###########################################################
# Epicurve symptom onset of hospitalised cases in Osiris data
# Rt development over time
# corrected for SO to first report of hospital admission
# from historic Osiris downloads
# 03-04-2020 ___UITZONDERINGSGROND_2___
###########################################################

library(tidyverse)
library(lubridate)

source("/___UITZONDERINGSGROND_6___functions_Osiris.r")

# get all case data files from Osiris
files <- list.files("/___UITZONDERINGSGROND_6___Previous", full.names = TRUE)
files <- c(files, list.files("/___UITZONDERINGSGROND_6___Geschoond", full.names = TRUE))
files <- files[grepl(files, pattern = "rds")]

report_date <- as.Date("2020-04-05")
report_date <- today()
pattern_date <- report_date %>% str_split(pattern = "-") %>% unlist %>% paste0(collapse = "")
tmpfiles <- files[grep(files, pattern = pattern_date)]
file.name <- tmpfiles[tmpfiles %>% file.info %>% pull(ctime) %>% which.max]
dataOsiris <- read_rds(file.name)


symptomonset2reporting <- get_symptomonset2reporting(start_date = report_date - 7, end_date = report_date, IC = FALSE)
epicurve <- extract_epicurve(data = dataOsiris, IC = FALSE, report_date = report_date, SOtoRep = symptomonset2reporting)

epicurve <- calculate_Ru_Osiris(epicurve)

plot_epicurve_Osiris(epicurve, IC = FALSE)

plot_Reff(epicurve, caseReff = TRUE, IC = FALSE)


# Per provincie (ook nog delay per provincie)

provincies <- unique(dataOsiris$Provincie)

report_delays_perprov <- tibble()
for(provincie in provincies) {
  tmp <- get_symptomonset2reporting_perprov(provincie = provincie, start_date = today()-7, end_date = today(), IC = FALSE)
  report_delay <- tibble(provincie = provincie, day = 0:39, cdf = tmp)
  report_delays_perprov <- bind_rows(report_delays_perprov, report_delay)
}

ggplot(data = report_delays_perprov,
       mapping = aes(x = day, y = cdf, col = provincie)) + 
  geom_line() + 
  labs(x = "eerste zieketedag tot rapportage (dagen)",
       y = "fractie eerste ziektedagen gerapporteerd") +
  theme_minimal()



epicurves <- tibble()
for(provincie in provincies) {
  print(provincie)
  symptomonset2reporting <- get_symptomonset2reporting_perprov(provincie = provincie, start_date = today()-7, end_date = today(), IC = FALSE)
  epicurve <- extract_epicurve(data = dataOsiris %>% filter(Provincie == provincie), IC = FALSE, report_date = today(), SOtoRep = symptomonset2reporting)
  epicurve <- calculate_Ru_Osiris(epicurve)
  epicurve <- epicurve %>% mutate(provincie = provincie)
  epicurves <- bind_rows(epicurves, epicurve)
}

plot_epicurve_Osiris(epicurve = epicurves) + facet_wrap(facets = vars(provincie))

plot_Reff(epicurve = epicurves, IC = FALSE) + facet_wrap(facets = vars(provincie))

# Rapportagevertraging

report_dates <- seq(as.Date("2020-03-20"), today(), by = "day") %>% as.character
report_delays <- tibble()
for(report_date in report_dates) {
  tmp <- get_symptomonset2reporting(end_date = as.Date(report_date), IC = FALSE)
  report_delay <- tibble(report_date = report_date, day = 0:39, cdf = tmp)
  report_delays <- bind_rows(report_delays, report_delay)
}

ggplot(data = report_delays,
       mapping = aes(x = day, y = cdf, col = report_date)) + 
  geom_line() + 
  labs(x = "eerste zieketedag tot rapportage (dagen)",
       y = "fractie eerste ziektedagen gerapporteerd") +
  theme_minimal()