| si {linelistBayes} | R Documentation |
Calculate Serial Interval Distribution for COVID-19
Description
This function computes the probability distribution function (PDF) of the serial interval for COVID-19 using a gamma distribution with specified shape and rate parameters. The serial interval is defined as the time between successive cases in a chain of transmission. This implementation generates a discrete PDF over a given number of days.
Usage
si(ndays, alpha, beta)
Arguments
ndays |
Integer, the number of days over which to calculate the serial interval distribution. |
alpha |
Numeric, the shape parameter of the gamma distribution. |
beta |
Numeric, the rate parameter of the gamma distribution. |
Details
The function uses the 'pgamma' function to calculate cumulative probabilities for each day up to 'ndays' and then differences these to get daily probabilities. The resulting probabilities are normalized to sum to 1, ensuring that they represent a valid probability distribution.
Value
Numeric vector representing the serial interval probabilities for each of the first 'ndays' days. The probabilities are normalized so that their sum is 1.
References
Nishiura, H., Linton, N. M., & Akhmetzhanov, A. R. (2020). Serial interval of novel coronavirus (COVID-19) infections. International Journal of Infectious Diseases, 93, 284-286. [Link to the article](https://www.sciencedirect.com/science/article/pii/S1201971220306111)
Examples
sip <- si(14, 4.29, 1.18)