Explore scenarios using gridding with sampling for parameters not in the grid. Parameters that
are included in the grid are currently hard coded. Use the future package to control parallisation
outside of the function.
parameter_sweep(scenarios, sim_fn, samples = 1)A nested data.table containing the parameters for each scenario and a nested list of output
from scenario_sim().
library(data.table)
# Put parameters that are grouped by disease into this data.table
scenarios <- data.table(
expand.grid(
delay_group = list(data.table(
delay = c("SARS", "Wuhan"),
onset_to_isolation = c(
\(x) rweibull(n = x, shape = 1.651524, scale = 4.287786),
\(x) rweibull(n = x, shape = 2.305172, scale = 9.483875)
)
)),
r0_community = c(1.1, 1.5),
prop_presymptomatic = c(0.01, 0.15),
prop_asymptomatic = c(0, 0.1),
prop_ascertain = seq(0, 1, 0.25),
initial_cases = c(5, 10),
quarantine = FALSE
)
)
list_cols <- grep("_group", colnames(scenarios), value = TRUE)
non_list_cols <- setdiff(colnames(scenarios), list_cols)
scenarios <- scenarios[, rbindlist(delay_group), by = c(non_list_cols)]
scenarios[, scenario := 1:.N]
#> r0_community prop_presymptomatic prop_asymptomatic prop_ascertain
#> <num> <num> <num> <num>
#> 1: 1.1 0.01 0.0 0
#> 2: 1.1 0.01 0.0 0
#> 3: 1.5 0.01 0.0 0
#> 4: 1.5 0.01 0.0 0
#> 5: 1.1 0.15 0.0 0
#> ---
#> 156: 1.5 0.01 0.1 1
#> 157: 1.1 0.15 0.1 1
#> 158: 1.1 0.15 0.1 1
#> 159: 1.5 0.15 0.1 1
#> 160: 1.5 0.15 0.1 1
#> initial_cases quarantine delay onset_to_isolation scenario
#> <num> <lgcl> <char> <list> <int>
#> 1: 5 FALSE SARS <function[1]> 1
#> 2: 5 FALSE Wuhan <function[1]> 2
#> 3: 5 FALSE SARS <function[1]> 3
#> 4: 5 FALSE Wuhan <function[1]> 4
#> 5: 5 FALSE SARS <function[1]> 5
#> ---
#> 156: 10 FALSE Wuhan <function[1]> 156
#> 157: 10 FALSE SARS <function[1]> 157
#> 158: 10 FALSE Wuhan <function[1]> 158
#> 159: 10 FALSE SARS <function[1]> 159
#> 160: 10 FALSE Wuhan <function[1]> 160
incub <- \(x) rweibull(n = x, shape = 1.65, scale = 4.28)
scenarios[, incubation_period := rep(list(incub), .N)]
#> r0_community prop_presymptomatic prop_asymptomatic prop_ascertain
#> <num> <num> <num> <num>
#> 1: 1.1 0.01 0.0 0
#> 2: 1.1 0.01 0.0 0
#> 3: 1.5 0.01 0.0 0
#> 4: 1.5 0.01 0.0 0
#> 5: 1.1 0.15 0.0 0
#> ---
#> 156: 1.5 0.01 0.1 1
#> 157: 1.1 0.15 0.1 1
#> 158: 1.1 0.15 0.1 1
#> 159: 1.5 0.15 0.1 1
#> 160: 1.5 0.15 0.1 1
#> initial_cases quarantine delay onset_to_isolation scenario
#> <num> <lgcl> <char> <list> <int>
#> 1: 5 FALSE SARS <function[1]> 1
#> 2: 5 FALSE Wuhan <function[1]> 2
#> 3: 5 FALSE SARS <function[1]> 3
#> 4: 5 FALSE Wuhan <function[1]> 4
#> 5: 5 FALSE SARS <function[1]> 5
#> ---
#> 156: 10 FALSE Wuhan <function[1]> 156
#> 157: 10 FALSE SARS <function[1]> 157
#> 158: 10 FALSE Wuhan <function[1]> 158
#> 159: 10 FALSE SARS <function[1]> 159
#> 160: 10 FALSE Wuhan <function[1]> 160
#> incubation_period
#> <list>
#> 1: <function[1]>
#> 2: <function[1]>
#> 3: <function[1]>
#> 4: <function[1]>
#> 5: <function[1]>
#> ---
#> 156: <function[1]>
#> 157: <function[1]>
#> 158: <function[1]>
#> 159: <function[1]>
#> 160: <function[1]>
## Parameterise fixed paramters
sim_with_params <- purrr::partial(
ringbp::scenario_sim,
cap_max_days = 365,
cap_cases = 5000,
r0_isolated = 0,
disp_isolated = 1,
disp_asymptomatic = 0.16,
disp_community = 0.16
)
## parameter_sweep uses the future_lapply() function
## Default is to run sequntially on a single core
# future::plan("sequential")
## Set up multicore if using see ?future::plan for details
## Use the workers argument to control the number of cores used.
# future::plan("multiprocess")
## Run parameter sweep
sweep_results <- parameter_sweep(
scenarios,
sim_fn = sim_with_params,
samples = 1
)
sweep_results
#> scenario data sims
#> <int> <list> <list>
#> 1: 1 <data.table[1x9]> [NULL]
#> 2: 50 <data.table[1x9]> [NULL]
#> 3: 88 <data.table[1x9]> [NULL]
#> 4: 66 <data.table[1x9]> [NULL]
#> 5: 145 <data.table[1x9]> [NULL]
#> ---
#> 156: 38 <data.table[1x9]> [NULL]
#> 157: 157 <data.table[1x9]> [NULL]
#> 158: 11 <data.table[1x9]> [NULL]
#> 159: 110 <data.table[1x9]> [NULL]
#> 160: 118 <data.table[1x9]> [NULL]