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bearmod_fx.R
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##### BEARmod v.0.81
#
# Basic Epidemic, Activity, and Response simulation model
#
#
# v0.9
# - added functionality for time spent version of movement matrix (for now implemented in a new function, runSim_timespent())
# - fixed bug in how model tracks recovered people
#
# v0.81
# - Major speed increase in infection
#
# v0.8
# - added capacity for multiple timesteps per day. TSinday defaults to 1, but defines the number of timesteps per day in the model (must be integer)
# - added capacity for a probability of moving per timestep (fitting to Vodafone data). Not accounted for if prob_move_per_TS = 0
#
# v0.7 updates:
# - Add percentage of exposed people who are infectious
#
# v0.65 updates:
# - Cleaned up inputs into model for easier pre-processing
# - Added capacity for time-dependent contact rates
#
# v0.6 updates:
# - fixed bug when recovery rate data are missing a patch for a day
# - patched bug that could lead to negative nInf values (however, the actual solution needs revisiting!)
#
#v0.5 updates:
# - Added functionality to input relative movement table
# - added functionality for time-variable recovery rates
#
# v.0.4 Updates:
# - Model calibrated using HKU studies.
# - Fixed transmission term to Poisson distribution
# - Added "date" versatility to use non-contiguous dates
#
# This model runs a basic SEIR model, with stochastic exposure, incubation, recovery, and movement
# Disease spread occurs each day, based on the movement patterns from specific days from mobile phone-derived data.
#
#
#
# See run_model.R for working example.
#
# TO DO:
# - Add in infectious period for part of the exposed period (new category: exposed and infectious)
#
#
#
######
library(lubridate)
#This function creates the starting population
InitiatePop = function(pat_locator,initialInf,initialExp){
NPat = dim(pat_locator)[1]
list(
nInitialInf = initialInf,
nInitialExp = initialExp,
nInf = initialInf,
nExp = initialExp,
nRec = rep(0,NPat),
nTotal = pat_locator$pop,
names = pat_locator$patNames,
IDs = pat_locator$patIDs,
relativeInf = rep(1,NPat),
nRecoveredToday = rep(0,NPat),
nInfectedToday = rep(0,NPat),
nExposedToday = rep(0,NPat),
nInfMovedToday = rep(0,NPat),
controlled = rep(0,NPat)
)
}
##### Epidemic functions: exposure, infectivity, recovery ####
recoveryTimeStep = function(HPop, recrate_values,current_day){
recrate = subset(recrate_values,date == current_day)$recrate
#print(recrate)#print(paste0("Day ",current_day, " recovery rate: ", recrate))
for (i in 1:length(HPop$nInf)){
HPop$nRecoveredToday[i]= sum(rbinom(HPop$nInf[i],1,recrate))
HPop$nInf[i] = HPop$nInf[i] - HPop$nRecoveredToday[i]
HPop$nRec[i] = HPop$nRec[i] + HPop$nRecoveredToday[i]
}
#print(paste0("Number of people recovering: ",sum(HPop$nRecoveredToday)))
HPop
}
exposedtoinfTimeStep = function(HPop, exp_to_infrate){
#(exp_to_infrate)
for (i in 1:length(HPop$nInf)){
print(HPop$nExposedToday[i])
print(HPop$nExp[i])
HPop$nInfectedToday[i]= sum(rbinom(HPop$nExp[i],1,exp_to_infrate))
HPop$nInf[i] = HPop$nInf[i] + HPop$nInfectedToday[i]
HPop$nExp[i] = HPop$nExp[i] - HPop$nInfectedToday[i]
}
#print(paste0("Number of people newly infectious: ",sum(HPop$nInfectedToday)))
HPop
}
exposedTimeStep = function(HPop, exposerate_df, current_day, exposed_pop_inf_prop){
if (is.numeric(exposerate_df)){
exposerate = exposerate_df
}
if (is.data.frame(exposerate_df)){
exposerate = subset(exposerate_df, date == current_day)$exposerate
}
for (i in 1:length(HPop$nInf)){
infectious_pop = HPop$nInf[i] + exposed_pop_inf_prop * HPop$nExp[i]
#HPop$nExposedToday[i]= sum(rbinom(infectious_pop,1,exposerate)) * (1 - ( (HPop$nInf[i] + HPop$nExp[i]) / HPop$nTotal[i]))
HPop$nExposedToday[i]= sum(rpois(infectious_pop,exposerate)) * (1 - ( (HPop$nInf[i] + HPop$nExp[i] + HPop$nRec[i]) / HPop$nTotal[i]))
if (HPop$nExp[i] + HPop$nExposedToday[i] < HPop$nTotal[i] - HPop$nInf[i] - HPop$nRec[i] ) {
HPop$nExp[i] = HPop$nExp[i] + HPop$nExposedToday[i]
} else {
HPop$nExposedToday[i] = max(0,HPop$nTotal[i] - HPop$nInf[i] - HPop$nExp[i]- HPop$nRec[i])
HPop$nExp[i] = max(0,HPop$nTotal[i] - HPop$nInf[i] - HPop$nRec[i])
}
}
#print(paste0("Number of people newly exposed: ",sum(HPop$nExposedToday)))
HPop
}
exposedTimeStep_timespent = function(HPop, exposerate_df, current_day, exposed_pop_inf_prop,ts_data){
TS_matrix = matrix(0,NPat,NPat,dimnames=list(patIDs,patIDs))
daily_move = subset(ts_data,date == current_day)
daily_move = subset(daily_move,!is.na(fr_pat) & !is.na(to_pat) & !is.na(fr_users) & !is.na(movers))
daily_move_mat = daily_move[,is.element(names(daily_move),c("fr_pat","to_pat","fr_users","movers"))]
daily_move_mat = as.matrix(daily_move_mat)
col1 = which(colnames(daily_move_mat) == "fr_pat")
col2=which(colnames(daily_move_mat) == "to_pat")
colmove = which(colnames(daily_move_mat) == "movers")
colusers=which(colnames(daily_move_mat) == "fr_users")
TS_matrix[daily_move_mat[,c(col1,col2)]] = daily_move_mat[,colmove]/daily_move_mat[,colusers]
if (length(which(rowSums(TS_matrix)>1)) > 0){
print("Warning: row sums > 1 in movement matrix. Correcting...")
correctingrows = which(rowSums(TS_matrix)>1)
for (i in correctingrows){
TS_matrix[i,] = TS_matrix[i,] /sum(TS_matrix[i,] )
}
}
for (i in 1:length(patIDs)){
TS_matrix[i,i] = 1 - sum(TS_matrix[i,-i])
}
if (is.numeric(exposerate_df)){
exposerate = exposerate_df
}
if (is.data.frame(exposerate_df)){
exposerate = subset(exposerate_df, date == current_day)$exposerate
}
movement_adjusted_infectious_prop = rep(0,length(HPop$nInf))
for (i in 1:length(HPop$nInf)){
movement_adjusted_infectious_prop[i] = sum(((HPop$nInf * TS_matrix[,i]) + exposed_pop_inf_prop * sum(( HPop$nExp * TS_matrix[,i])))) / sum(HPop$nTotal * TS_matrix[,i])
}
susceptible_vec = round(HPop$nTotal - HPop$nInf - HPop$nExp - HPop$nRec)
probability_infection = 1-exp(-exposerate * movement_adjusted_infectious_prop)
for (i in 1:length(HPop$nInf)){
susceptible_weighted_pop = round(susceptible_vec[i]*TS_matrix[i,])
HPop$nExposedToday[i] = round(sum(rbinom(length(susceptible_weighted_pop),size = susceptible_weighted_pop,prob=probability_infection)))
if (HPop$nExp[i] + HPop$nExposedToday[i] < HPop$nTotal[i] - HPop$nInf[i] - HPop$nRec[i] ) {
HPop$nExp[i] = HPop$nExp[i] + HPop$nExposedToday[i]
} else {
HPop$nExposedToday[i] = HPop$nTotal[i] - HPop$nInf[i] - HPop$nExp[i]- HPop$nRec[i]
HPop$nExp[i] = HPop$nTotal[i] - HPop$nInf[i] - HPop$nRec[i]
}
}
#print(paste0("Number of people newly exposed: ",sum(HPop$nExposedToday)))
HPop
}
####### Activity functions: Human movement ####
movementTimeStep = function(HPop, mobmat,day,control_df,prob_move_per_TS){
movement_matrix = matrix(0,NPat,NPat,dimnames=list(patIDs,patIDs))
daily_move = subset(mobmat,date == day)
daily_move = subset(daily_move,!is.na(fr_pat) & !is.na(to_pat) & !is.na(fr_users) & !is.na(movers))
daily_move_mat = daily_move[,is.element(names(daily_move),c("fr_pat","to_pat","fr_users","movers"))]
daily_move_mat = as.matrix(daily_move_mat)
col1 = which(colnames(daily_move_mat) == "fr_pat")
col2=which(colnames(daily_move_mat) == "to_pat")
colmove = which(colnames(daily_move_mat) == "movers")
colusers=which(colnames(daily_move_mat) == "fr_users")
movement_matrix[daily_move_mat[,c(col1,col2)]] = daily_move_mat[,colmove]/daily_move_mat[,colusers]
if (length(which(rowSums(movement_matrix)>1)) > 0){
print("Warning: row sums > 1 in movement matrix. Correcting...")
correctingrows = which(rowSums(movement_matrix)>1)
for (i in correctingrows){
movement_matrix[i,] = movement_matrix[i,] /sum(movement_matrix[i,] )
}
}
if (prob_move_per_TS > 0){
movement_matrix = movement_matrix*prob_move_per_TS
}
for (i in 1:length(patIDs)){
movement_matrix[i,i] = 1 - sum(movement_matrix[i,-i])
}
HPop$controlled = rep(0,length(HPop$names))
if (length(which(control_df$date == day)) > 0){
control_df_sub = subset(control_df,date == day)
if (dim(control_df_sub)[1] > 0){
for (i in 1:dim(control_df_sub)[1]){
HPop$controlled[which(HPop$names == control_df_sub$from[i])] = control_df_sub$relative_move[i]
}
}
}
if (sum(HPop$controlled)>0){
movement_matrix = stopMovement(HPop,movement_matrix,day)
}
#deterministic version
#HPop$nInfMovedToday = colSums(diag(HPop$nInf) %*% movement_matrix) - HPop$nInf
#HPop$nInf = colSums(diag(HPop$nInf) %*% movement_matrix)
HPop$nInf = ceiling(HPop$nInf)
# stochastic version
z <- rbinom(n=NPat^2,size = rep(HPop$nInf,each=NPat),prob = t(movement_matrix)[])
moved_matrix = t(matrix(z,NPat,NPat,dimnames=list(patIDs,patIDs)))
for (i in 1:dim(moved_matrix)[1]){
if (sum(moved_matrix[i,]) > 0){
moved_matrix[i,] = moved_matrix[i,]/sum(moved_matrix[i,]) * HPop$nInf[i]
}
}
HPop$nInfMovedToday = ceiling(colSums(moved_matrix))
HPop$nInf = HPop$nInf - floor(rowSums(moved_matrix)) + ceiling(colSums(moved_matrix))
#quick fix
for (i in 1:length(HPop$nInf)){
if (HPop$nInf[i] > HPop$nTotal[i]){
HPop$nInf[i] = HPop$nTotal[i]
}
}
#print(paste0("Number of infected people moving: ",sum(abs(HPop$nInfMovedToday))/2))
HPop
}
###### Response functions: Control
#relative_movement is the proportion of original movement out/in that we want to keep -- ie. .1 = 10% of original movement rate
stopMovement = function(HPop,mobmat,current_date){
stopping = which(HPop$controlled > 0)
if (length(stopping) > 0){
# print(paste("stopping movement in patches", HPop$names[stopping]))
for (ctrl_pat in stopping){
control_patches = HPop$IDs[ctrl_pat]
mobmat[control_patches,] = mobmat[control_patches,] * HPop$controlled[ctrl_pat]
mobmat[,control_patches] = mobmat[,control_patches] * HPop$controlled[ctrl_pat]
for (i in 1:length(HPop$IDs)){
mobmat[i,i] = 1 - sum(mobmat[i,-i])
}
}
}
mobmat
}
###### Master function ####
runSim = function(HPop,pat_info,control_info,mobmat,day_list,recrate_values,exposerate_df,exposepd,exposed_pop_inf_prop = 0,TSinday = 1,prob_move_per_TS=0) {
epidemic_curve <- data.frame(Date=as.Date(character()),
inf=c(),
stringsAsFactors=FALSE)
if (TSinday > 1){
#recrate_values$recrate = 1-(1-recrate_values$recrate)^(1/TSinday)
exposetoinfrate = 1/exposepd
exposepd = 1/(1 - exp(log(1-exposetoinfrate) / TSinday))
#recrate_values$recrate = 1 - ((1 - recrate_values$recrate) ^ (1/TSinday))
recrate_values$recrate = 1 - exp(log(1-recrate_values$recrate) / TSinday)
if (is.numeric(exposerate_df)){
# exposerate_df = 1-(1-exposerate_df)^(1/TSinday)
exposerate_df = exposerate_df/TSinday
# recrate_values$recrate = 1 - ((1 - recrate_values$recrate) ^ (1/TSinday))
}
if (is.data.frame(exposerate_df)){
# exposerate_df$exposerate = 1-(1-exposerate_df$exposerate)^(1/TSinday)
exposerate_df$exposerate = 1 - exp(log(1-exposerate_df$exposerate) / TSinday)
}
}
all_spread = matrix(0,length(day_list),length(HPop$nInf))
colnames(all_spread) = HPop$names
#print(all_dates)
for (current_day in 1:length(day_list)){
for (current_TS in 1:TSinday){
print(day_list[current_day])
HPop = recoveryTimeStep(HPop,recrate_values,day_list[current_day])
HPop = exposedtoinfTimeStep(HPop,1/exposepd)
HPop = exposedTimeStep(HPop,exposerate_df, day_list[current_day], exposed_pop_inf_prop)
HPop = movementTimeStep(HPop,mobmat,day_list[current_day],control_info,prob_move_per_TS)
}
#save(HPop,file=paste(current_day,".RData"))
epidemic_curve = rbind(epidemic_curve,data.frame(Date = day_list[current_day], inf = sum(HPop$nInf)))
all_spread[current_day,] = HPop$nInf
}
all_spread_2 = data.frame(dates = day_list,runday = 1:length(day_list))
all_spread_2= cbind(all_spread_2,all_spread)
list(HPop = HPop,epidemic_curve = epidemic_curve,all_spread=all_spread_2)
}
runSim_timespent = function(HPop,pat_info,control_info,TS_data,day_list,recrate_values,exposerate_df,exposepd,exposed_pop_inf_prop = 0,TSinday = 1) {
epidemic_curve <- data.frame(Date=as.Date(character()),
inf=c(),
stringsAsFactors=FALSE)
if (TSinday > 1){
#recrate_values$recrate = 1-(1-recrate_values$recrate)^(1/TSinday)
exposetoinfrate = 1/exposepd
exposepd = 1/(1 - exp(log(1-exposetoinfrate) / TSinday))
#recrate_values$recrate = 1 - ((1 - recrate_values$recrate) ^ (1/TSinday))
recrate_values$recrate = 1 - exp(log(1-recrate_values$recrate) / TSinday)
if (is.numeric(exposerate_df)){
# exposerate_df = 1-(1-exposerate_df)^(1/TSinday)
exposerate_df = exposerate_df/TSinday
# recrate_values$recrate = 1 - ((1 - recrate_values$recrate) ^ (1/TSinday))
}
if (is.data.frame(exposerate_df)){
# exposerate_df$exposerate = 1-(1-exposerate_df$exposerate)^(1/TSinday)
exposerate_df$exposerate = 1 - exp(log(1-exposerate_df$exposerate) / TSinday)
}
}
all_spread = matrix(0,length(day_list),length(HPop$nInf))
colnames(all_spread) = HPop$names
#print(all_dates)
for (current_day in 1:length(day_list)){
for (current_TS in 1:TSinday){
print(day_list[current_day])
HPop = recoveryTimeStep(HPop,recrate_values,day_list[current_day])
HPop = exposedtoinfTimeStep(HPop,1/exposepd)
HPop = exposedTimeStep_timespent(HPop,exposerate_df, day_list[current_day], exposed_pop_inf_prop,TS_data)
}
#save(HPop,file=paste(current_day,".RData"))
epidemic_curve = rbind(epidemic_curve,data.frame(Date = day_list[current_day], inf = sum(HPop$nInf)))
all_spread[current_day,] = HPop$nInf
}
all_spread_2 = data.frame(dates = day_list,runday = 1:length(day_list))
all_spread_2= cbind(all_spread_2,all_spread)
list(HPop = HPop,epidemic_curve = epidemic_curve,all_spread=all_spread_2)
}