adamkucharski

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# Code to compare finalsize model outputs with ONS data in UK
# Adam Kucharski (2022)
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library(devtools)
devtools::install_github("epiverse-trace/finalsize",force=T)
devtools::install_github("adamkucharski/quickfit")

library(finalsize)

adamkucharski

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
# Illustrative code to general finalsize model outputs from contact matrix
# Adapted from: https://epiverse-trace.github.io/finalsize/articles/finalsize.html
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library(devtools)
install.packages("finalsize")
library(finalsize)
library(rio)
library(tidyverse)

adamkucharski

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
# Code to estimate R and k from clusters sizes using MLE via quickfit
# Adam Kucharski (2023)
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library(devtools)
library(rstan)
library(MASS)

#devtools::install_github("adamkucharski/quickfit")

adamkucharski

# serofoi analysis on Fiji serosurvey data

# Load libraries
library(remotes)
remotes::install_github("TRACE-LAC/serofoi")
library(serofoi)
library(rio)

# Load Fiji dengue data
url_fiji <- "https://raw.githubusercontent.com/adamkucharski/fiji-denv3-2014/master/data/serology_inputs.csv"

adamkucharski

You are R software teacher specialising in epidemic scenario modelling. Use the below example code for the R package 'finalsize' as a basis for your response to the user question at the end.

Use case: There is substantial heterogeneity in contact patterns in a population. We want to know how this heterogeneity could affect the final size of the epidemic. Example code:

# load finalsize
library(finalsize)

# load necessary packages
if (!require("socialmixr")) install.packages("socialmixr")

adamkucharski

# Calculate final size using {finalsize}
library(finalsize)

xx <- seq(1,4,0.1) # range of R0 values
finalsize_calc <- sapply(xx,function(x){finalsize::final_size(r0=x,matrix(1),1,matrix(1),matrix(1))$p_infected}) # simple final size model

# Plot results
par(mfrow=c(1,1),mar=c(4,4,1,1),las=0)
plot(xx,finalsize_calc, ylim=c(0,1),lwd=2, col="dark blue",type="l", main="", yaxs="i",xaxs="i", yaxt="n",xlab=expression(R[0]), ylab="Percentage infected",bty="l")
axis(2, at=seq(0,1,0.1),labels = paste0(seq(0,100,10),"%"),col = "black") 

adamkucharski

# - - - - - - - - - - - - - - - - - - - - - - - 
# Code by Adam Kucharski to accompany post 'Counting current COVID infections' (2nd Jan 2024)
# Post is available at: https://kucharski.substack.com
# Code in this file is shared under an MIT license (https://opensource.org/license/mit/)
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# Load libraries
library(dplyr)
library(readr)

adamkucharski

# Example code for simulation recovery of infection dynamics from deaths for COVID -----------------------------------------

# Load libraries:

# install.packages("EpiNow2", repos = "https://epiforecasts.r-universe.dev")
library(EpiNow2)

# Simulate data and delays -----------------------------------------

# Simulate infections with a sharp drop

adamkucharski

adamkucharski

# Analysis of VE during epidemic when vaccine protection < infection protection -------------------------------------------------------------
# Adam Kucharski (2024)


# Define assumptions about exposure over time in epidemic -----------------

xx <- seq(0,6,1/30) # time points for model
peak_time <- 3 # peak in months post-vaccine campaign
peak_width <- 0.8 # variance in epidemic width (i.e. sd of distribution)
exposure_prop <- 0.6 # proportion of population exposure during wave