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The Covid-19 epidemic has been with us for nearly a year, and I'm still having trouble understanding in simple terms just how contagious it is or isn't.

Lets say I have a 1 hour face-to-face conversation at a distance of 2 meters with someone with covid-19. To keep it simple we'll assume no face masks, and no physical contact. What are the odds of me catching Covid in that period? 0.1 percent? 1 percent? 10 percent? More? Less?

Or a different scenario - imagine 100 people in a room 100m by 100m randomly mingling (again, assume no masks and no physical contact). If one of those people has Covid-19, how many others are likely to have caught Covid after 1 hour?

I'm aware that there are many different factors in the spread of disease, age, health, behaviour etc but even so I amazed that I can find no real-world practical examples of just how likely I am to catch covid in different situations. I've seen a couple of statements describing things as 'low risk' or 'high risk' but never with numeric examples. For example spectating at a football match has been described as 'high risk' - but what does that mean? 1% chance of catching Covid? or 30% chance? Similarly visiting close family for 1 day over Xmas was described as 'low risk' but again no indication (in numbers) of what 'low' means.

Update - I'll admit I'm amazed by the apparent difficulty in answering this question (even with very approximate estimates), covid is currently the worlds number 1 problem, I would have expected this to have been computer modelled to hell and back by now, even if I can't get the answer to three decimal places, I'd have hoped to get an approximation to +-2%.

2nd Update - firstly, sorry if my tone upset anyone. Two more says of digging around and BrenBarns spreadsheet is the still the best/closest thing to an answer I've found. My request for a 'laypersons answer' is basically asking for something that a non-doctor or non-virologist can understand. As an engineer myself, I'm still looking for a science/math based answer, with some numbers attached, something a bit more explicit than just 'high risk' or 'low risk'

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    You don't see this because it isn't really possible to know. Science is based on experiments, but you can't experiment on this. There have been some "natural experiments" reported on where tracing was used to identify people who were infected based on their location in a room with infected people, but these are not controlled conditions and you can't get anything but suggestive conclusions from them. – Bryan Krause Jan 28 at 16:16
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    Also, ordinary people don't interpret risks well, a well known phenomenon in psychology. Releasing numbers like you are asking for would likely be a severe public health problem. – Bryan Krause Jan 28 at 16:17
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    I'm saying "it's not possible to do accurately and especially not in the detail you ask", and also "there isn't much motivation to attempt it". – Bryan Krause Jan 28 at 16:36
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    I've DV this because while you're emphasizing "layperson's terms" in the title, what you're really asking for are highly controlled experiments and probabilities, which aren't really layperson's terms. Also, you can't easily translate an experiment to another, so there's no way to say what's the chance of catching Covid at a football match even if you knew the exact probabilities from the previous two experiments you've described, experiments which by the way would have to be repeated numerous times to account for the variation in individual immunity. – Fizz Jan 28 at 19:53
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    You can "hope" for an approximation all you want, that doesn't make the problem any more simple, and I think the complexity should be readily apparent to a "layperson". I think the tone of your edits is quite rude. – Bryan Krause Jan 29 at 14:57
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The closest thing I have found to this is a tool called the COVID-19 Airborne Transmission Estimator. It is basically a spreadsheet on which you can tweak various parameters and get an estimate of the number of people infected at a given gathering. It was developed last summer by a professor at the University of Colorado; he is not a doctor but is a chemist who studies aerosols. Here is a page about his research group and here is a brief press release about the tool. As far as I can tell there has been no peer review of this model and it seems to be still quite provisional.

The Spanish newspaper El Pais wrote an article which describes some examples of risk based on the model. National Geographic also did an article showing some graphs of risk in various situations based on the model.

Those articles are useful because the spreadsheet itself can be somewhat overwhelming to use. The model requires values for certain parameters that the average person has no real knowledge about, such as the volume of air breathed in by a person in an hour and the rate at which the air in the space is replaced with fresh air from an external source; as well as parameters that are not definitively known or may be quite variable, such as the number of infectious doses of the virus exhaled per hour.

Thus the model comes with a major "garbage in, garbage out" caveat. Also, of course, it is not so much an analysis of experimental data as a mathematical model, and it relies on many simplifying assumptions. For instance, it does not consider the details of airflow within the space, although there is evidence that that can be important. Nonetheless, it is the only model I've seen that attempts to push the numbers all the way through to direct estimates of infection. The author is quoted in the National Geographic article as saying, "We do not have a ton of information, but we cannot afford to wait for a ton of information."

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    That's quite a spreadsheet, and it demonstrates rather nicely just how complex the problem is. Although the outputs are readily understood by a layman, the inputs sure aren't. – Carey Gregory Jan 29 at 21:06
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Utilizing a sprinkling of good will, perhaps, I find the general impetus (which I think I detect) behind your question quite pertinent — to the whole ordeal which nearly every layman and grocery shopper (etc.) in the world is going through right now, that is.

Models trying to account for in-situ transmissibility of a respiratory virus will of course allow for a vast morass of input variables, and quickly become very complex.

However, I don't see why it wouldn't be of great interest (to the "layperson", as it were) to be taken through a slew of comparable contact tracing case scenarios (from real world incidents, chosen for their comparability), which point to concrete, known infections that either corroborate or thwart expectations that have been formed in public consciousness — about what behavior straddles the spectrum from very safe to very unsafe behavior. Using as a guiding benchmark, here, for instance, what has been defined by various health authorities to be a "close contact" or not. (Less/more than 2 meters, various time frames, etc.)

Statistics, percentages and encroaching complexity aside, I am absolutely inclined to think that a robust accumulation of expert-curated case material here would be interesting. Not least if there are case studies that can speak to the new, allegedly more transmissible virus strains. I would've been quite willing to enter the thicket and do some work here for you, provided that such case data can be procured (One should think so, cf. "currently the world's number 1 problem") but I'm not even close to the kind of expert that can strike a good path here.

This is rather an attempt, I suppose, to turn your question at a somewhat different reflective angle. Does this seem a fair one?

And, if this is so, can anyone knowledgeable pick up the thread from here?

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    Hi, Valarien, welcome to Medical Sciences.SE. You might want to have a look at the help center and take the tour to get a better idea how this site works. Your answer is interesting and well written, but the way you ended it calling for more discussion is what you would find in a discussion forum, which this is not. It's a Q&A site, so discussion isn't really even possible. The closest I can come is to convert your answer to a wiki, which means that others can modify it and add on to it. – Carey Gregory Jan 31 at 0:06

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