discr_si | R Documentation |
discr_si
computes the discrete distribution of the serial interval,
assuming that the serial interval is shifted Gamma distributed, with shift 1.
discr_si(k, mu, sigma)
k |
Positive integer, or vector of positive integers for which the discrete distribution is desired. |
mu |
A positive real giving the mean of the Gamma distribution. |
sigma |
A non-negative real giving the standard deviation of the Gamma distribution. |
Assuming that the serial interval is shifted Gamma distributed with mean
\mu
, standard deviation \sigma
and shift 1
,
the discrete probability w_k
that the serial interval is equal to
k
is:
w_k = kF_{\{\mu-1,\sigma\}}(k)+(k-2)F_{\{\mu-1,\sigma\}}
(k-2)-2(k-1)F_{\{\mu-1,\sigma\}}(k-1)\\
+(\mu-1)(2F_{\{\mu-1+\frac{\sigma^2}{\mu-1},
\sigma\sqrt{1+\frac{\sigma^2}{\mu-1}}\}}(k-1)-
F_{\{\mu-1+\frac{\sigma^2}{\mu-1},
\sigma\sqrt{1+\frac{\sigma^2}{\mu-1}}\}}(k-2)-
F_{\{\mu-1+\frac{\sigma^2}{\mu-1},
\sigma\sqrt{1+\frac{\sigma^2}{\mu-1}}\}}(k))
where F_{\{\mu,\sigma\}}
is the cumulative density function of a Gamma
distribution with mean \mu
and standard deviation \sigma
.
Gives the discrete probability w_k
that the serial interval is
equal to k
.
Anne Cori a.cori@imperial.ac.uk
Cori, A. et al. A new framework and software to estimate time-varying reproduction numbers during epidemics (AJE 2013).
overall_infectivity
, estimate_R
## Computing the discrete serial interval of influenza
mean_flu_si <- 2.6
sd_flu_si <- 1.5
dicrete_si_distr <- discr_si(seq(0, 20), mean_flu_si, sd_flu_si)
plot(seq(0, 20), dicrete_si_distr, type = "h",
lwd = 10, lend = 1, xlab = "time (days)", ylab = "frequency")
title(main = "Discrete distribution of the serial interval of influenza")
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