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> To get meaningfully constant values for infectiousness you'd probably need a theory that let you derive it from RNA sequences. As others have already noted i

> To get meaningfully constant values for infectiousness you'd probably need a theory that let you derive it from RNA sequences. As others have already noted in this thread, the rate at which a disease spreads inherently depends on both the biology of the pathogen and human behavior. For example, if everyone goes to crowded nightclubs, then R0 for an airborne respiratory disease goes up. If everyone stays home, then it goes down. The biology of the pathogen and human behavior interact in complex, multidimensional ways; promoting condoms will reduce R0 for HIV, but not for influenza. The disease spreads only through human behavior, so the concept of infectiousness independent of that simply doesn't exist. You are correct that R0 is determined by curve-fitting to actual case counts. It couldn't be otherwise though, since that's the only way to capture the actual human behavior. We expect different R0 in different situations, so the range you see for SARS-CoV-2 isn't a surprise. This is textbook stuff: > Any factor having the potential to influence the contact rate, including population density (e.g., rural vs. urban), social organization (e.g., integrated vs. segregated), and seasonality (e.g., wet vs. rainy season for vectorborne infections), will ultimately affect R0. Because R0 is a function of the effective contact rate, the value of R0 is a function of human social behavior and organization, as well as the innate biological characteristics of particular pathogens. More than 20 different R0 values (range 5.4–18) were reported for measles in a variety of study areas and periods (22), and a review in 2017 identified feasible measles R0 values of 3.7–203.3 (23). This wide range highlights the potential variability in the value of R0 for an infectious disease event on the basis of local sociobehavioral and environmental circumstances. https://wwwnc.cdc.gov/eid/article/25/1/17-1901_article https://wwwnc.cdc.gov/eid/article/25/1/17-1901_article As with your confused understanding of PCR specificity, you may have been misinformed by indefensibly oversimplified public messaging as to the definition of R0. (I had a comment deleted from /r/coronavirus in 2020, because they considered my statement that R0 varies with environment and human behavior to be misinformation; the citation didn't help.) The literature is once again available for anyone who wishes to read it, though.

mike_hearn 3y ago
No, I'm not confused about any of this. I'm giving the textbook definition in this thread, right? I'm sure even sure why you are arguing with me instead of the user who started the thread by giving specific R0 numbers for influenza and SARS-CoV-2. As you say, you can't do that because R0s are characteristics of outbreaks, not viruses. So the confusion is the other way around. An outsider to the field would think R0 would be defined biologically given it's called the "basic reproduction number" and because epidemiologists themselves regularly make claims like "influenza has an R0 of this and measles has an R0 of that", but it takes all of five minutes to discover that the way it's calculated can't support such statements. That's why as presently defined it's useless. If you can't compare the values with any other value, what are they for? Put another way, claims about R0 aren't falsifiable. Epidemiology needs to develop far more robust methods that aren't just applying R-the-software to random datasets scraped from the web if it wants to be taken seriously as a field. There are very basic philosophy of science issues here. Argument by textbook gets us nowhere, because the textbooks are themselves written by people engaged in unscientific practices. The expectation by outsiders is reasonable, the actual way things operate isn't. Therefore, my suggestion - meant constructively! - is to rebase the field on top of microbiological theory. Scrap the models for now. Delete "and everything else" from the R0 definition and come up with an algorithm to compute a measure of infectiousness from DNA/RNA or lab experiments only. Once you've got a base definition that lets different labs replicate each other's numbers, you can start to incorporate other aspects (under new variable names) like immune system strength, population density grids etc. Such definitions won't let you give governments predictions of hospital bed demand right away, and indeed may not let you calculate much of real world value for a while, but the field isn't able to do that successfully today anyway. COVID models were all well off reality. What it would do though, is put epidemiology on track to one day deliver accurate results based on a firm theoretical foundation.
- tripletao 3y ago
  The suggestion that SARS-CoV-2 spreads faster than influenza because two studies on different populations at different times found R0 of 2.5 vs. 1.7 respectively is indeed false--that difference is obviously within the expected spread from different environments. I thought the top reply to that comment (quoting Wikipedia) clearly implied that, so I didn't think any further effort there was required. You posted other statements that were false in different ways, so I responded to those. That said, SARS-CoV-2 really does spread faster than influenza; among other reasons, we know this because influenza cases went almost to zero during the pandemic, implying that the same behavior in the same population that clearly resulted in R0 > 1 for SARS-CoV-2 resulted in R0 < 1 for influenza. That's a relatively trivial and obvious claim, but it's falsifiable and it involves R0. It's pretty common to reduce a time series to a single number. For example, in economics, it's common to look at a compound growth rate per year, averaged over the period of interest. Likewise, in epidemiology, it's common to look at a compound growth rate per estimated serial interval, averaged over the outbreak and corrected for immunity acquired during the outbreak. That's R0, with all the convenience and all the flaws of any other simple aggregate statistic. > Therefore, my suggestion - meant constructively! - is to rebase the field on top of microbiological theory. Scrap the models for now. Delete "and everything else" from the R0 definition and come up with an algorithm to compute a measure of infectiousness from DNA/RNA or lab experiments only. I hope you realize that biologists aren't all stupid? If they could somehow define "infectiousness of the pathogen alone, without environmental factors", then that would be incredibly useful, removing all the factors that complicate comparisons of R0. The fact that they've made no attempt to do so should be a clue that the concept that you're wishing for simply doesn't exist. They do study growth rates in cell culture, or the amount of virus exhaled by a sick lab animal, or the amount of virus that a healthy lab animal must inhale to get infected with some probability. Those are well-defined and somewhat repeatable lab measurements, but they're not very predictive of spread in actual humans. Computational methods are even less predictive; the idea of calculating infectiousness in humans from a viral genome is mostly science fiction for now. They're trying, but this may be harder than you think.
  - mike_hearn 3y ago
    > we know this because influenza cases went almost to zero during the pandemic, implying that the same behavior in the same population We don't know this no, what you said here is just an assumption. There's a competing hypothesis (viral interference) which seems to explain the data better. The way epidemiology currently works just cannot tell us which virus is more infectious. I agree that a rebase onto microbiology would be hard and maybe fail but the current approach has already failed. Doing difficult research that might not pan out is how they justify grant funding in the first place.

mike_hearn 3y ago

No, I'm not confused about any of this. I'm giving the textbook definition in this thread, right? I'm sure even sure why you are arguing with me instead of the user who started the thread by giving specific R0 numbers for influenza and SARS-CoV-2. As you say, you can't do that because R0s are characteristics of outbreaks, not viruses. So the confusion is the other way around. An outsider to the field would think R0 would be defined biologically given it's called the "basic reproduction number" and because epidemiologists themselves regularly make claims like "influenza has an R0 of this and measles has an R0 of that", but it takes all of five minutes to discover that the way it's calculated can't support such statements. That's why as presently defined it's useless. If you can't compare the values with any other value, what are they for? Put another way, claims about R0 aren't falsifiable. Epidemiology needs to develop far more robust methods that aren't just applying R-the-software to random datasets scraped from the web if it wants to be taken seriously as a field. There are very basic philosophy of science issues here. Argument by textbook gets us nowhere, because the textbooks are themselves written by people engaged in unscientific practices. The expectation by outsiders is reasonable, the actual way things operate isn't. Therefore, my suggestion - meant constructively! - is to rebase the field on top of microbiological theory. Scrap the models for now. Delete "and everything else" from the R0 definition and come up with an algorithm to compute a measure of infectiousness from DNA/RNA or lab experiments only. Once you've got a base definition that lets different labs replicate each other's numbers, you can start to incorporate other aspects (under new variable names) like immune system strength, population density grids etc. Such definitions won't let you give governments predictions of hospital bed demand right away, and indeed may not let you calculate much of real world value for a while, but the field isn't able to do that successfully today anyway. COVID models were all well off reality. What it would do though, is put epidemiology on track to one day deliver accurate results based on a firm theoretical foundation.

tripletao 3y ago
The suggestion that SARS-CoV-2 spreads faster than influenza because two studies on different populations at different times found R0 of 2.5 vs. 1.7 respectively is indeed false--that difference is obviously within the expected spread from different environments. I thought the top reply to that comment (quoting Wikipedia) clearly implied that, so I didn't think any further effort there was required. You posted other statements that were false in different ways, so I responded to those. That said, SARS-CoV-2 really does spread faster than influenza; among other reasons, we know this because influenza cases went almost to zero during the pandemic, implying that the same behavior in the same population that clearly resulted in R0 > 1 for SARS-CoV-2 resulted in R0 < 1 for influenza. That's a relatively trivial and obvious claim, but it's falsifiable and it involves R0. It's pretty common to reduce a time series to a single number. For example, in economics, it's common to look at a compound growth rate per year, averaged over the period of interest. Likewise, in epidemiology, it's common to look at a compound growth rate per estimated serial interval, averaged over the outbreak and corrected for immunity acquired during the outbreak. That's R0, with all the convenience and all the flaws of any other simple aggregate statistic. > Therefore, my suggestion - meant constructively! - is to rebase the field on top of microbiological theory. Scrap the models for now. Delete "and everything else" from the R0 definition and come up with an algorithm to compute a measure of infectiousness from DNA/RNA or lab experiments only. I hope you realize that biologists aren't all stupid? If they could somehow define "infectiousness of the pathogen alone, without environmental factors", then that would be incredibly useful, removing all the factors that complicate comparisons of R0. The fact that they've made no attempt to do so should be a clue that the concept that you're wishing for simply doesn't exist. They do study growth rates in cell culture, or the amount of virus exhaled by a sick lab animal, or the amount of virus that a healthy lab animal must inhale to get infected with some probability. Those are well-defined and somewhat repeatable lab measurements, but they're not very predictive of spread in actual humans. Computational methods are even less predictive; the idea of calculating infectiousness in humans from a viral genome is mostly science fiction for now. They're trying, but this may be harder than you think.
- mike_hearn 3y ago
  > we know this because influenza cases went almost to zero during the pandemic, implying that the same behavior in the same population We don't know this no, what you said here is just an assumption. There's a competing hypothesis (viral interference) which seems to explain the data better. The way epidemiology currently works just cannot tell us which virus is more infectious. I agree that a rebase onto microbiology would be hard and maybe fail but the current approach has already failed. Doing difficult research that might not pan out is how they justify grant funding in the first place.