Construct beta array

Construct the beta (contact rate) over time array for use within sircovid models.

sircovid_parameters_beta(date, value, dt)

Arguments

date	Either `NULL`, if one value of beta will be used for all time steps, or a vector of times that will be used as change points. Must be provided as a `sircovid_date()`, i.e., days into 2020.
value	A vector of values to use for beta - either a scalar (if `date` is `NULL`) or a vector the same length as `date`.
dt	The timestep that will be used in the simulation. This must be of the form `1 / n` where `n` is an integer representing the number of steps per day. Ordinarily this is set by sircovid internally to be `0.25` but this will become tuneable in a future version.

Value

Returns a vector of beta values, one per timestep, until the beta values stabilise. After this point beta is assumed to be constant.

Examples

# If "date" is NULL, then beta is constant and this function is
# trivial:
sircovid2::sircovid_parameters_beta(NULL, 0.1, 0.25)
#> [1] 0.1

date <- sircovid2::sircovid_date(
   c("2020-02-01", "2020-02-14", "2020-03-15"))
value <- c(3, 1, 2)
beta <- sircovid2::sircovid_parameters_beta(date, value, 1)

# The implied time series looks like this:
t <- seq(0, date[[3]])
plot(t, beta, type = "o")
points(date, value, pch = 19, col = "red")

# After 2020-03-15, the beta value will be fixed at 2, the value
# that it reached at that date.

# You can see this using sircovid_parameters_beta_expand
# If a vector of dates is provided then, it's more complex. We'll
# use dt of 1 here as it's easier to visualise
t <- seq(0, 100, by = 1)
sircovid2::sircovid_parameters_beta_expand(t, beta)
#>   [1] 3.000000 3.000000 3.000000 3.000000 3.000000 3.000000 3.000000 3.000000
#>   [9] 3.000000 3.000000 3.000000 3.000000 3.000000 3.000000 3.000000 3.000000
#>  [17] 3.000000 3.000000 3.000000 3.000000 3.000000 3.000000 3.000000 3.000000
#>  [25] 3.000000 3.000000 3.000000 3.000000 3.000000 3.000000 3.000000 3.000000
#>  [33] 3.000000 2.846154 2.692308 2.538462 2.384615 2.230769 2.076923 1.923077
#>  [41] 1.769231 1.615385 1.461538 1.307692 1.153846 1.000000 1.033333 1.066667
#>  [49] 1.100000 1.133333 1.166667 1.200000 1.233333 1.266667 1.300000 1.333333
#>  [57] 1.366667 1.400000 1.433333 1.466667 1.500000 1.533333 1.566667 1.600000
#>  [65] 1.633333 1.666667 1.700000 1.733333 1.766667 1.800000 1.833333 1.866667
#>  [73] 1.900000 1.933333 1.966667 2.000000 2.000000 2.000000 2.000000 2.000000
#>  [81] 2.000000 2.000000 2.000000 2.000000 2.000000 2.000000 2.000000 2.000000
#>  [89] 2.000000 2.000000 2.000000 2.000000 2.000000 2.000000 2.000000 2.000000
#>  [97] 2.000000 2.000000 2.000000 2.000000 2.000000
plot(t, sircovid2::sircovid_parameters_beta_expand(t, beta), type = "o")
points(date, value, pch = 19, col = "red")

# If dt is less than 1, this is scaled, but the pattern of beta
# change is the same
beta <- sircovid2::sircovid_parameters_beta(date, value, 0.5)
t <- seq(0, date[[3]], by = 0.5)
plot(t, beta, type = "o", cex = 0.25)
points(date, value, pch = 19, col = "red")

Arguments

Value

See also

Examples