221 updated categorical rate change thresholds and algorithm for 2024 25 flusight season

R/forecast.R

@@ -596,7 +596,7 @@ forecast_categorical <- function(.forecast, .observed, method = "density", forma
   } else if (method == "density") {
 
     if(format != "hubverse") {
-      stop("Currently the desnity method only works with 'hubverse' forecast format.")
+      stop("Currently the density method only works with 'hubverse' forecast format.")
     }
 
     last_week <-
@@ -673,32 +673,32 @@ density_probs <- function(df, n_horizons = 5, ...){
               "-1" = list(t1 = max(c(10, as.numeric(unique(df$count_rate1)))),
                           t2 = max(c(10, as.numeric(unique(df$count_rate2))))
               ),
-              "0" = list(t1 = max(c(10, as.numeric(unique(df$count_rate1)))),
+              "0" = list(t1 = max(c(10, as.numeric(unique(df$count_rate0p3)))),
+                         t2 = max(c(10, as.numeric(unique(df$count_rate1p7))))
+              ),
+              "1" = list(t1 = max(c(10, as.numeric(unique(df$count_rate0p5)))),
                          t2 = max(c(10, as.numeric(unique(df$count_rate3))))
               ),
-              "1" = list(t1 = max(c(10, as.numeric(unique(df$count_rate2)))),
+              "2" = list(t1 = max(c(10, as.numeric(unique(df$count_rate0p7)))),
                          t2 = max(c(10, as.numeric(unique(df$count_rate4))))
               ),
-              "2" = list(t1 = max(c(10, as.numeric(unique(df$count_rate2p5)))),
-                         t2 = max(c(10, as.numeric(unique(df$count_rate5))))
-              ),
-              "3" = list(t1 = max(c(10, as.numeric(unique(df$count_rate2p5)))),
+              "3" = list(t1 = max(c(10, as.numeric(unique(df$count_rate1)))),
                          t2 = max(c(10, as.numeric(unique(df$count_rate5))))
               )
       )
   } else if (n_horizons == 4) {
     thresh <-
       switch (as.character(tmp_horizon),
-              "0" = list(t1 = max(c(10, as.numeric(unique(df$count_rate1)))),
-                         t2 = max(c(10, as.numeric(unique(df$count_rate2))))
+              "0" = list(t1 = max(c(10, as.numeric(unique(df$count_rate0p3)))),
+                         t2 = max(c(10, as.numeric(unique(df$count_rate1p7))))
               ),
-              "1" = list(t1 = max(c(10, as.numeric(unique(df$count_rate1)))),
+              "1" = list(t1 = max(c(10, as.numeric(unique(df$count_rate0p5)))),
                          t2 = max(c(10, as.numeric(unique(df$count_rate3))))
               ),
-              "2" = list(t1 = max(c(10, as.numeric(unique(df$count_rate2)))),
+              "2" = list(t1 = max(c(10, as.numeric(unique(df$count_rate0p7)))),
                          t2 = max(c(10, as.numeric(unique(df$count_rate4))))
               ),
-              "3" = list(t1 = max(c(10, as.numeric(unique(df$count_rate2p5)))),
+              "3" = list(t1 = max(c(10, as.numeric(unique(df$count_rate1)))),
                          t2 = max(c(10, as.numeric(unique(df$count_rate5))))
               )
       )

data-raw/generate_sysdata.R

@@ -36,17 +36,18 @@ locations <-
 ## provenance of data file
 # download.file("https://raw.githubusercontent.com/cdcepi/Flusight-forecast-data/master/data-locations/locations.csv",
 #               destfile=here::here("data-raw/legacy-rate-change.csv"))
+## NOTE: this data was updated for the 2024-25 season and re-downloaded so we can use the correct thresholds
 # download.file("https://raw.githubusercontent.com/cdcepi/Flusight-forecast-hub/main/auxiliary-data/locations.csv",
-#               destfile=here::here("data-raw/hubverse-rate-change.csv"))
-
+#               destfile=here::here("data-raw/hubverse-rate-change-2425.csv"))
 ## read the location crosswalk table from file downloaded from github
 ## select columns of interest
 legacy_rate_change <-
   read_csv(here::here("data-raw/legacy-rate-change.csv"), col_types="cccddd") %>%
   select(location, count_rate1per100k, count_rate2per100k)
 
 hubverse_rate_change <-
-  read_csv(here::here("data-raw/hubverse-rate-change.csv"), col_types="cccddddddd")
+  # read_csv(here::here("data-raw/hubverse-rate-change-2324.csv"), col_types="cccddddddd")
+  read_csv(here::here("data-raw/hubverse-rate-change-2425.csv"), col_types="cccddddddddd")
 
 # Quantiles ---------------------------------------------------------------
 

data-raw/hubverse-rate-change-2425.csv

@@ -0,0 +1,54 @@
+abbreviation,location,location_name,population,count_rate0p3,count_rate0p5,count_rate0p7,count_rate1,count_rate1p7,count_rate3,count_rate4,count_rate5
+US,US,US,334914895,1005,1675,2344,3349,5694,10047,13397,16746
+AL,01,Alabama,5108468,15,26,36,51,87,153,204,255
+AK,02,Alaska,733406,2,4,5,7,12,22,29,37
+AZ,04,Arizona,7431344,22,37,52,74,126,223,297,372
+AR,05,Arkansas,3067732,9,15,21,31,52,92,123,153
+CA,06,California,38965193,117,195,273,390,662,1169,1559,1948
+CO,08,Colorado,5877610,18,29,41,59,100,176,235,294
+CT,09,Connecticut,3617176,11,18,25,36,61,109,145,181
+DE,10,Delaware,1031890,3,5,7,10,18,31,41,52
+DC,11,District of Columbia,678972,2,3,5,7,12,20,27,34
+FL,12,Florida,22610726,68,113,158,226,384,678,904,1131
+GA,13,Georgia,11029227,33,55,77,110,187,331,441,551
+HI,15,Hawaii,1435138,4,7,10,14,24,43,57,72
+ID,16,Idaho,1964726,6,10,14,20,33,59,79,98
+IL,17,Illinois,12549689,38,63,88,125,213,376,502,627
+IN,18,Indiana,6862199,21,34,48,69,117,206,274,343
+IA,19,Iowa,3207004,10,16,22,32,55,96,128,160
+KS,20,Kansas,2940546,9,15,21,29,50,88,118,147
+KY,21,Kentucky,4526154,14,23,32,45,77,136,181,226
+LA,22,Louisiana,4573749,14,23,32,46,78,137,183,229
+ME,23,Maine,1395722,4,7,10,14,24,42,56,70
+MD,24,Maryland,6180253,19,31,43,62,105,185,247,309
+MA,25,Massachusetts,7001399,21,35,49,70,119,210,280,350
+MI,26,Michigan,10037261,30,50,70,100,171,301,401,502
+MN,27,Minnesota,5737915,17,29,40,57,98,172,230,287
+MS,28,Mississippi,2939690,9,15,21,29,50,88,118,147
+MO,29,Missouri,6196156,19,31,43,62,105,186,248,310
+MT,30,Montana,1132812,3,6,8,11,19,34,45,57
+NE,31,Nebraska,1978379,6,10,14,20,34,59,79,99
+NV,32,Nevada,3194176,10,16,22,32,54,96,128,160
+NH,33,New Hampshire,1402054,4,7,10,14,24,42,56,70
+NJ,34,New Jersey,9290841,28,46,65,93,158,279,372,465
+NM,35,New Mexico,2114371,6,11,15,21,36,63,85,106
+NY,36,New York,19571216,59,98,137,196,333,587,783,979
+NC,37,North Carolina,10835491,33,54,76,108,184,325,433,542
+ND,38,North Dakota,783926,2,4,5,8,13,24,31,39
+OH,39,Ohio,11785935,35,59,83,118,200,354,471,589
+OK,40,Oklahoma,4053824,12,20,28,41,69,122,162,203
+OR,41,Oregon,4233358,13,21,30,42,72,127,169,212
+PA,42,Pennsylvania,12961683,39,65,91,130,220,389,518,648
+RI,44,Rhode Island,1095962,3,5,8,11,19,33,44,55
+SC,45,South Carolina,5373555,16,27,38,54,91,161,215,269
+SD,46,South Dakota,919318,3,5,6,9,16,28,37,46
+TN,47,Tennessee,7126489,21,36,50,71,121,214,285,356
+TX,48,Texas,30503301,92,153,214,305,519,915,1220,1525
+UT,49,Utah,3417734,10,17,24,34,58,103,137,171
+VT,50,Vermont,647464,2,3,5,6,11,19,26,32
+VA,51,Virginia,8715698,26,44,61,87,148,261,349,436
+WA,53,Washington,7812880,23,39,55,78,133,234,313,391
+WV,54,West Virginia,1770071,5,9,12,18,30,53,71,89
+WI,55,Wisconsin,5910955,18,30,41,59,100,177,236,296
+WY,56,Wyoming,584057,2,3,4,6,10,18,23,29
+PR,72,Puerto Rico,3205691,10,16,22,32,54,96,128,160