This function returns a keyed list of priors related to delays in diagnosing
of cases and deaths. Called with no arguments, the default values are
returned. Custom hyperpriors can be specified by passing values for the
parameters specified below. The return value of this function must be added
to a `covidestim`

object using the addition operator (see examples).

priors_diagnosis_delays_scale(dx_delay_sym = c(2, 2), dx_delay_sev = c(2, 2))

## Arguments

dx_delay_sym |
A two element vector containing the `c(alpha,beta)`
parameters/hyperpriors of a Beta distribution modeling a scaling factor.
Delay to diagnosis for symptomatic cases is modeled as the *fraction of
time in the symptomatic disease state*, scaled by this factor.
For instance, if `dx_delay_sym` is specified to have `E[dx_delay_sym] = 1/2` , this implies an assumption that individuals will be diagnosed
roughly halfway through the course of their symptomatic disease state - or
in other words, progress to diagnosis at twice the rate at which they
progress to severe infection, following transition into the symptomatic
disease state. |

dx_delay_sev |
A two element vector containing the `c(alpha,beta)`
parameters/hyperpriors of a Beta distribution modeling a scaling factor.
Delay to diagnosis for severe cases is modeled as the *fraction of time in
the severe disease state*, scaled by this factor.
Similarly to above, a scaling factor with `E[x] = 0.5` implies that
individuals get diagnosed, on average, halfway through time spent in the
severe disease state. |

## Value

An S3 object of class 'priors'

## Details

Boundary avoiding priors are used by default.

## Examples