View source: R/function_fordistribution.R
d_reproductiondistn | R Documentation |
This function (i.e., d_reproductiondistn()
) is the probability density function (PDF) of individual reproduction number that was modelled as a shifted gamma distribution.
d_reproductiondistn(
x = 1,
epi.para = list(mean = 1, disp = 0.5, shift = 0.2),
offspring.type = "D",
is.log = FALSE
)
x |
A scalar, or a vector of non-negative integer. |
epi.para |
A list ( |
offspring.type |
A character label (
By default, |
is.log |
A logical variable, under which probability would be taken natural logarithm, if |
d_reproductiondistn()
is the probability density function (PDF), and it returns value of probability density (non-negative value).
Only the PDF of individual reproduction number (i.e., d_reproductiondistn()
) was created here (without cumulative distribution, quantile, or random variable generating functions).
The function d_reproductiondistn()
was used mainly for data visualization purpose (rather than using for analysis),
because the distribution of individual reproduction number was not explicitly used in model fitting to the disease contact tracing data.
When offspring.type = "P"
, individual reproduction number follows a Dirac delta distribution, which is difficult to return any value, or visualize the PDF, because of the nature of this pulse function.
Lloyd-Smith JO, Schreiber SJ, Kopp PE, Getz WM. Superspreading and the effect of individual variation on disease emergence. Nature. 2005;438(7066):355-359. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1038/nature04153")}
Zhao S, Chong MK, Ryu S, Guo Z, He M, Chen B, Musa SS, Wang J, Wu Y, He D, Wang MH. Characterizing superspreading potential of infectious disease: Decomposition of individual transmissibility. PLoS Computational Biology. 2022;18(6):e1010281. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1371/journal.pcbi.1010281")}
## an example to visualize individual reproduction number is as follows.
plot(seq(0.01,9.99, length.out = 1001), d_reproductiondistn(
x = seq(0,10, length.out = 1001),
epi.para = list(mean = 2, disp = 1.5, shift = 0.5),
offspring.type = "D",
is.log = FALSE
), type = 'l', xlab = 'individual reproduction number', ylab = 'density')
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