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>> It came from the first article that I linked, by a medical doctor and professor of epidemiology. From that article: >>> Over the ensuing years, after its em

>> It came from the first article that I linked, by a medical doctor and professor of epidemiology. From that article: >>> Over the ensuing years, after its emergence in 1977, the virus went on to infect a significant portion of the worldwide population, killing roughly 700,000 people. That estimate is not referenced in the paper you link. I looked for a different source estimating the number of deaths. I found this article [1] that reports an earlier estimate of a mortality rate of less than 5/100,000. "Mortality rate" is taken over the total of a population (not infections). With a mortality rate of less than 5/100,000, for the virus to kill 700,000 people, as in the claim in the article you link, it would have to circulate in a population of 14,000,000,000 (see rule of three). That's not possible. >> If I had to guess knowing only that such a novel pathogen had just been introduced, I'd thus bet with p ~ 5% that it was a research accident. Does that mean you believe every recent pandemic has a ~5% chance to be a "research accident"? SARS, MERS, HIV, Ebola, mpox, Zika, etc, all had a ~5% chance to be research accidents or is Covid-19 special? What happens to this 5% number as we see more pandemics? Does it go up or down? Can you say? ______________ [1] https://pmc.ncbi.nlm.nih.gov/articles/PMC4542197/ https://pmc.ncbi.nlm.nih.gov/articles/PMC4542197/ >> In 1977, an H1N1 influenza virus appeared and circled the globe. Colloquially referred to as the “Russian flu,” as the USSR was the first to report the outbreak to the World Health Organization (WHO), the 1977 strain was actually isolated in Tientsin, Liaoning, and Jilin, China, almost simultaneously in May of that year (1). It was atypically mild for a new epidemic strain; the influenza mortality rate (IMR) of the 1977 flu was calculated to be <5 out of 100,000, less than typical seasonal influenza infections (IMR of 6/100,000 people) (2). In addition, the 1977 strain appeared to affect only those 26 years of age and younger (3). These odd characteristics turned out to have a simple scientific explanation: the virus was not novel. The 1977 strain was virtually identical to an H1N1 influenza strain that was prevalent in the 1950s but had since dropped out of circulation (4).

tripletao 2y ago
> That estimate is not referenced in the paper you link. I linked two papers, and that quote appears in the first. Perhaps you were looking at the second? Here's the link again, with the text that I quoted highlighted: https://muse.jhu.edu/article/936217#:~:text=Over%20the%20ensuing%20years%2C%20after%20its%20emergence%20in%201977%2C%20the%20virus%20went%20on%20to%20infect%20a%20significant%20portion%20of%20the%20worldwide%20population%2C%20killing%20roughly%20700%2C000%20people https://muse.jhu.edu/article/936217#:~:text=Over%20the%20ens... Influenza statistics are noisy, since most patients are never tested. We could compare the methodologies of those estimates, and try to decide which is better; but I don't think that's a productive use of our time, unless you think 700k deaths are worthy of concern but 200k deaths (or whatever the right number would be) are not. > Does that mean you believe every recent pandemic has a ~5% chance to be a "research accident"? SARS, MERS, HIV, Ebola, mpox, Zika, etc, all had a ~5% chance to be research accidents or is Covid-19 special? If I knew nothing else about the pandemic, I'd bet with p ~ 5% that it was research-origin--that's my prior, knowing only that it just emerged. For real, I of course know more. For e.g. MERS, the proximal animal host was found, and genomic evidence shows repeated spillover; so that additional evidence makes me effectively certain it was natural (p ~ 0%). For the 1977 flu, I'm very confident it was unnatural (p ~ 99%). Those average to something close to 5%. If we get strong confirmation that SARS-CoV-2 (or any other new pandemic) arose unnaturally, then my prior would go up. If we get many more natural pandemics without any unnatural ones, then my prior would go down. I would also incorporate other information. For example, if many more scientists undertake high-risk research on enhanced potential pandemic pathogens, then my prior would go up. If such projects are restricted and become less common, then my prior would go down. I think that's fairly normal reasoning?
- YeGoblynQueenne 2y ago
  >> I linked two papers, and that quote appears in the first. Perhaps you were looking at the second? Yes, that's the paper I looked at. "Not referenced" means that the 700k number given in the paper has no reference. The author seems to have pulled it out of thin air. Here I'm copying the text from your link with the highlight: >> By comparison, the 1977 "Russian flu" virus produced a milder illness in most people (Gregg, Hinman, and Craven 1978). Over the ensuing years, after its emergence in 1977, the virus went on to infect a significant portion of the worldwide population, killing roughly 700,000 people. But this particular pandemic isn't important only because of its impact on disease and death: it also reveals uncomfortable truths about the complex relationship between viruses and humans. The 1977 pandemic is a cautionary tale, one that—hopefully—we can learn from. Note there's no citation next to the 700k number. So where does it come from? Nowhere. The author just made it up. Perhaps that's the reason for the lack of concern at the 700k deaths: they didn't happen. And where did your 200k number come from? Can you show your work? >> If we get strong confirmation that SARS-CoV-2 (or any other new pandemic) arose unnaturally, then my prior would go up. That's not a prior, it's a posterior: it's the probability of an event given the evidence. We're interested in a prior probability when we don't have enough evidence to calculate a posterior. Like with Covid-19. Once more I suggest to try and get the terminology right so you don't confuse yourself and everyone else. It will also help you to find better ways to reason under uncertainty.
  - tripletao 2y ago
    Sorry, I'd misunderstood what you meant by "referenced". The 700k is also reported in papers published before the one I linked, for example Table 1 of https://link.springer.com/article/10.1007/s00430-009-0118-5 https://link.springer.com/article/10.1007/s00430-009-0118-5 So the author didn't just make it up. Peer review isn't magic, but it does tend to catch such a blatant level of fraud; so it's odd that was your first assumption. He should have included the reference, but it's not uncommon to get sloppy when a result is widely reported. I haven't yet traced this number back to the epidemiological methodology. As noted, I fully accept that it might wrong, since influenza numbers tend to be calculated from excess mortality and not directly counted. Until we've reviewed that methodology, I agree that it's reasonable to question the exact number. I believe that uncertainty applies to most epidemiological estimates, including but not limited to the 1977 flu. But I've explicitly acknowledged the uncertainty, so I'm not sure why you're continuing to focus on this. There is no significant question that the 1977 flu was reintroduced into humans due to a research accident, and no question that it went on to infect a large fraction of the world. There's no question that many people died, even if the exact number is uncertain. Are you proposing that it's few enough that there's no reason to care? If so, what's your cutoff? The 200k was an arbitrary placeholder. I thought the phrase "or whatever the right number would be" following it would make that clear, but maybe not. > That's not a prior, it's a posterior: it's the probability of an event given the evidence. We're interested in a prior probability when we don't have enough evidence to calculate a posterior. Like with Covid-19. I mean that if SARS-CoV-2 were proven to have arisen unnaturally, then my prior that a different future pandemic was unnatural would go up. That again seems very normal to me, since humans predict the future using the past. As I noted, I would adjust my prior based on factors other than a simple count of past research-origin pandemics too; but the count is obviously relevant information. Your updated prior of p ~ 0.0001 to 0.00001 seems to also be derived from a past count, just reaching into prehistory to inflate the denominator. This thread started when you asserted that "every single pandemic and disease that has ever infected humans did not leak from a lab". Exact numbers for the death toll and prior aside, do you at least agree that's false?

tripletao 2y ago

> That estimate is not referenced in the paper you link. I linked two papers, and that quote appears in the first. Perhaps you were looking at the second? Here's the link again, with the text that I quoted highlighted: https://muse.jhu.edu/article/936217#:~:text=Over%20the%20ensuing%20years%2C%20after%20its%20emergence%20in%201977%2C%20the%20virus%20went%20on%20to%20infect%20a%20significant%20portion%20of%20the%20worldwide%20population%2C%20killing%20roughly%20700%2C000%20people https://muse.jhu.edu/article/936217#:~:text=Over%20the%20ens... Influenza statistics are noisy, since most patients are never tested. We could compare the methodologies of those estimates, and try to decide which is better; but I don't think that's a productive use of our time, unless you think 700k deaths are worthy of concern but 200k deaths (or whatever the right number would be) are not. > Does that mean you believe every recent pandemic has a ~5% chance to be a "research accident"? SARS, MERS, HIV, Ebola, mpox, Zika, etc, all had a ~5% chance to be research accidents or is Covid-19 special? If I knew nothing else about the pandemic, I'd bet with p ~ 5% that it was research-origin--that's my prior, knowing only that it just emerged. For real, I of course know more. For e.g. MERS, the proximal animal host was found, and genomic evidence shows repeated spillover; so that additional evidence makes me effectively certain it was natural (p ~ 0%). For the 1977 flu, I'm very confident it was unnatural (p ~ 99%). Those average to something close to 5%. If we get strong confirmation that SARS-CoV-2 (or any other new pandemic) arose unnaturally, then my prior would go up. If we get many more natural pandemics without any unnatural ones, then my prior would go down. I would also incorporate other information. For example, if many more scientists undertake high-risk research on enhanced potential pandemic pathogens, then my prior would go up. If such projects are restricted and become less common, then my prior would go down. I think that's fairly normal reasoning?

YeGoblynQueenne 2y ago
>> I linked two papers, and that quote appears in the first. Perhaps you were looking at the second? Yes, that's the paper I looked at. "Not referenced" means that the 700k number given in the paper has no reference. The author seems to have pulled it out of thin air. Here I'm copying the text from your link with the highlight: >> By comparison, the 1977 "Russian flu" virus produced a milder illness in most people (Gregg, Hinman, and Craven 1978). Over the ensuing years, after its emergence in 1977, the virus went on to infect a significant portion of the worldwide population, killing roughly 700,000 people. But this particular pandemic isn't important only because of its impact on disease and death: it also reveals uncomfortable truths about the complex relationship between viruses and humans. The 1977 pandemic is a cautionary tale, one that—hopefully—we can learn from. Note there's no citation next to the 700k number. So where does it come from? Nowhere. The author just made it up. Perhaps that's the reason for the lack of concern at the 700k deaths: they didn't happen. And where did your 200k number come from? Can you show your work? >> If we get strong confirmation that SARS-CoV-2 (or any other new pandemic) arose unnaturally, then my prior would go up. That's not a prior, it's a posterior: it's the probability of an event given the evidence. We're interested in a prior probability when we don't have enough evidence to calculate a posterior. Like with Covid-19. Once more I suggest to try and get the terminology right so you don't confuse yourself and everyone else. It will also help you to find better ways to reason under uncertainty.
- tripletao 2y ago
  Sorry, I'd misunderstood what you meant by "referenced". The 700k is also reported in papers published before the one I linked, for example Table 1 of https://link.springer.com/article/10.1007/s00430-009-0118-5 https://link.springer.com/article/10.1007/s00430-009-0118-5 So the author didn't just make it up. Peer review isn't magic, but it does tend to catch such a blatant level of fraud; so it's odd that was your first assumption. He should have included the reference, but it's not uncommon to get sloppy when a result is widely reported. I haven't yet traced this number back to the epidemiological methodology. As noted, I fully accept that it might wrong, since influenza numbers tend to be calculated from excess mortality and not directly counted. Until we've reviewed that methodology, I agree that it's reasonable to question the exact number. I believe that uncertainty applies to most epidemiological estimates, including but not limited to the 1977 flu. But I've explicitly acknowledged the uncertainty, so I'm not sure why you're continuing to focus on this. There is no significant question that the 1977 flu was reintroduced into humans due to a research accident, and no question that it went on to infect a large fraction of the world. There's no question that many people died, even if the exact number is uncertain. Are you proposing that it's few enough that there's no reason to care? If so, what's your cutoff? The 200k was an arbitrary placeholder. I thought the phrase "or whatever the right number would be" following it would make that clear, but maybe not. > That's not a prior, it's a posterior: it's the probability of an event given the evidence. We're interested in a prior probability when we don't have enough evidence to calculate a posterior. Like with Covid-19. I mean that if SARS-CoV-2 were proven to have arisen unnaturally, then my prior that a different future pandemic was unnatural would go up. That again seems very normal to me, since humans predict the future using the past. As I noted, I would adjust my prior based on factors other than a simple count of past research-origin pandemics too; but the count is obviously relevant information. Your updated prior of p ~ 0.0001 to 0.00001 seems to also be derived from a past count, just reaching into prehistory to inflate the denominator. This thread started when you asserted that "every single pandemic and disease that has ever infected humans did not leak from a lab". Exact numbers for the death toll and prior aside, do you at least agree that's false?