Adam Kucharski adamkucharski

age	sex	location	occupation	education	daily_step_count	weekly_screen_time_hours	monthly_leisure_activities_count	weekly_internet_usage_hours	monthly_public_transport_trips	digital_engagement_score
39	Male	Urban	Office	College	13971	5	1	26	10	73
75	Female	Coastal	Office	No formal	6574	32	4	17	5	100
47	Female	Suburban	Manual	Secondary	2520	39	1	32	3	65
82	Male	Suburban	Office	No formal	11092	24	4	16	16	58
86	Female	Suburban	Manual	Secondary	3742	10	7	7	1	85
21	Male	Rural	Unemployed	Secondary	7493	13	13	17	7	77
56	Male	Urban	Office	Secondary	8407	12	10	19	7	45
82	Male	Urban	Service	Postgraduate	7629	10	9	21	32	50
58	Female	Urban	Unemployed	Undergraduate	8989	33	20	59	12	40

	# 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)

	# 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)

	# 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)

	# 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){

	# 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))

	# 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