# How contagious is Covid-19 - in laypersons terms?

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'

• 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. Commented Jan 28, 2021 at 16:16
• 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. Commented Jan 28, 2021 at 16:17
• 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". Commented Jan 28, 2021 at 16:36
• 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. Commented Jan 28, 2021 at 19:53
• 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. Commented Jan 29, 2021 at 14:57

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."

• 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. Commented Jan 29, 2021 at 21:06
• Most of the parameters are about the environment (air-flow, masks, etc.) not about the properties of the specific disease. What would be more useful is a series of comparisons, each with identical environments and only the characteristics of the virus changed (e.g. fix the environment, and compare the spread of COVID-19, influenza, common cold, etc.). This would definitely put the results into layman's terms (e.g. in one environment COVID-19 spreads twice as fast as the other diseases, while in another environment it spreads at half the rate). Commented Aug 29, 2021 at 8:14

I'll note that a more recent "room simulation" paper was published in Science (a top journal). This paper was basically based on a Fangcang Hospital simulation (200 people in a 500m2 x 10m high hall--see supplementary material) and lots of data on infectious dose estimates, mask filtration efficiency etc., but neither the authors of the paper nor the Science editors could conclude from it something "in layperson's terms" about some other setting like a restaurant, a bus etc. The editors' best attempt at summarizing the results were something like:

In indoor settings, it is impossible to avoid breathing in air that someone else has exhaled, and in hospital situations where the virus concentration is the highest, even the best-performing masks used without other protective gear such as hazmat suits will not provide adequate protection.

Meh. A slightly more daring simulation study (in terms of conclusions) was published in PNAS. They offered this summary graph for a classroom and respectively a nursing home:

Basically such models are informed (by real-world parameter estimates) but not really calibrated, since you can't easily check their predictions conform to some experimental results.

I'm personally pretty skeptical of the predictions in this latter (PNAS) study. It almost looks engineered to predict that a classroom is safe without masks at normal occupancy (6ft distance) and natural ventilation, for the duration of a fairly normal school day (6-7 hrs)... and with (cloth) masks, basically indefinitely. The paper's statement:

For normal occupancy and without masks, the safe time after an infected individual enters the classroom is 1.2 h for natural ventilation and 7.2 h with mechanical ventilation, according to the transient bound, SI Appendix, Eq. S8. Even with cloth mask use (pm=0.3), these bounds are increased dramatically, to 8 and 80 h, respectively. Assuming 6 h of indoor time per day, a school group wearing masks with adequate ventilation would thus be safe for longer than the recovery time for COVID-19 (7 d to 14 d), and school transmissions would be rare. We stress, however, that our predictions are based on the assumption of a “quiet classroom”, where resting respiration (Cq=30) is the norm. Extended periods of physical activity, collective speech, or singing would lower the time limit by an order of magnitude (Fig. 2).

But a recent CDC case study found that a teacher who at least occasionally didn't wear a mask when lecturing very likely was the source of an outbreak, infecting (in two days) all the children in the front row, and some/fewer in the back, in a nearly similar setup, i.e. 6ft distance [supposedly] maintained, even though the children supposedly were wearing masks, and the windows were open; this is with the delta variant though.