This repository is under construction.
This repository is designed as an example modeling Hub that follows the infrastructure guidelines laid out by the Consortium of Infectious Disease Modeling Hubs.
The example model outputs that are provided here are adapted from
forecasts submitted to the FluSight Forecast Hub for the 2022/23 season.
The original forecasts were provided in quantile format, but they have
been modified to provide examples of additional model output types and
targets. They should be viewed only as illustrations of the data
formats, not as realistic examples of forecasts. Note that the folder
internal-data-raw is not a part of the standard hub setup; it contains
the original source data and scripts used to create the example model
output data and target data.
To work with the data in R, you can use code like the following:
library(hubUtils)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
model_outputs <- hubUtils::connect_hub(hub_path = ".") %>%
dplyr::collect()
head(model_outputs)
#> # A tibble: 6 × 9
#> location reference_date horizon target_end_date target output_type
#> <chr> <date> <int> <date> <chr> <chr>
#> 1 US 2022-10-22 0 2022-10-22 wk inc flu hosp quantile
#> 2 US 2022-10-22 0 2022-10-22 wk inc flu hosp quantile
#> 3 US 2022-10-22 0 2022-10-22 wk inc flu hosp quantile
#> 4 US 2022-10-22 0 2022-10-22 wk inc flu hosp quantile
#> 5 US 2022-10-22 0 2022-10-22 wk inc flu hosp quantile
#> 6 US 2022-10-22 0 2022-10-22 wk inc flu hosp quantile
#> # ℹ 3 more variables: output_type_id <chr>, value <dbl>, model_id <chr>
target_time_series_data <- read.csv(
"target-data/flu-hospitalization-time-series.csv")
head(target_time_series_data)
#> date location value
#> 1 2020-01-11 01 0
#> 2 2020-01-11 15 0
#> 3 2020-01-11 18 0
#> 4 2020-01-11 27 0
#> 5 2020-01-11 30 0
#> 6 2020-01-11 37 0
inc_flu_hosp_target_data <- read.csv(
"target-data/wk-inc-flu-hosp-target-values.csv")
head(inc_flu_hosp_target_data)
#> location reference_date horizon target_end_date target value
#> 1 01 2022-10-22 0 2022-10-22 wk inc flu hosp 141
#> 2 01 2022-10-22 1 2022-10-29 wk inc flu hosp 262
#> 3 01 2022-10-22 2 2022-11-05 wk inc flu hosp 360
#> 4 01 2022-10-22 3 2022-11-12 wk inc flu hosp 303
#> 5 01 2022-10-29 0 2022-10-29 wk inc flu hosp 262
#> 6 01 2022-10-29 1 2022-11-05 wk inc flu hosp 360
rate_category_target_data <- read.csv(
"target-data/wk-flu-hosp-rate-category-target-values.csv")
head(rate_category_target_data)
#> location reference_date horizon target_end_date target
#> 1 01 2022-10-22 0 2022-10-22 wk flu hosp rate category
#> 2 01 2022-10-22 1 2022-10-29 wk flu hosp rate category
#> 3 01 2022-10-22 2 2022-11-05 wk flu hosp rate category
#> 4 01 2022-10-22 3 2022-11-12 wk flu hosp rate category
#> 5 01 2022-10-29 0 2022-10-29 wk flu hosp rate category
#> 6 01 2022-10-29 1 2022-11-05 wk flu hosp rate category
#> output_type_id value
#> 1 moderate 1
#> 2 high 1
#> 3 high 1
#> 4 high 1
#> 5 high 1
#> 6 high 1