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Try this calculator. I don’t know if they have updated it with options to account for increased infectiveness of the delta variant or for the protective effects

Try this calculator. I don’t know if they have updated it with options to account for increased infectiveness of the delta variant or for the protective effects of masks and vaccines since I found and submitted it here though. https://www.mpg.de/16015780/corona-covid-19-aerosol-infection https://www.mpg.de/16015780/corona-covid-19-aerosol-infectio...

mech422 5y ago
Thanks - but I'm actually looking for how to solve problems like this myself and google fodder to look for more info. I think this is some sort of probability, but I'm not sure how the multi-hour stuff plays in...
- Someone 5y ago
  You start with a model. Depending on how good you want to match real-world data, that may involve answering questions such as - if somebody sneezes, how many virus particles get out? (may depend on person’s age, mask-wearing, etc) - how far (may depend on person’s height, lung volume, etc) - how long do they survive? (may depend on temperature, humidity, etc) - how likely are they to get to uninfected people? (may depend on what people are there. Kids in a kindergarten, for example, tend to get closer together than workers in an office) As you can see, that can get complex fairly fast. If you want it simpler, you can start with such things as - If you’re in a room with P persons who are spreading virus for an hour (or a minute, a second, etc), how many virus particles will you receive? - What’s the probability you’ll become ill if you receive P virus particles in a given time period? - if you become ill, how serious will it be? - if you receive P particles in a time period and don’t get ill, and repeat that immediately, is the probability of getting ill in that second period the same as in the first, higher, or lower? You can make each as simple or complex as you want. Once you have a model, calculations can be easy or very complex. That’s what the spreadsheet given in that link does for one model. If you’ve done the calculations you’re not done. You have to validate your model to know whether the numbers it spits out actually make sense.
  - mech422 5y ago
    >>if you receive P particles in a time period and don’t get ill, and repeat that immediately, is the probability of getting ill in that second period the same as in the first, higher, or lower? This is the part I was trying to figure out how to compute. I think the binomial distribution stuff deals with that.. eg how to calculate an 'overall' probability from n trials with p probability each. But I'll have to read up more on it
    - Someone 5y ago
      That depends on how you model infection. If you treat it as gunshots, that is: if you didn’t get hit by the infection in the first time period, you’re 100% healthy, and just as likely to get hit in the second time period, the probability of surviving n trials each of probability p of getting infected is 1 - (1-p)^n. That’s a simple model, though. Another model may have your resistance decrease after being ‘attacked’ by the virus in the first period (as a boxer who gets knocked out in round 10 due to a blow that wouldn’t have hurt him in round 1), and/or it increasing (I would guess over longer time periods) because your body develops some resistance. Such models are likely better in the sense that they can better predict what happens, but also more complex, and their parameters may be harder to estimate.
      - mech422 5y ago
        thanks - those were very understandable examples!

mech422 5y ago

Thanks - but I'm actually looking for how to solve problems like this myself and google fodder to look for more info. I think this is some sort of probability, but I'm not sure how the multi-hour stuff plays in...

Someone 5y ago
You start with a model. Depending on how good you want to match real-world data, that may involve answering questions such as - if somebody sneezes, how many virus particles get out? (may depend on person’s age, mask-wearing, etc) - how far (may depend on person’s height, lung volume, etc) - how long do they survive? (may depend on temperature, humidity, etc) - how likely are they to get to uninfected people? (may depend on what people are there. Kids in a kindergarten, for example, tend to get closer together than workers in an office) As you can see, that can get complex fairly fast. If you want it simpler, you can start with such things as - If you’re in a room with P persons who are spreading virus for an hour (or a minute, a second, etc), how many virus particles will you receive? - What’s the probability you’ll become ill if you receive P virus particles in a given time period? - if you become ill, how serious will it be? - if you receive P particles in a time period and don’t get ill, and repeat that immediately, is the probability of getting ill in that second period the same as in the first, higher, or lower? You can make each as simple or complex as you want. Once you have a model, calculations can be easy or very complex. That’s what the spreadsheet given in that link does for one model. If you’ve done the calculations you’re not done. You have to validate your model to know whether the numbers it spits out actually make sense.
- mech422 5y ago
  >>if you receive P particles in a time period and don’t get ill, and repeat that immediately, is the probability of getting ill in that second period the same as in the first, higher, or lower? This is the part I was trying to figure out how to compute. I think the binomial distribution stuff deals with that.. eg how to calculate an 'overall' probability from n trials with p probability each. But I'll have to read up more on it
  - Someone 5y ago
    That depends on how you model infection. If you treat it as gunshots, that is: if you didn’t get hit by the infection in the first time period, you’re 100% healthy, and just as likely to get hit in the second time period, the probability of surviving n trials each of probability p of getting infected is 1 - (1-p)^n. That’s a simple model, though. Another model may have your resistance decrease after being ‘attacked’ by the virus in the first period (as a boxer who gets knocked out in round 10 due to a blow that wouldn’t have hurt him in round 1), and/or it increasing (I would guess over longer time periods) because your body develops some resistance. Such models are likely better in the sense that they can better predict what happens, but also more complex, and their parameters may be harder to estimate.
    - mech422 5y ago
      thanks - those were very understandable examples!