CharonY

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.

Isn't it true for a vast swath of capitalist markets, though? I mean you can buy and sell debts, bet on increase/decrease of values and so on. Compared to that, a blockchain is almost physical.

! Moderator Note The topic does not seem to be related to evolution but potentially about how bodies can be preserved? Could be an engineering challenge.

Pump it up and ring my bell cause I want to ride my bicycle, bicycle, bicycle.

The fundamental question is rather simple: "when does human life begin?". The issue is that answers are a tad more complicated. While most proponents of fertilization as the starting point are likely religious, it is not always the case. Also, it is not really an US-specific issue. It pretty much is aligned with the issue of abortion rights and the connected ethical dilemma, where you will find polarized views across the globe. The concept that one might start defining something as human and therefore worthy of protection later in the development cycle (though precise boundaries are not really forthcoming) has higher acceptance (I think) now than it was in 90s or early 2000s, so we may be seeing a shift in attitude. But as a whole it is one of the questions that operates in an ethical grey zone as biology escapes simple binary classifications.

SARS (if that is what you referring to) killed much fewer folks than H1N1 (less than 1000). It should also be noted that spread (as in pandemic) does not equal to lethality. Seasonal influenza for example claims a lot of deaths each year, and it is annoying (to put it mildly) that folks just assume that it is fine. In addition, how folks deal with a disease (i.e. the medical system) highly influences how deadly a disease ultimately is. More as a side note "new" is quite a bit of an issue with diseases. The issue being that many have been around for quite a long time. However, many viruses (but also bacteria) can exchange genetic material and merge to various degrees resulting in pathogens that are either originally non-infectious to humans or are unable to spread between humans, suddenly become able to do so. These recombinations as well as regular mutations can happen anywhere with a disease reservoir (e.g. human populations, wildlife and/or farm animals). In fact, human reservoirs are where new SARS-CoV-2 strains are emerging in the UK, South Africa, Brazil, USA and so on. I.e. areas where the virus is circulating. There is no good reason to assume that any place on Earth is safe from the rise or development of new diseases. No, most influenza deaths are among the elderly. H1N1 had an uncharacteristic high level of deaths among younger folks. Silly me, of course it is just because of ideology and attitudes and not the fact that all had a friggen pandemic plan in place and acted on it, which mitigated issues. It also is news to me that the listed countries don't have capitalism. So it appears that only countries which are antagonistic to China can implement measures within their own borders, such as aggressive contact tracing programs or ramping up on critical supplies (and note, Taiwan never enacted a blanket ban on travel from China, they restricted travel from affected provinces, such as Hubei instead). Seriously, read up on it, there is not a lot of reason to wildly speculate. China did not even play a seriously role in their respective mitigation strategies. Of course it is all yours then, after all you promoted indefinite travel restriction while at the same time claiming that, contrary to all evidence, contact tracing won't work. Or how else should one interpret: And it does not really explain why it failed in Europe, either. Also, the argument does not seem to be internally consistent. Assuming that free marker and/or capitalist concerns are overriding pandemic plans, one would assume that travel restrictions would be less likely to be enforced than, say contact tracing. The former could disrupt a range of business, whereas the latter (if successful, as we have seen elsewhere) can keep businesses open. Let me summarize. On the one hand there are numerous studies tackling the pandemic from perspectives ranging from health services, policy and economy. There is almost an universal consensus that the most effective measures are strong, ideally centralized eradications strategies (see e.g. Baker et al. Med J Aust 2020). We have empirical evidence that the outcome were much better than in other countries, regardless of their economic system. Likewise, travel restrictions have shown to have a moderate effect at best (see Russell et al. Lancet 2021 6:1) Meanwhile, do you have any evidence that what you propose would have any effect beyond hand waving? Do you think Europe and the US have a very different economic system than Taiwan or South Korea that would impact responses? If so, how? If you cannot substantiate any of your claims, I can only assume that you just want to peddle your personal hypotheses without critically dealing with the matter at hand. And rather obviously, any conclusion based on evidence-free assumptions are unlikely to be useful.

! Moderator Note It does not seem that the discussion is progressing, mostly because there seems to be a lack of understanding of basic principles. As I suspect that a discussing or reading up on said basics are more beneficial than wild speculations and off-topic tangents, I am going to lock the thread for now.

It is stupid to assume that outbreaks will only happen in China. In fact, the assumption that outbreaks are limited to elsewhere is one of the reason we are in this mess. While most pandemic in recent times had low death tolls (well, H1N1 is estimated to have killed between 100 and over 500k people, which is not precisely low...) , it is a matter of chance until a more deadly one (like COVID-19) arises, with just the right combination of traits that also make them difficult to detect, especially in the early phase. The only effective strategy would be to restrict all travel. You'd be using the same logic. Why should convenience of, say, US travelers be more important than anyone's safety? Also it was lack of a pandemic plan and lack of political will, not capitalism that led to poor results. You should read up on how other countries responded. Also I reiterate that looking outward as the main or sole preparation for disease outbreaks and pandemic preparedness is a special type of short-sighted and annoys me to no end. Everyone with a little bit of background in infectious diseases observing the situation could tell where things were going early last year. Sure, there were a lot of uncertainties (the risk of asymptomatic spread being one important bit), but a lot of professionals in the field raised alarm (the early timeline could have been shifted by maybe few weeks at best). And then, while some countries activated their plans, plenty others did not (I am sparing you the details less MigL gets annoyed by reading it over and over again). There were plenty of measures to take and saying that it is capitalism, individualism, desire for freedom or culture (I have heard it all at this point) are just a piss-poor excuses for not taking the right measures. Most economists at this point agree that even hard lockdowns at the beginning would have been less costly then the reactive responses that were eventually implemented. Travel restrictions can slow things down but then you have to take measures in your own friggen country just as others have done in order to control spread. Yes it is annoying, but if you are unwillingly to be inconvenienced in a deadly pandemic then I do not know what to tell you.

I think the broadest term would be bias of some sorts- the intention might not be willful misrepresentation. To be maybe a bit clearer in an example. If a graduate student performs an experiment, e.g. looking at growth differences between bacterial strains, at the beginning you often have huge variation in the data. This is often caused by mistakes, such as inoculating varying amounts of cells at the beginning, contaminating your sample or making mistakes in media composition. With practice, the variance typically narrows and then one might detect significant differences. If one reports every growth data, the what we consider "better ones" will be drowned out by the rest. Even if you just dump the data without highlighting it in the paper much, the reviewer would have a hard time going through all of them only to come to the conclusion that, yes trainees probably did not did a good job. Given the time constraints that we operate in, it would make the process really cumbersome. But I do understand the larger point- data scientists are more comfortable in dealing with big data set and at least in theory, if everything is on the table, perhaps not during the peer-review process but at some later point, those folks could extract the data and maybe see other patterns in there. But the really big issue from an experimental viewpoint is that the largest set will be simply low-quality data. If you ask any grad student, they will all tell you that the most significant data usually is generate toward the end of their degree, when they a) built up the skill to perform the experiments reliably and b) figured out all the ways they should not run the experiment. As you can probably read, I am a bit torn regarding the best way to report complex data. However, I do think that to make a case of fraud it is necessary that the one committing it knows that the form of reporting distorts the findings. I.e. if deliberate deception is involved. Traditionally, we use controls to account for bias (rather than offering all data sets, including those that we deem failed experiments), of course this is not fool-proof, either. From an outside view, it is difficult to tell, of course. There is also the issue that much of it depends ultimately on trust. I trust that my students are reporting data the way they performed it, for example. This is certainly an issue and especially the high competition makes it worse. I am not sure whether there are short-term solutions for it. However, one should keep in mind that ultimately the system is (slowly) self-correcting. Obviously if you publish something interesting, but biased, others will have difficulties to build upon that data. Eventually newer findings will indicate that what has been published before is probably not accurate or is missing some key criteria. Cases of outright fraud are often that far off from the base that it could trigger retractions or even more serious sanctions. Biased data on the other hand are often more borderline.

It can be, if the full reporting substantially changes the outcome. There are borderline cases which can on either side of the issue. For example, some data sets might be selective due to their nature. Examples include microscopic images which are qualitative in nature (e.g. showing co-localization as a random example). Now if you have taken hundreds or thousands of pictures you generally are unable to provide all of them (and likely, no reviewer would want to go through all of them). So as a consequence you provide images that are supposedly representative. But this criterion can be highly subjective and biased. In other cases it is not uncommon that difficult experiments need to be repeated fairly often until the assay works (remember that most research is actually done by trainees). So here the issue becomes whether a particular data set that might be just botched should be added to the final analysis or not. Fundamentally there is a big move to have all data sets, including supposedly bad data published, which in principle makes sense. However, it has a lot of practical limitations.

The only studies i am aware of are looking whether immunization of one sibling helps prevent other siblings from getting sick (e.g. If they are too young). You won't have a lot of other cases as folks who deliberately don't vaccinate, usually do so for all kids. There of course you see a lot of preventable disease outbreaks.

namegoeshere decided to hurl abuses by way of introduction. Therefore, he has been shown the door.

! Moderator Note I think that is enough information to determine that a fruitful discussion is not to be had here. Locked.

! Moderator Note I am sorry for any hardships you have encountered but a) we cannot dispense medical advice on this forum and b) I do not see actual science or politics to be discussed. However, as you are exclusively talking about a series of personal experiences, I do not see a lot to be discussed here. So perhaps try to figure out what you would like to discuss that ideally goes beyond unverifiable personal anecdotes.