forecast ILI

DESCRIPTION

@@ -32,6 +32,8 @@ RoxygenNote: 7.1.2
 Imports: 
     cdcfluview,
     dplyr,
+    fable,
+    fabletools,
     lubridate,
     magrittr,
     MMWRweek,

NAMESPACE

@@ -1,6 +1,7 @@
 # Generated by roxygen2: do not edit by hand
 
 export("%>%")
+export(forecast_ili)
 export(get_cdc_hosp)
 export(get_cdc_ili)
 export(get_cdc_vax)

R/fiphde.R

@@ -48,6 +48,8 @@ if(getRversion() >= "2.15.1")  utils::globalVariables(c(".",
                                                         "sea_label",
                                                         "monday",
                                                         "yweek",
+                                                        "weighted_ili",
+                                                        ".mean",
                                                         "0.025",
                                                         "0.975",
                                                         "<NA>",
@@ -63,5 +65,12 @@ if(getRversion() >= "2.15.1")  utils::globalVariables(c(".",
                                                         "truth",
                                                         "rmse",
                                                         "value",
+                                                        "unweighted_ili",
+                                                        "ili",
+                                                        "miss",
+                                                        "total",
+                                                        "pmiss",
+                                                        "arima",
+                                                        "x",
                                                         "."))
 

R/forecast.R

@@ -0,0 +1,145 @@
+#' @title Forecast ILI
+#' @description Forecasts ILI up to specified weeks in the future. Used in downstream modeling.
+#' @details Currently limited to one location only.
+#' @param ilidat Data returned from [get_cdc_ili].
+#' @param horizon Optional horizon periods through which the forecasts should be generated; default is `4`
+#' @param trim_date Earliest start date you want to use for ILI data. Default `NULL` doesn't trim.
+#' @param constrained Should the model be constrained to a non-seasonal model? If `TRUE` the parameter space defined in "param_space" argument will be used. See [fable::ARIMA].
+#' @param param_space Named list for ARIMA parameter space constraint; only used if "constrained == `TRUE`"; default is `list(P=0,D=0,Q=0,p=1:2,d=0:2,0)`, which sets space to PDQ(0,0,0) and pdq(1:2,0:2,0).
+#' @return A named list containing:
+#' 1. `ilidat`: The data sent into the function filtered to the location and the `trim_date`. Select columns returned.
+#' 1. `ilidat_tsibble`: The `tsibble` class object returned by running [make_tsibble] on the data above.
+#' 1. `ili_fit`: The fit from [fabletools::model].
+#' 1. `ili_forecast`: The forecast from [fabletools::forecast] at the specified horizon.
+#' 1. `ili_future`: The `horizon`-number of weeks of ILI data forecasted into the future.
+#' 1. `ili_bound`: The data in 1 bound to the data in 5.
+#' 1. `arima_params`: A tibble with ARIMA model parameters for each location.
+#' 1. `locstats`: A tibble with missing data information on all locations.
+#' 1. `removed`: A tibble with locations removed because of high missing ILI data.
+#' @examples
+#' \dontrun{
+#' # Get data
+#' ilidat <- get_cdc_ili(region=c("national", "state"), years=2010:lubridate::year(lubridate::today()))
+#'
+#' # Using data only from march 2020 forward, for US only
+#' ilidat_us <- ilidat %>% dplyr::filter(location=="US")
+#' ilifor_us <- forecast_ili(ilidat_us, horizon=4L, trim_date="2020-03-01")
+#' ilifor_us$ili_fit
+#' ilifor_us$arima_params
+#' ilifor_us$ili_forecast
+#' head(ilifor_us$ili_bound)
+#' tail(ilifor_us$ili_bound, 10)
+#' # Plot
+#' library(dplyr)
+#' library(ggplot2)
+#' theme_set(theme_classic())
+#' ilifor_us$ili_bound %>%
+#'  mutate(date=cdcfluview::mmwr_week_to_date(epiyear, epiweek)) %>%
+#'  filter(date>"2021-03-01") %>%
+#'  ggplot(aes(date, ili)) +
+#'  geom_line(lwd=.3, alpha=.5) +
+#'  geom_point(aes(col=forecasted), size=2)
+#'
+#' # At the state level
+#' ilifor_st <- forecast_ili(ilidat, horizon=4L, trim_date="2020-03-01")
+#' ilifor_st$ili_fit
+#' ilifor_st$arima_params
+#' ilifor_st$ili_forecast
+#' head(ilifor_us$ili_bound)
+#' tail(ilifor_us$ili_bound, 10)
+#' # Plot
+#' library(dplyr)
+#' library(ggplot2)
+#' theme_set(theme_classic())
+#' ilifor_st$ili_bound %>%
+#'   mutate(date=cdcfluview::mmwr_week_to_date(epiyear, epiweek)) %>%
+#'   filter(date>"2021-08-01") %>%
+#'   ggplot(aes(date, ili, col=forecasted)) +
+#'   geom_line(lwd=.3) +
+#'   geom_point(aes(col=forecasted), size=.7) +
+#'   facet_wrap(~abbreviation, scale="free_y")
+#' }
+#' @export
+forecast_ili <- function(ilidat, horizon=4L, trim_date=NULL, constrained=TRUE, param_space = list(P=0,D=0,Q=0,p=1:2,d=0:2,q=0)) {
+
+  # If trim_date is not null, trim to selected trim_date
+  if (!is.null(trim_date)) {
+    ilidat <-
+      ilidat %>%
+      dplyr::filter(week_start > as.Date(trim_date, format = "%Y-%m-%d"))
+  }
+
+  # Select just the columns you care about, and call "ili" the measure you're using
+  ilidat <-
+    ilidat %>%
+    dplyr::select(location, epiyear, epiweek, ili=unweighted_ili)
+
+  # Get missing data rates
+  locstats <- ilidat %>%
+    dplyr::group_by(location) %>%
+    dplyr::summarize(miss=sum(is.na(ili)), total=dplyr::n()) %>%
+    dplyr::mutate(pmiss=miss/total) %>%
+    dplyr::arrange(dplyr::desc(pmiss)) %>%
+    dplyr::mutate(remove=pmiss>.1)
+
+  # Get locations that will be removed
+  removed <- locstats %>%
+    dplyr::filter(remove) %>%
+    dplyr::inner_join(locations, by="location")
+  if(nrow(removed)>0) message(sprintf("Removed %s row(s) because of missing data. See result$removed.", nrow(removed)))
+
+  # Remove those locations
+  ilidat <- locstats %>%
+    dplyr::filter(!remove) %>%
+    dplyr::distinct(location) %>%
+    dplyr::inner_join(ilidat, by="location")
+
+  ## make a tsibble. do not chop the last week - because this is weekly data we won't have an incomplete final week
+  ilidat_tsibble <-
+    ilidat %>%
+    make_tsibble(epiyear = epiyear, epiweek = epiweek, key=location, chop=FALSE)
+
+  # Defaults to constrained, non-seasonal model.
+  if (constrained) {
+    # Nonseasonal fit: PDQ(0, 0, 0)
+    # Nonseasonal components unrestricted: pdq(0:5,0:5,0:5)
+    message("Fitting nonseasonal constrained ARIMA model...")
+    ili_fit <- fabletools::model(ilidat_tsibble,
+                                 arima = fable::ARIMA(ili ~ PDQ(param_space$P,param_space$D,param_space$Q) + pdq(param_space$p,param_space$d, param_space$q),
+                                                      stepwise=FALSE,
+                                                      approximation=FALSE))
+  } else {
+    # If unconstrained, need to set stepwise=TRUE and approxmiation=NULL to speed up.
+    message("Fitting unconstrained ARIMA model...")
+    ili_fit <- fabletools::model(ilidat_tsibble,
+                                 arima = fable::ARIMA(ili,
+                                                      stepwise=TRUE,
+                                                      approximation=NULL))
+  }
+
+  arima_params <-
+    ili_fit %>%
+    dplyr::mutate(x=purrr::map(arima, ~purrr::pluck(., "fit") %>% purrr::pluck("spec"))) %>%
+    tidyr::unnest_wider(col=x) %>%
+    dplyr::select(-arima)
+
+  # Get the forecast
+  ili_forecast <- fabletools::forecast(ili_fit, h=horizon)
+
+  # Get the next #horizon weeks in a tibble
+  ili_future <- ili_forecast %>%
+    tibble::as_tibble() %>%
+    dplyr::mutate(epiyear=lubridate::epiyear(yweek)) %>%
+    dplyr::mutate(epiweek=lubridate::epiweek(yweek)) %>%
+    dplyr::select(location, epiyear, epiweek, ili=.mean)
+
+  # bind the historical data to the new data
+  ili_bound <- dplyr::bind_rows(ilidat %>% dplyr::mutate(forecasted=FALSE),
+                                ili_future %>% dplyr::mutate(forecasted=TRUE)) %>%
+    dplyr::arrange(location, epiyear, epiweek) %>%
+    dplyr::inner_join(locations, by="location")
+
+  # Create results
+  res <- tibble::lst(ilidat, ilidat_tsibble, ili_fit, ili_forecast, ili_future, ili_bound, arima_params, locstats, removed)
+  return(res)
+}

R/retrieve.R

@@ -106,7 +106,7 @@ get_hdgov_hosp <- function(endpoint="https://healthdata.gov/resource/g62h-syeh.j
 #' @references API documentation: <https://dev.socrata.com/foundry/data.cdc.gov/k87d-gv3u>.
 #' @examples
 #' \dontrun{
-#' d <- get_cdc_vax_data()
+#' d <- get_cdc_vax()
 #' d
 #' library(ggplot2)
 #' d %>%
@@ -183,14 +183,14 @@ get_cdc_ili <- function(region=c("national", "state"), years=NULL) {
   # Get only relevant columns (drop age group distributions)
   # Join to internal package data to get state abbreviations and FIPS codes
   d <- d %>%
-    dplyr::select(region_type, region, year, week, week_start, dplyr::contains("ili"), ilitotal:total_patients) %>%
+    dplyr::select(region_type, region, epiyear=year, epiweek=week, week_start, dplyr::contains("ili"), ilitotal:total_patients) %>%
     dplyr::mutate(region=gsub("National", "US", region)) %>%
     dplyr::inner_join(locations, by=c("region"="location_name")) %>%
     dplyr::select(location, region_type, abbreviation, region, dplyr::everything())
   message(sprintf("Latest week_start / year / epiweek available:\n%s / %d / %d",
                   max(d$week_start),
-                  unique(d$year[d$week_start==max(d$week_start)]),
-                  unique(d$week[d$week_start==max(d$week_start)])))
+                  unique(d$epiyear[d$week_start==max(d$week_start)]),
+                  unique(d$epiweek[d$week_start==max(d$week_start)])))
   return(d)
 }
 
@@ -211,16 +211,16 @@ get_cdc_hosp <- function(years=NULL) {
     dplyr::transmute(location="US",
                      abbreviation="US",
                      region="US",
-                     year,
-                     week=year_wk_num,
+                     epiyear=year,
+                     epiweek=year_wk_num,
                      week_start=wk_start,
                      week_end=wk_end,
                      rate,
                      weeklyrate,
                      season=sea_label)
   message(sprintf("Latest week_start / year / epiweek available:\n%s / %d / %d",
                   max(d$week_start),
-                  unique(d$year[d$week_start==max(d$week_start)]),
-                  unique(d$week[d$week_start==max(d$week_start)])))
+                  unique(d$epiyear[d$week_start==max(d$week_start)]),
+                  unique(d$epiweek[d$week_start==max(d$week_start)])))
   return(d)
 }

R/utils.R

@@ -7,13 +7,14 @@
 #' @param df A `tibble` containing columns `epiyear` and `epiweek`.
 #' @param epiyear Unquoted variable name containing the MMWR epiyear.
 #' @param epiweek Unquoted variable name containing the MMWR epiweek.
+#' @param key Unquoted variable name containing the name of the column to be the tsibble key. See [tsibble::as_tsibble].
 #' @param chop Logical indicating whether or not to remove the most current week (default `TRUE`).
 #' @return A `tsibble` containing additional columns `monday` indicating the date
 #'   for the Monday of that epiweek, and `yweek` (a yearweek vctr class object)
 #'   that indexes the `tsibble` in 1 week increments.
 #' @export
 #' @md
-make_tsibble <- function(df, epiyear, epiweek, chop=TRUE) {
+make_tsibble <- function(df, epiyear, epiweek, key=location, chop=TRUE) {
   out <- df %>%
     # get the monday that starts the MMWRweek
     dplyr::mutate(monday=MMWRweek::MMWRweek2Date(MMWRyear={{epiyear}},
@@ -23,7 +24,7 @@ make_tsibble <- function(df, epiyear, epiweek, chop=TRUE) {
     # convert represent as yearweek (see package?tsibble)
     dplyr::mutate(yweek=tsibble::yearweek(monday), .after="monday") %>%
     # convert to tsibble
-    tsibble::as_tsibble(index=yweek, key=location)
+    tsibble::as_tsibble(index=yweek, key={{key}})
   # Remove the incomplete week
   if (chop) out <- utils::head(out, -1)
   return(out)
@@ -113,3 +114,4 @@ plot_forc <- function(.forecasts, .train, .test) {
 }
 
 
+