get_shape_R_flat: Precompute shape of posterior distribution for R

In annecori/EpiEstim: Estimate Time Varying Reproduction Numbers from Epidemic Curves

Precompute shape of posterior distribution for R

Description

Precompute shape of posterior distribution for R

Usage

get_shape_R_flat(incid, priors, t_min = 2L, t_max = nrow(incid))

Arguments

incid	a multidimensional array containing values of the (local) incidence for each time step (1st dimension), location (2nd dimension) and pathogen/strain/variant (3rd dimension)
priors	a list of prior parameters (shape and scale of a gamma distribution) for epsilon and R; can be obtained from the function 'default_priors'. The prior for R is assumed to be the same for all time steps and all locations
t_min	an integer >1 giving the minimum time step to consider in the estimation. Default value is 2 (as the estimation is conditional on observations at time step 1 and can therefore only start at time step 2).
t_max	an integer >'t_min' and <='nrow(incid)' giving the maximum time step to consider in the estimation. Default value is 'nrow(incid)'.

Value

a vector of the shape of the posterior distribution of R for each time step t and each location l (stored in element (l-1)*(t_max - t_min + 1) + t of the vector)

Examples

n_v <- 2
n_loc <- 3 # 3 locations
T <- 100 # 100 time steps
priors <- default_priors()
# constant incidence 10 per day everywhere
incid <- array(10, dim = c(T, n_loc, n_v))
get_shape_R_flat(incid, priors)

annecori/EpiEstim documentation built on Sept. 7, 2024, 7:34 p.m.