adamkucharski

# Define parameters
bkg_val <- 15 # background value
xx <- 1e3 # Number of values to simulate from positive and negative distribution
xx_pos <- xx; xx_neg <- xx

set.seed(1)

# Set up plot
par(mfcol=c(2,2),mgp=c(2,0.7,0),mar = c(3,3,1,3))

adamkucharski

# Define parameters
xx <- 1e3 # Number of values to simulate from positive and negative distribution
xx_pos <- xx; xx_neg <- xx

set.seed(1)

# Set up plot
par(mfcol=c(2,2),mgp=c(2,0.7,0),mar = c(3,3,1,3))

for(kk in 1:2){

adamkucharski

# Define R0
r_number <- 1.4

# Final size model
attack_rate <- finalsize::final_size(r_number)$n_infected

print(attack_rate)

# Calculate total exposures over the whole epidemic
# (note: later in epidemic, now-immune people will be re-exposed)

adamkucharski

# Define delay params
param_1 <- log(5)
param_2 <- 1

# Onsets at time 0
initial_onsets <- 100

# CFR
cfr <- 0.5

adamkucharski

# Correcting for real-time CFR bias -------------------------------------------------
# This script builds on the theory outlined in this vignette:
# https://epiverse-trace.github.io/cfr/articles/estimate_from_individual_data.html


# Load packages
library(cfr)
library(dplyr)
library(ggplot2)
library(gridExtra)

adamkucharski

adamkucharski

# GPT prompt: Edit this odin code to add waning immunity from R to S. 
# Explain your edits in the code comments, and make sure parameters and variables are consistent.

# Load packages
library(epidemics)
library(socialmixr)
library(odin)
library(data.table)
library(ggplot2)