looking for the probability of infection (for Covid19) vs the number of days to the onset of symptoms I have been searching for a graph of the probability of infection (for covid 19) vs the number of days to the onset of symptoms. The following graph is taken from Namilae, et al (Multiscale model for pedestrian and infection dynamics during air travel): 
The above figure is for Ebola, adapted from CDC website.
I was thinking that at this stage of the pandemic, we would have this kind of data right now. Why would something like this not yet available after millions already infected?
Any insights? (Or anyone who has the lead to the probability of infection for covid?)
 A: Different types of intervals
There are several time periods that can be identified when person 'a' infects person 'b' (respectively the infector and infectee).

*

*Serial interval: Time difference between symptoms of infector and infectee.

*Generation interval: Time difference between infection times of infector and infectee

*Incubation period: Time between getting infected and first symptoms.


Serial interal
Often you see articles about the 'serial interval' (the symptoms are easier to track/observe than the time of infection). For covid-19 there are numerous articles that make estimates of this epidemiological parameter based on some model and set of observations.
For example, Ganyani et.el "Estimating the generation interval for COVID-19 based on symptom onset data" Euro Surveill. 2020 Apr 30
Computing infectiousness
In the article from Ganyani they have estimated the serial interval and the incubation time separately. They assume that these follow a gamma distribution.
Time distribution of infections relative to 1st symptoms in jnfector If X is the serial time and if Y is the incubation time for the infector, then  X-Y is the time between infection of the infectee and the first symptoms of the infector (and this can be negative).
To get to the distribution of these times you can convolve the two gamma distributions (if the assumption is that X and Y are independent, which they often do in these models)
Infectiousness the above gives a time distribution of the infections (and sums up to 1). To get to the probability of infecting somebody you need to multiply this with the total number of people that get infected by somebody that is already infected. The $R_0$ or $R_t$ value.

Note that these distributions are constantly changing in time due to changes in measures like social distancing, increase (and simultaneously decrease) in immunity and possibly weather or other factors that change our health or the spreading potential of the virus.
The article from Ganyani estimates the curve by looking at data about infections and most others do the same. The article about ebola made the curve by using information about blood serum levels of the virus over the course of time (this sounds more rigorous, but note that this is based on very simple assumptions how blood serum levels relate to infection probability).
