inst/extdata/scripts/helpers/read_case_data_from_server.R

In kylieainslie/vacamole: Deterministic Age-Structured SEIR model with Vaccination

# ----------------------------------------------------
# read in updated OSIRIS data from RIVM server
# ----------------------------------------------------
library(fst)
library(tidyverse)
# get file path --------------------------------------
path <- "/rivm/r/COVID-19/Surveillance/Data/OSIRIS/Geschoond/"
file <- list.files(path, pattern = ".fst")
if (identical(file, character(0))) {
  path <- paste0(path,"Previous/")
  file <- list.files(path, pattern = ".fst") %>%
    max()
}

# read in data ---------------------------------------
osiris <- read_fst(paste0(path,file))

# aggregate for number of cases per day --------------
# this removes any identifiable data

osiris_tally <- osiris %>%
  select(OSIRISNR, INFECTIEZIEKTE, ZIE1eZiekteDt, Land) %>%
  filter(Land == "Nederland",
         INFECTIEZIEKTE %in% c("NCOV", "Weak Positive", "Antitgen Pos. + Symptoms", "PCR Positief", "Antigen Positief")) %>%
  select(-Land) %>%
  rename(date = ZIE1eZiekteDt) %>%
  group_by(date) %>%
  summarise(inc = n()) %>%
  filter(!is.na(date)) %>%
  complete(date = seq.Date(min(date), max(date), by="day"), fill = list(inc = 0))

# calculate rolling average and remove last 3 days ---
osiris1 <- osiris_tally %>%
  mutate(roll_avg = zoo::rollmean(inc, k = 7, fill = 0)) %>%
  filter(date <= max(date) - 3) # remove last 3 days due to reporting delay

last_date_in_osiris <- tail(osiris1$date, 1)
# save output ----------------------------------------
saveRDS(osiris1, file = paste0("inst/extdata/data/case_data_upto_", last_date_in_osiris, ".rds"))