seabbs

# load required packages
library(EpiNow2)
library(future.apply)
library(purrr)
library(data.table)


warning("This gist is depreciated. Please use the following development repository: https://github.com/seabbs/regional-secondary.git")
# extract priors from a posterior and update fitting args
extract_secondary_priors <- function(posterior) {

seabbs

# set number of cores to use fitting the model
# no benefit on runtime if cores > chains which is set to 4 by default
options(mc.cores = 4)

# Packages ----------------------------------------------------------------
# install.packages(c("data.table", "remotes", "ggplot2"))		
# remotes::install_github("epiforecasts/EpiNow2")
# remotes::install_github("epiforecasts/covidregionaldata")
library(data.table)
library(ggplot2)

seabbs

# Load packages
library(epinowcast)
library(data.table)
library(ggplot2)

# Load and filter germany hospitalisations
nat_germany_hosp <- germany_covid19_hosp[location == "DE"][age_group %in% "00+"]
nat_germany_hosp <- enw_filter_report_dates(
  nat_germany_hosp,
  latest_date = "2021-10-01"

seabbs

# If we receive X-Forwarded-Proto, pass it through; otherwise, pass along the
# scheme used to connect to this server
map $http_x_forwarded_proto $proxy_x_forwarded_proto {
  default $http_x_forwarded_proto;
  ''      $scheme;
}
# If we receive X-Forwarded-Port, pass it through; otherwise, pass along the
# server port the client connected to
map $http_x_forwarded_port $proxy_x_forwarded_port {
  default $http_x_forwarded_port;

seabbs

#' Disease Model, with Vaccination, Data Based Population Demographics and Ageing as a Pomp Object
#'
#' @param df A data frame of data to be passed to the pomp model.
#' @param t0 An integer value denoting the starting time for the model, this may be negative.
#' @param nstageA The number of age compartments to model.
#' @param timestep Value indicating the timestep over which to solve the model.
#' @param time The variable to use the time parameter from the data.
#' @param covars Additional data to use within the model.
#' @param tcovar A character string indicating the time variable in covar.
#' @param obsnames A character string of the observable variables.

seabbs

# packages
# install.packages(c("data.table", "remotes", "EpiNow2"))		
library(data.table)
library(EpiNow2)
library(covidregionaldata)

# target country (must be present in ECDC data)
country <- "france"

# set number of cores to use fitting the model

seabbs

# 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)

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

# Simulate some truncated and truncation data

seabbs

functions{ 
#include functions/ct_trajectory.stan
#include functions/truncated_normal_rng.stan
#include functions/censor.stan
}

data {
  int P; // number of patients
  int N; // number of tests
  real c_lod; // Ct value at limit of detection 

seabbs

library(data.table)
library(snakecase)
library(ggplot2)

# make a convolution matrix
convolution_matrix <- function(vec1, vec2) {
  lvec1 <- length(vec1)
  lvec2 <- length(vec2)
  conv <- matrix(0, nrow = lvec1, ncol = lvec1)
  for (s in 1:lvec1) {

seabbs

# packages
# install.packages(c("data.table", "remotes", "EpiNow2"))		
# remotes::install_github("epiforecasts/EpiNow2")
# remotes::install_github("epiforecasts/covidregionaldata")
library(data.table)
library(EpiNow2)
library(covidregionaldata)

# target country (must be supported in covidregionaldata)
country <- "uk"   # harder to fit "india"