Machu Peach
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Everything posted by Machu Peach
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It's really interesting to me how differently each of the groups that I interact with (circles of friends, family, scientists/coworkers) talk about COVID. Different topics, different conclusions, different risk levels, different solutions... The only consistency between groups is a certainty that everyone else "doesn't get it."
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I was going to ignore this because it really don't matter, but you're repeating it again and it's incorrect. If one looks at daily infections (a poor metric as the testing was extremely limited at the time), the virus began spread in Iran in late Feb and peaked at the end of March, not April. If one looks at daily mortality (a much better metric) the virus was at it's worst throughout March, not April. The highs in Tehran in March were mostly in the 60s, with the max being a day of 72. The lows were generally in the 40s. One has to go to April 20th to get the first day with a high of over 72. Other parts of the country were even cooler. This is all very easy to check on a number of websites. That said, according to my Iranian wife and her family, their government vastly underreported the extent of COVID early on. I obviously don't know, but the numbers may be suspect.
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What about Iran's climate makes you think it's an example of a climate fantasy? The average highs in Tehran in Feb/March are probably about 50 degrees. They're significantly cooler in more northern cities like Tabriz or if you gain any elevation.
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The overall rate for vaccines going from an initial IND to full approval is about 33%. Most vaccines fail on the transition from phase IIa to phase IIb or phase IIb to phase III. The phase III to full approval transition is quite good, over 80%. There's a couple caveats when applying those numbers to COVID. 1) The timelines for COVID vaccines are a tiny fraction of normal vaccine studies. 2) Most of what the media are calling "phase III trials" are actually phase IIb/III trials.
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Why do you think this? The data I'm seeing show that it's pretty clearly 'peaked' around Memorial Day. I think your point is fair- death rates have declined, hospitalizations have steadied, though still at a fairly high rate vs. even the heart of the early onset and quarantine in April. I think the idea that the certainty that the 'peak' has happened considering that was the assumption elsewhere until the last two weeks is problematic. Non-ICU hospitalizations have decreased by 50% and ICU hospitalizations have decreased by 40% since 'around Memorial Day'. To say that they are 'steadied' is disingenuous. Also, you specified Minnesota not 'elsewhere', so I provided data on Minnesota.
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I understand that there's a unstated reason why some are focusing so hard on AZ, TX, and FL, but CA is experiencing a similar increase in new infections. It would be good not to ignore it when attempting to determine the cause of the increase. CA state website (https://public.tableau.com/views/COVID-19PublicDashboard/Covid-19Public?:embed=y&:display_count=no&:showVizHome=no)
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Why do you think this? The data I'm seeing show that it's pretty clearly 'peaked' around Memorial Day. Off of the state website (https://www.health.state.mn.us/diseases/coronavirus/situation.html)
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I don't really care to discuss it further other than to say that this is a highly unusual and very dangerous practice even by COVID-emergency standards.
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I can't speak to your friend as I'm not a surgeon and I don't know her hospital's policies, but at my hospital putting myself in a situation where I splash blood up on my face without a full face shield+ would get me sent to remedial blood-borne pathogen training even during COVID. Continuing to wear said bloody mask while seeing other patients would certainly draw the attention of coworkers and OHSA staff to say the least.
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I don't think you know enough about me and many other people to make this sort of statement. It's a needlessly confrontational generalization based on your biases.
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One does not have to search very hard to find examples of senior health officials stating that masks didn't need to be worn by the general public because they would be ineffective at preventing spread. I, and many others, did not 'miss that'.
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With public health officials basically admitting they've been lying regarding masks and the inconsistent and hypocritical messaging on risky behaviors I'm not surprised that people are beginning to ignore them. I may not agree, but I can't really blame them either.
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This is deeply concerning to me. There are numerous examples of public health officials going far beyond "opt[ing] not to promote the use of masks" and directly saying that masks were mostly ineffective or symbolic. At least one reasonable interpretation is that they intentionally mislead people to save PPE for healthcare workers. That might even be an arguably correct path, but the cost of knowingly lying is that people trust you less each time you do it. Given how critically important public buy-in is to public health initiatives I think that anything that harms public-health credibility is ultimately going to have a massive negative effect. Given how staggeringly inconsistent, overly-alarmist, and hypocritical the messaging from public health officials and media has been I actually understand peoples' frustrations. If a second lockdown is needed I fear there will be massive non-compliance by a large group of people as a result of this abysmally bad messaging.
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I am well aware that scientists can change fields. My PhD is in Microbiology and Immunology and while I still do a little of that I switched to gene engineering 5 years ago. This guy is "part of a team that usually models the spread of crop diseases" at "Cambridge’s department of plant sciences." By contrast I am "part of a team that usually engineers genes" at a major university's "gene engineering center." This seems more like a one off foray into a completely different field which historically is a giant red flag for any study. I would consider the skillsets vastly different, so agree to disagree. He's not automatically wrong of course, but there are an abundance of trained mathematical modelers in epidemiology that have been focused on respiratory diseases. I'd be more interested in their thoughts. ...and actually, if the subject is mask wearing, I'd be far far more interested in the mechanisms of mask wearing on transmission rather than weird estimates like "masks that only capture 50 per cent of exhaled droplets would still provide a “population-level benefit”". Do masks capture 50% of exhaled droplets? Is that the 50% that carry virions? Does catching 50% of droplets (and assuming that means 50% of virions) actually reduce transmission? If so, by how much? It's been estimated that 80% of infections occur in co-living environments. Does that mean that masks should be worn at home to reduce that 80%? If one can't answer these questions, it's really hard to trust any modeling based on assumptions like that.
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I'm confused as to why someone with a background in crop diseases would be consulted on respiratory disease transmission.
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Yea, there certainly is a lot of that. There are many examples of terrible media predictions, but this one sticks out in my mind as particularly egregious. It's probably because I thought The Atlantic was better than this sort of nonsense. Georgia’s Experiment in Human Sacrifice (https://www.theatlantic.com/health/archive/2020/04/why-georgia-reopening-coronavirus-pandemic/610882/)
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I'm getting the impression that you think I'm disagreeing with you when I am not. I posted the stats I posted to point out that the two claims in the tweets (ICU capacity near max and COVID patients on ventilators quadrupling) are unique to that hospital system. In fact, the only COVID-specific stat I posted shows a ~20% increase.
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I'd assume so. Most hospitals are reducing or eliminating the surge capacity they set up because it's not needed.
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Looking at total ICU occupancy for Arizona as a whole. May 15th: 1200 June 6th: 1192 Added: Total ventilators in use for COVID+ or COVID suspected for Arizona as a whole. May 15th: 210 June 6th: 249 Source: https://www.azdhs.gov/preparedness/epidemiology-disease-control/infectious-disease-epidemiology/covid-19/dashboards/index.php
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Thanks. I've been lurking on the site forever but never felt I had anything to add because y'all know a lot more about baseball than I do.
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I'd be lying if I said I could do anything other than guess. I switched fields from vaccine development 5 years ago so I'm not directly working on anything COVID-related now, but I do have a lot of old friend and colleagues. They're optimistic they'll have a protective vaccine, but worried that the decreasing rates of infection won't give their current studies enough statistical power. SAR and MERS (two very similar viruses) vaccines induced immunological features that one would assume would be protective, but there was obviously no way to test them after the outbreaks ended. If forced to guess (cannot stress enough that this is a guess), I would say that a vaccine that induces good protection will be out in some capacity sometime this winter. I think the duration of protection will largely be unknown (it could be just months or could be years) and that a a very sizable percentage of the population (in the 10-50% range) will not want to be vaccinated until it undergoes more testing. I am reasonably confident that a good vaccine will eventually be developed. There's nothing particularly unique about COVID (at least that's known right now) that makes vaccine development more difficult like with flu or HIV. I also don't really know what to expect as far as the "2nd wave". I think it's more likely than not to occur, but that's just my impression and I have no idea what it may look like if/when it does occur. I don't really trust anyone who claims to have good predictions for either a 2nd wave or the short-term success of vaccine development at this point. There are just too many unknowns.
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For vaccines we usually are aiming for well over 95% immunity because the goal is often eventual eradication. Measles was the target, but sadly due to the success of the anti-vax movement that no longer seems possible. I came up with over 90% because most diseases that I'm aware of need coverage in the 70-95% range and this seems quite infectious. I don't like modeling using R0 beyond very general estimations. I personally think R0 is a terrible concept and I wish the general population had never heard of it because it is so often misunderstood. It gives people the impression that it's something inherent to the virus, but really it's far more about the environment. An extreme example is malaria where R0 estimates have ranged up to 3000 in certain settings, but is effectively 0 in the Midwest due the lack of a vector. Thus an R0 is going to vary considerably and is not a constant. There's also significant heterogeneity of spread between infected individuals, something that most basic modeling does not account for. That seems particularly important to consider with COVID due the seemingly high reliance on 'super-spreaders' (another term I hate because it implies that it's a binary category) for non-co-living spread. I know there have been multiple studies that quantify this, but I'm not sure if anything method has become standard. My background is also viral pathogenesis not epidemiology so I probably have a bit of bias against modeling. I think comparisons to real world data from other diseases is probably more useful.
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Thanks for the response! Obviously things change drastically if you're comparing all systemic racism as opposed to just police brutality. You are making a few major assumptions. 1) That 100% of the difference in life expectancy is due to systemic racism. I don't think that's a safe assumption. Females in the US live about 7 years longer than males and I don't think anyone would blame 100% of that gap on systemic sexism. That's a bit of an apples to oranges comparison, but it does point out that there can be other factors that influence life expectancy. 2) That life expectancy is similar in all environments. Life expectancy varies greatly by state and urban/rural living and races are not evenly spread across locations. To avoid an apples to oranges comparison you'd need to normalize for both. I suspect this would have a major impact on the data. 3) That 66% immunity could achieve herd immunity. For a more easily spread disease like COVID 90% is a more realistic minimal value. Finally, I am a reasonable person. Asking for clarification or explanation is not unreasonable. I never accused anyone of bias or tribalism. I don't see the need for your last bullet point.
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One thing that has been bugging me is the definition of "public health". I can see two possible definitions. A more limited one would be hygiene, epidemiology, and disease prevention and their effects the health of a population. One can easily argue that this is too limited as it ignores many major things impacting health. A more expansive definition would be anything to do with the health of a population. One can easily argue that this definition is so broad that it covers literally everything in the world and thus is meaningless. One big criticism of mine of the CDC is that mission creep has caused them to study things like gun violence and accidental pool deaths. Both are certainly important topics that deserve study, but really fall more into a societal structure category than a disease category. Thus by having the CDC study them you're losing expertise/focus on actual diseases. There are certainly many things in grey areas, such as alcoholism, which falls into both societal structure and disease categories. I don't really have a good or simple answer for any of that, but they are things that I think need to be considered.
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Would you please prove what "numbers" you're looking through to determine that police brutality towards minorities and and COVID are of a similar magnitude from a public health point of view? I don't know how I would even begin to determine such a thing.