Get stan data for ww + hosp model
Usage
get_stan_data(
input_count_data,
input_ww_data,
forecast_date,
forecast_horizon,
calibration_time,
generation_interval,
inf_to_count_delay,
infection_feedback_pmf,
params,
include_ww,
compute_likelihood = 1
)Arguments
- input_count_data
a dataframe of the count data to be passed directly to stan, , must have the following columns: date, count, total_pop
- input_ww_data
a dataframe of the wastewater data to be passed directly to stan, must have the following columns: date, site, lab, genome_copies_per_ml, site_pop, below_lod, and exclude
- forecast_date
string indicating the forecast date in ISO8601 convention e.g. YYYY-MM-DD
- forecast_horizon
integer indicating the number of days to make a forecast for
- calibration_time
integer indicating the max duration in days that the model is calibrated to the count data for
- generation_interval
a vector with a zero-truncated normalized pmf of the generation interval
- inf_to_count_delay
a vector with a normalized pmf of the delay from infection to counts
- infection_feedback_pmf
a vector with a normalized pmf dictating the delay of infection feedback
- params
a list mapping parameter names to their values
- include_ww
integer either 1 or 0 indicating whether to fit the wastewater data or not, default is 1
- compute_likelihood
indicator variable telling stan whether or not to compute the likelihood, default =
1
Examples
ww_data <- tibble::tibble(
date = rep(seq(
from = lubridate::ymd("2023-08-01"),
to = lubridate::ymd("2023-11-01"),
by = "weeks"
), 2),
site = c(rep(1, 14), rep(2, 14)),
lab = c(rep(1, 28)),
conc = abs(rnorm(28, mean = 500, sd = 50)),
lod = c(rep(20, 14), rep(15, 14)),
site_pop = c(rep(2e5, 14), rep(4e5, 14))
)
ww_data_preprocessed <- preprocess_ww_data(ww_data,
conc_col_name = "conc",
lod_col_name = "lod"
)
input_ww_data <- indicate_ww_exclusions(ww_data_preprocessed)
hosp_data <- tibble::tibble(
date = seq(
from = lubridate::ymd("2023-07-01"),
to = lubridate::ymd("2023-10-30"),
by = "days"
),
daily_admits = sample(5:70, 122, replace = TRUE),
state_pop = rep(1e6, 122)
)
input_count_data <- preprocess_count_data(
hosp_data,
"daily_admits",
"state_pop"
)
generation_interval <- to_simplex(c(0.01, 0.2, 0.3, 0.2, 0.1, 0.1, 0.01))
inf_to_count_delay <- to_simplex(c(
rep(0.01, 12), rep(0.2, 4),
rep(0.01, 10)
))
infection_feedback_pmf <- generation_interval
params <- get_params(
system.file("extdata", "example_params.toml",
package = "wwinference"
)
)
forecast_date <- "2023-11-06"
calibration_time <- 90
forecast_horizon <- 28
include_ww <- 1
input_count_data_for_stan <- get_input_count_data_for_stan(
input_count_data,
calibration_time
)
last_count_data_date <- max(input_count_data_for_stan$date, na.rm = TRUE)
first_count_data_date <- min(input_count_data_for_stan$date, na.rm = TRUE)
input_ww_data_for_stan <- get_input_ww_data_for_stan(
input_ww_data,
first_count_data_date,
last_count_data_date,
calibration_time
)
stan_data_list <- get_stan_data(
input_count_data_for_stan,
input_ww_data_for_stan,
forecast_date,
forecast_horizon,
calibration_time,
generation_interval,
inf_to_count_delay,
infection_feedback_pmf,
params,
include_ww
)
#> Prop of population size covered by wastewater: 0.6
#> Removed 0 outliers from WW data