amount of extrapolation (a Swedish study showing that testosterone reduction does only lead to minor muscle mass loss, for example) rather than actual performance data. I think it is one of the (many) areas that would benefit from more precise data. But in general I think the whole way sports are set up are to a large degree arbitrary, anyway. For example in basketball taller players have large benefits, so one could at least in theory argue that to ensure fairness, there should be a height cap. And obviously there are no principles that would prohibit any arbitrary number or set of rules. Edit: Also, the scope of the issue might actually be fairly small so there is also the question whether the concern is actually balanced well with the real-life situation.

That is a good point.

I think there is no biological answers to that. We create gender separated leagues for a variety reasons and the decision would likely be based on what aspect one would like to emphasize.

From what I remember testosterone levels of transgender women are the same as as other female ranges after sex reassignment procedures. One could extend the question also to individuals who are intersex, for example. I am not sure whether there are clear answers to the issue, though. It also does not help that data is sparse, as there are only few elite transgender athletes. Depending on the sport some studies found little difference in performance (non-elite long-distancing running, IIRC). But it is unclear (beside anecdotal evidence) how big the effect would be in the elite athletic scene.

They generally are partially targeted as they are formulated to be preferentially taken up by phagocytotic cells. I am not really sure about tropism data of the current vaccines. I assume you are thinking of the risk of presentation of other cell types via MHC-I among non-professional antigen presenting cells? One of the original fears of mRNA vaccines is that they may have broader tropism resulting in widespread MHC-I presentation leading to severe inflammation. So far, no widespread tissue damage has not been reported, which indicate that the effects are likely more localized and targeted Presentation of antigens is a bit tricky, but fundamentally bits and pieces of proteins can be presented by many cell types via the MHC system. In non-professional antigen presenting cell the main mechanism would be via MHC-I, which allows presentation of peptides (i.e. bits and pieces of proteins) originating from the cytosol. So if such a cell would produce the protein and it gets presented, it could interact with CD8+ T cells likely resulting in target destruction. Attenuated vaccines often result in presentation of both MHC-I and II pathways, for example. With regard to protein turn-over, it is a bit difficult to tell. Synthesized the proteins are broken down and typically only fragments end up being presented (turnover rate of intact proteins in general covers a huge range depending on protein and cell type from hours to months in extreme cases). Those parts can remain around for a while but not necessarily in the same cell, as they may be released or broken down and taken up by other cells (the phagocytic pathway being the main mechanism of MHC-II presentation). It cannot be regenerated as once the mRNA is broken down, no new protein is going to be produced. However I think I have seen data suggesting that it may be around for a while, but cannot recall the paper (most I can think of focus on the duration of the T cell induction, which is related).

Primary targets are dendritic cells, which present antigens to T and B cells. While they can be killed on occasion, they typically have set lifespan which in part is coordinated via apoptosis. It should also be noted that mRNA are short-lived, even if they are presented to non-target cells their effects are limited. Only specific cells present antigens on their surface, in most other cases if you introduce RNA they might produce a protein, but it is often also degraded rather quickly by the proteasome.

There is a lot of lit out there, and I am not sure what the latest info is. However, I *think* that most recent life cycle assessment (i.e. from production until end of life) seem still to favour electric vehicles, especially if the electricity is produced using with low carbon emission. However there are are also studies with different conclusion, and the difference is down to which assumptions are being made. For example, if batteries are made in countries where electricity is mostly generated from coal, then the lifetime emission of electrical cars is at least close to hybrids. Conversely, if manufactured in areas with a high proportion of low carbon electricity, it would be much lower. Also, the longer electrical cars operate, the lower their lifetime footprint becomes over their fossil fuel counterparts. While a new electric car has a higher carbon footprint for production, estimates indicate that a low-capacity EV would reduce carbon emission compared to combustion engines after about 2 years of operations (again depending on how electricity is being produced). So if there is an overall strategy to decarbonize electrical production, it appears that the overall carbon footprint, even including the initially higher carbon cost eventually pays off. Conversely, if electricity is produced mostly via coal and electrical cars are rapidly replaced by new models (within 4 years) then lifecycle emission of EVs may be similar or worse than combustion cars. In other words, the overall decarbonization strategy is a key element in deciding what system is ultimately better.

The French famously make fun of Québécois for their outdated French. I am not sure how the relationship in reverse works.

Yes, my apologies, I tend to post without re-reading especially when I am supposed to do something else. I have added a sentence to also make it slightly clearer what I mean.

I think despite the dramatic ongoing situation, folks are still unaware of how the issues are interconnected. There is even a name for this interaction called the one-health framework. Moreover, even as we go through the pandemic, folks are already actively ignoring or trying to (knowingly or not) misdirect and obfuscate the issue. Folks have been saying for a long time now that pandemics are driven by our intrusion into wildlife, that many of our practices (e.g. state fairs, industrial meat production and so on) are risk factors. Yet they get soundly ignored and the only thing that occasionally gets traction is if one highlights the failures elsewhere. But that obviously only distracts from the need to actually do something locally (just take a look at some of the comments and discussions on this forum). Thus, folks assume that the risk of pandemic is something foreign, which also nicely feeds into the beliefs of xenophobic crowds ("they" bring diseases). The depressing result is then the utter lack of preparedness. If even in the middle of the crisis folks refuse to learn the situation, I have little hope for it to have any long-term effects. My assumption is that there will be some investment now with the pandemic still being fresh but within a few years we will basically be at a level of preparedness as before. If the next pandemic or major outbreak happens somewhat in the future, folks will be caught complete unaware, and we will basically revisit all discussions we already had, probably with few if any lessons learned.

Everything on Earth, including fishes, is covered by microbes. Ergo, it is the planet of microbes. The post does fall into the "not even wrong" category, though.

An interesting bit is that a single dose of either mRNA vaccine in patients who had an infection longer than 6 months ago resulted in a rapid increase of anti-spike protein IgG similar to two dosages of vaccinations. Closer on-topic, it seems that the question is fundamentally whether there truly is asymptomatic transmission. There are also apparently some things that are potentially unclear. For starters, the rate of asymptomatic cases have been revised down to 17-20% (UK and US data mostly). Initial reports overestimated true asymptomatic carriers as quite a few developed symptoms later in the disease. True asymptomatic carriers have a shorter time-frame in which they are positive (most were tested negative within 14 days) limiting the time-frame in which they could infect other persons. However, unless there is sufficient follow-up it is difficult to distinguish pre- and asymptomatic carriers and depending on the data, they may be classified as a single group. I.e. folks that are positive, might infect folks, but show no symptoms at time of testing. Why do folks think that asymptomatic or pre-symptomatic carriers might be infectious? The reason is that in all cases significant viral loads are detected. Among pre-symptomatic carriers the level is highest just before onset of symptoms (in other respiratory diseases the titer tends to be higher during symptom onsets). I.e. there is good reason to believe that folks can transmit even if they do not show symptoms (yet). Viral load is a decent indicator of potential risk and while there is a high variance among patients (regardless of symptoms or not) larger patient pools indicate that even truly asymptomatic carriers can carry high loads (at least as high as mild carriers). The only real counter-argument so far is that most analyses are based on genetic material and few folks are actually doing cultures to check whether the virus actually infects a cell culture. The other side of the argument is epidemiological in nature, where there is a big discrepancy between (known) active cases and spread patterns. It could be caused by folks with no or mild symptoms that do not get tested. Other evidence are gathered by isolating families in which spread was detected but the carrier did not show symptoms. As a whole, it is more likely than not that asymptomatic spread adds to the pandemic, though uncertainty exists about relative contribution. Obviously it will depend on the population (e.g. in older populations symptomatic cases will likely be much higher, whereas in younger milder cases may be dominant). Also asymptomatic cases have a shorter window of infection, so in well-isolated communities they may be not that relevant. However, as there is no means to test everyone all the time, from a health policy perspective the only effective measure is to isolate and distance, regardless of detected symptoms (again, based on known viral kinetics).

Was CO2 controlled?

I think you misunderstanding something or you might be a bit unclear what you want to achieve. There is no way to have 100% accurate patient data virtually with any disease outbreak as you would have to test everyone and keep testing until the outbreak is over (just because you test negative now does not mean you will be negative tomotrow, or the day after). Except for very small populations this is not feasible, the CDC recommendation notwithstanding (I am not even sure why you bring that one up, it does little to address the overall challenges in accurate disease monitoring). What folks have always done is to use secondary measures on top of the known cases to estimate infection rates, as I mentioned above. This is why we do have general ranges of estimates of things like the flu season. I do not see why COVID-19 should be different (except that we have done more tests). The issue is with you bringing up "accurate". All disease estimates have an error. If your criterion is an error of 0% then it is likely not obtainable except in very small precisely defined populations. Again, I mentioned that IFR and other measures of mortality have very specific (and somewhat limited) uses. If you want to compare diseases, for example then it depends on how you measure them. For most diseases reporting is done on symptomatic cases as we have no or little data on folks that may have been positive but do not have sufficient symptoms to seek a physician. And even then, often they are sent back with some cough and fever medicine rather than a test. Again, it is not clear to me what precisely you seek to compare. The only important bit is that an apple-to-apple comparison is made. Folks have done serological investigations into defined populations to estimated IFR for SARS-CoV-2 to be around 0.45-1%, depending on the study and with estimates of ca. 20% asymptomatic cases. Of course, that will also depend on the age distribution. But again, in isolation I am not sure what that would tell us other that if we let everyone getting infected would be lose up to 1% of a given population to the disease (ignoring age gradients). If the idea is to figure out whether the disease has more or less impact than others, I think other metrics (as others have pointed out) are more interesting.

I stand corrected then, but I admit it does surprise me. There were a couple of discussion on that matter and at least personally I do not think that we can control the risks well enough to pass it through ethics review. But apparently others see it differently. Thanks for letting me know, btw, I found a related article, apparently they got approval to test 90 volunteers to check infectious dosages. Absolutely. I haven't seen calculations covering the whole of 2020 and while the lack of a flu season curbs things a little bit in some areas, but even in the middle of last year, the excess deaths have pushed overall mortality statistics a fair bit.

A direct test would involve to deliberate infect a person. For some diseases it is possible to have them approved as the risks are well known and can be controlled. SARS-CoV-2 has too many surprises at this point, including causing blood clots and causing neurological symptoms. As such it would be highly unethical to initiate such studies. There are also no good proxies as other coronavirus have quite different infection properties. However, what has been done is measure the titer of infected folks (symptomatic and asymptomatic). There would be no reason to assume that viral particles produced by an asymptomatic patient would be any less infectious than from a symptomatic one. Not sure what you mean. Testing protocols are well established. And the number of positively tested folks are clearly our lower limit of estimates. I.e. we know that at least that many folks were infected. To figure out the likely values beyond that requires additional research. These include indirect measures, environmental measures or antibody testing. There are studies out there which, given the time frame obviously cannot cover the whole of 2020 and likely will take a bit longer to provide us with estimates. It is not something that I describe myself, rather if folks on this forum have demonstrated expertise in a certain field, they may be given such broad labels, if they agree to it (e.g. we got physics experts, but their specialty is of course a sub-field). But my main expertise is in cellular and microbial systems and associated analytics. From memory I think every local expert had advanced degrees in their field. But I do not think that we have that many left. But for the most part is just a whimsy thing to have, as you will note by the various free-form labels many of our older members have. It is less a failure of policy, but rather a failure to adhere to them. After H1N1 the Obama administration has created a pandemic task force, to specifically deal with pandemics and allow tight coordination of the CDC with local health authorities. However, the task force was basically dismantled and folks were put in place who basically downplayed the disease. Reportedly there was a lot of friction between what the CDC wanted to do and what the Trump administration wanted and it included changes in how data was collected and/or presented, limiting how the CDC could communicate with the public. Specifically to your question of asymptomatic testing, there was a report where a senior CDC official told the press that there was pressure from the administration to stop recommending testing if one has symptoms in order to keep the infections numbers lower. Likewise, at one point the administration decided to move the responsibility to collect data from the CDC and hired a private contractor, resulting in the resignation of data officers of the CDC. In summary it does appear that much of it was preventing the CDC from doing their job in order to control the narrative. That is not to say that a fully-led CDC response would not have had issue, but we know that with all the politicking around the issue, the USA has suffered the most (validated) COVID-19 related deaths and serves as an example what happens if one does not take measures.

As Studiot pointed out, different countries have different reporting systems. The US specifically was potentially hobbled by the last administration. Normally you will find details on their respective websites how they do it. However, the data is generally submitted on the local level, e.g. coded by a hospital and then may go through local health authorities or even submitted simultaneously to local and federal reporting systems. As example here are reporting instructions from the US-CDC: https://www.cdc.gov/coronavirus/2019-ncov/downloads/php/COVID19-CSV-Case-Reporting-Instructions.pdf With regard to asymptomatic spread, there is of course no way you can test that in laboratory directly as it involved to actually make someone sick. Rather, folks will depend on retrospective analyses or other measures, including antibody and wastewater testing. However, tests of folks who are asymptomatic but turned out to be positive found that even without symptoms, the viral titer can be fairly high, which makes spread very likely. Likewise, asymptomatic spread is also the best explanation for high levels of community spread, where infected folks could not be linked to positive cases. Also you need to define "accurate case estimates". The most accurate number are of course people tested positive, which forms the baseline. If you want to figure out how many may be underreported, that requires additional research. As the pandemic is still ongoing, the estimates will continue to change so I am not sure to what accuracy would refer to here (a specific timeframe, for example?).

No, as asymptomatic cases can still produce enough viral titer to be tested positive and spread the infection. The massive spread and susceptibility in the population is the reason why we have so many deaths ( as I have mentioned above). Perfect data is a challenge for any disease. However for this one we do have a ton of data with a range of supporting estimates. But note that death rate is heavily influenced by a lot of parameters, such as availability and access to emergency treatment, oxygen, ventilators and so on. As such there is a wide range of estimates, depending on where you are. I.e. there is not a singular estimate satisfying all criteria or uses. In other words, it depends on what you want to figure out. If the goal is to compare to, say influenza, it is going to be difficult as influenza is usually highly underreported and often relying on indirect measures (e.g. absence from work) to estimate the actual outbreak numbers. There are also different measures that one need to distinguish- the case fatality rate. I.e. how many of folks tested positive ultimately die. That, of course depends on how well we test the population. The infection fatality rate relies much more on estimating the the total rate of infections. The ranges even for established diseases such as influenza have several order of magnitude differences in range (again, because the actual known infected proportion is generally not known).But COVID-19 makes things even worse- there is also the risk of long-term damages, i.e. folks might indirectly die from the disease quite a bit off in the future. As it turns out, case of infection mortality alone is probably not a great measure to characterize a disease- it ignores for example the proportion of susceptible people. This is why in the USA alone we have more COVID-19 linked deaths than in the whole world for the H1N1 pandemic. Moreover, I see mortality rates frequently misused in the media (one way or a another). But as mentioned above, we now have a single disease which has been verified to cause as much deaths as all other lower respiratory infections combined. It is a single virus that worldwide ranks somewhere in the top 5 of causes of deaths. In the USA, COVID-19 is the third leading cause (or higher) of death for folk above 45. Between 35-44 it is about as lethal as transport accidents (but double as high as homicide). Influenza an pneumonia generally is only around the top 9 and only for groups older than 65. Again, it is a single disease that significantly alters the death statistic of the population. With regard to the response, I think at this point it is clear that countries that fail to have a centralized, updated pandemic plan or, if they have one did not act on it (recent reports have highlighted the issues in Italy) suffered more excess deaths and have resulted in higher circulation of viruses. The latter is also the cause for the emergency of new variants, and which makes it more likely that COVID-19 might become an endemic disease.

I think some folks, including many students, are under the assumption that as long someone cites something, it somehow becomes more credible. That of course is not true. Assuming the citation was done correctly, it only points out to a fact or observation made by some other group. It does not mean that it follows the argument that one wants to make. I can, for example, correctly cite a paper that shows similarities of SARS-CoV-2 to existing bat coronaviruses, but if the main thrust of my paper is about how lizardmen have released the virus in order to overthrow their pangolin overlords, it does not actually add credibility. It is more that if no citations (or mainly self-citations) are given, that one should be even more skeptical.

The issue is if you make things up, there is no reference point to assess whether something is correct. At best one can check for internal consistency. However, if the made-up concept is not well described either (especially if deliberately so), then even that can be challenging or impossible.

Oh no, this study did not look at cultural factors. The goal was to figure out the high death rates and found a strong association with a) being infected in the first place and b) a strong effect of SES. Other studies have looked at reasons for higher infection rates among black and Hispanic folks and the conclusion from those is that it seems to be strongly correlated with jobs. 75% of frontline workers are POC, they are overrepresented in high-risk jobs such as meat factories and so on. While there might be cultural aspects, the economic ones (i.e. jobs) seem to explain most of the variance on their own, if looking at larger patterns. There are specific communities in which have high infection rates that could be based on cultural aspects, such as among orthodox Jews which appear overrepresented. But they tend to be pockets rather than larger patterns.