Closed stephenturner closed 2 years ago
heads up i started working on this over at fluforce-init before i realized there was a scratch dir in this repo.
heres where i am so far:
https://github.com/signaturescience/fluforce-init/blob/main/fiphde.R
can use the auto ARIMA to model => forecast ILI variable. then can pass that in a list of models to use in the glm framework.
code is very thinly documented. some values hardcoded in there. a lot more work to do. but wanted you to be aware so we dont duplicate effort.
I added some of the utils from focustools, but updated, in #8, withj more flexibility. make_tsibble
used to require epiyear and epiweek hard-coded variable names. It's more flexible.
You no longer need to mutate in the epiyear/week (https://github.com/signaturescience/fluforce-init/blob/f8b985c0c6eb5b96bcf32f9f7a568e0265ec50c6/fiphde.R#L17-L18) because the cdcfluview query brings that in (as year
and week
)
> library(fiphde)
> ilidat <- get_cdc_ili(region="national", years=2019:2021)
Latest week_start / year / epiweek available:
2021-12-05 / 2021 / 49
> ilidat %>% make_tsibble(epiyear=year, epiweek=week, chop=FALSE)
# A tsibble: 115 x 15 [1W]
# Key: location [1]
location region_type abbreviation region year week monday yweek week_start weighted_ili unweighted_ili ilitotal
<chr> <chr> <chr> <chr> <int> <int> <date> <week> <date> <dbl> <dbl> <dbl>
1 US National US US 2019 40 2019-09-30 2019 W40 2019-09-29 1.49 1.50 21916
2 US National US US 2019 41 2019-10-07 2019 W41 2019-10-06 1.59 1.60 22954
3 US National US US 2019 42 2019-10-14 2019 W42 2019-10-13 1.73 1.74 24886
4 US National US US 2019 43 2019-10-21 2019 W43 2019-10-20 1.83 1.86 27419
5 US National US US 2019 44 2019-10-28 2019 W44 2019-10-27 2.04 2.01 28910
6 US National US US 2019 45 2019-11-04 2019 W45 2019-11-03 2.39 2.36 34586
7 US National US US 2019 46 2019-11-11 2019 W46 2019-11-10 2.63 2.64 37732
8 US National US US 2019 47 2019-11-18 2019 W47 2019-11-17 2.90 2.97 44161
9 US National US US 2019 48 2019-11-25 2019 W48 2019-11-24 3.42 3.45 44044
10 US National US US 2019 49 2019-12-02 2019 W49 2019-12-01 3.26 3.31 48904
# … with 105 more rows, and 3 more variables: num_of_providers <dbl>, total_patients <dbl>, population <dbl>
Running through that code, some of the "weird this works need fix in focustools" bits... Pete you and I should chat about whether we want to try to fix focustools and import that package, or (my preference (I think?)) copy over functionality we need and fix it here.
The functions implemented in #3 bring in ILI data from ILINET. Note that this data is never up to date with the most recent weeks. We'll need to forecast ILI data at least a few weeks in advance if we want to use this as a predictor in any kind of GLM for hospitalization.
See some of the focustools utils and forecasting functions.