sbfnk

library("tidyr")
library("dplyr")
library("cowplot")
library("stringi")
library("scales")
library("ggplot2")
library("here")

dt_contacts <- readRDS("contacts.rds")

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#' Aggregate daily forecasts to weekly forecasts
#' 
#' Aggregate daily case forecasts from regional_epinow2 to weekly forecasts.
#' 
#' @param inf List output from `EpiNow2::regional_epinow()`. No default.
#' @param week_start Week start day. Default is 1 (Monday).
#' @param summary_quantiles Quantiles for forecast summary. Default is median, plus upper and lower bound for 20\%, 50\% and 90\% forecasts. 
#' @importFrom dplyr group_by summarise filter mutate select case_when contains
#' @importFrom tidyr pivot_wider
#' @importFrom purrr map_df

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# Updated from https://gist.github.com/pkazmier to support yabai
#
# The following configuration heavily leverages modal keymaps to minimize the
# pollution of global keybindings. In addition, the modal keymaps facilitate
# the consistent use of the same keybindings across different modes. For
# example, this configuration uses 'h', 'l', 'j', and 'k' to represent west,
# east, south, and north when: changing focus, warping windows, resizing
# windows, swapping windows, and moving floating windows. Those four keys are
# mapped differently depending on the current mode to provide a consistent user
# experience.

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library("readr")
library("dplyr")

df <- readr::read_csv("https://raw.githubusercontent.com/epiforecasts/inc2prev/master/outputs/estimates_age_ab.csv") ## nolint

td <- df |>
  dplyr::filter(name == "infections") |>
  dplyr::select(t_index, date) |>
  dplyr::distinct() |>
  dplyr::arrange(t_index)

sbfnk

library("devtools")
library("here")
library("socialmixr")

redo <- FALSE
files_file <- here::here("files.rds")
dir.create(here::here("surveys"), showWarnings = FALSE)

if (!file.exists(files_file) || redo) {
  ls <- list_surveys()

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# Load packages
library(brms)
library(cmdstanr)
library(data.table) # here we use the development version of data.table install it with data.table::update_dev_pkg
library(purrr)
library(bpmodels) # devtools::install_github("sbfnk/bpmodels")

# Set up parallel cores
options(mc.cores = 4)

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library("readr")
library("dplyr")
library("janitor")
library("lubridate")
library("ggplot2")

## SOURCE 1: download data
df <- read_delim("https://www.sanidad.gob.es/profesionales/saludPublica/ccayes/alertasActual/nCov/documentos/Datos_Capacidad_Asistencial_Historico_19102022.csv", delim = ";", show_col_types = FALSE)

df_national <- df %>%

sbfnk

library("readr")
library("dplyr")
library("ggplot2")

highlight_models <- c(
  "EuroCOVIDhub-ensemble", "KITmetricslab-bivar_branching",
  "EuroCOVIDhub-baseline"
)

model_scores <- read_csv(paste0(

sbfnk

library("readr")
library("dplyr")
library("lubridate")
library("here")
library("tidyr")

regional_estimates <-
	read_csv(paste0("https://raw.githubusercontent.com/epiforecasts/inc2prev/",
			"master/outputs/estimates_regional.csv"))
national_estimates <-

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##' Get estimates shown at \url{https://epiforecasts.io/covid} for all times
##'
##' Nowcasts at \url{https://epiforecasts.covid} and the related github repository at
##' \url{https://github.com/epiforecasts/covid-rt-estimates} only cover the last 3
##' months. This function downloads all available estimates and applies a median
##' average to the provided quantiles to provide an estimate of a time series
##' covering all times available
##'
##' @param dataset character; data set corresponding to directories at
##' \url{https://github.com/epiforecasts/covid-rt-estimates}. Default is