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isolate_random_no_dftd.R
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# Libraries & WD---------------------------------------------------------------
#setwd("C:/Users/User/Desktop/MRes/Tasmanian Devils")
library(igraph)
argv = commandArgs(trailingOnly = T);
argc = length(argv);
if (argc < 2) {
stop("Please specify R, Z");
} else {
R = as.numeric(argv[1]);
Z = as.numeric(argv[2]);
}
# Parameters --------------------------------------------------------------
patchData = read.csv(file = "data/dry_wet_patch_areas.csv")
patchAreas = patchData$area / 1000000
patchDims = sqrt(patchAreas)
ExDens = patchData$density1996
meanExDens = mean(ExDens)
relDens = ExDens/meanExDens
baseDens = 53000/sum(relDens*patchAreas)
K = round(relDens * baseDens * patchAreas)
relDens86 = patchData$density1986 / meanExDens
startpop = round(patchAreas * relDens86 * baseDens)
Sex = c("Female", "Male")
MaxAge = 7*52
TumGrRate = 0.064
DispDist = 5
# import network topology ------------------------------------------------------
relations = read.csv("data/dry_wet_networktopology.csv")
g = graph.data.frame(relations, directed=F)
g$AdjList = get.adjlist(g, mode="out")
# Tumour growth function-----------------------------------------------------
TumGrowth = function(inds, TumGrRate){
for(j in 1:nrow(inds)){
V = inds$Tum.Vol[j]
newV = 202/(1+(202/V - 1)*exp(-TumGrRate*7))
inds$Tum.Vol[j] = newV
}
return(inds)
}
#Movement of individuals ------------------------------------------------------
Move.inds = function(inds, dispersal.dist, Boundary_X, Boundary_Y){
for(j in 1:nrow(inds)){
dist <- rnorm(1, dispersal.dist, dispersal.dist/10)
direct <- runif(1, 0, pi*2)
x.prev <- inds$XLoc[j]
y.prev <- inds$YLoc[j]
dx <- sin(direct) * dist
dy <- cos(direct) * dist
inds$XLoc[j] <- min(max((x.prev + dx), Boundary_X[1]), Boundary_X[2])
inds$YLoc[j] <- min(max((y.prev + dy), Boundary_Y[1]), Boundary_Y[2])
}
return(inds)
}
# Kill at max age --------------------------------------------------------------
mort.Max <- 0.012
OldAge = function(inds, MaxAge, K){
mort = c(0.011, 0.004, 0.006, 0.005, 0.012)
mortD = c()
if(nrow(inds) < K){mortD <- mort}
else{
for(i in 1:5){
mortD[i] <- min(mort[i] + 0.005*(nrow(inds)-K), mort.Max)
}
}
for(j in 1:nrow(inds)){
if(inds$Age[j] == MaxAge){inds$Death[j] = 1}
if(inds$Age[j] < 52){inds$Death[j] = sample(c(1,0), 1, prob = c(mortD[1], 1-mortD[1]))}
if(inds$Age[j] >= 52 & inds$Age[j] < 104){inds$Death[j] = sample(c(1,0), 1, prob = c(mortD[2], 1-mortD[2]))}
if(inds$Age[j] >= 104 & inds$Age[j] < 156){inds$Death[j] = sample(c(1,0), 1, prob = c(mortD[3], 1-mortD[3]))}
if(inds$Age[j] >= 156 & inds$Age[j] < 260){inds$Death[j] = sample(c(1,0), 1, prob = c(mortD[4], 1-mortD[4]))}
if(inds$Age[j] >= 260 & inds$Age[j] < MaxAge){inds$Death[j] = sample(c(1,0), 1, prob = c(mortD[5], 1-mortD[5]))}
}
inds = inds[inds$Death == 0,]
return(inds)
}
# Infections -------------------------------------------------------------------
Infection <- function(inds, infect.dist, BP){
TumVol.0 <- 0.0001
TumVol.max = 202
ind.S <- which(inds$Infected == 0)
nS <- length(ind.S)
ind.I <- which(inds$Infected > 0 & inds$Tum.Vol > TumVol.0 * 10)
nI <- length(ind.I)
N <- nrow(inds)
beta.ij = c()
if(nS > 0 & nI > 0){
for(i in 1:nS){
X.i <- inds$XLoc[ind.S[i]]
Y.i <- inds$YLoc[ind.S[i]]
if(inds$Age[ind.S[i]] < 52){ageinfi = 0}
else{ageinfi = BP}
for(j in 1:nI){
if(inds$Age[ind.I[j]] < 52){ageinfj = 0}
else{ageinfj = BP}
V.j <- inds$Tum.Vol[ind.I[j]]
X.j <- inds$XLoc[ind.I[j]]
Y.j <- inds$YLoc[ind.I[j]]
meet.ij <- as.numeric(sqrt((X.i - X.j)^2 + (Y.i - Y.j)^2) < infect.dist )
beta.ij[j] <- meet.ij * (V.j/TumVol.max) * ageinfi * ageinfj
}
# Force of infection
lambda.ind <- max(min(sum(beta.ij[], na.rm=T), 1), 0)
# Draw new infections status for individual i
infect.ind <- sample(c(1,0), 1, prob = c(lambda.ind, 1 - lambda.ind))
inds$Infected[ind.S[i]] <- infect.ind
inds$Tum.Vol[ind.S[i]] <- infect.ind * TumVol.0
}
}
return(inds)
}
# Kill as tumour load increases --------------------------------------------------
TumDeath = function(inds){
for(j in 1:nrow(inds)){
TumProp = (inds$Tum.Vol[j] / 202)^4
inds$Death[j] = sample(c(1,0), 1, prob = c(TumProp, 1-TumProp))
}
inds = inds[inds$Death == 0,]
return(inds)
}
#New Tasmanian Devil Births ----------------------------------------------------
Births = function(inds, Sex){
ind.mother_release <- inds[which(inds$Sex == "Female" & inds$Age > 52 & inds$Age < 52*5),]
n.mother.release <- nrow(ind.mother_release)
ind.father_release <- inds[which(inds$Sex == "Male" & inds$Age > 52 & inds$Age < 52*5),]
n.father.release <- nrow(ind.father_release)
if(n.mother.release > 0 & n.father.release > 0){
for(m in 1:n.mother.release){
fgenotype = sample(ind.father_release$Genotype, 1)
mgenotype = ind.mother_release$Genotype[m]
if(ind.mother_release$Age[m] >= 52 & ind.mother_release$Age[m] < 104){breedSuccess = 0.21}
if(ind.mother_release$Age[m] >= 104 & ind.mother_release$Age[m] < 156){breedSuccess = 0.71}
if(ind.mother_release$Age[m] >= 156){breedSuccess = 0.56}
breeder = sample(c(1,0), 1, prob = c(breedSuccess, 1 - breedSuccess))
if(breeder==1){
nyoung <- rbinom(1, 4, 0.64)
if(nyoung > 0){
offspring = setNames(data.frame(matrix(ncol = 9, nrow = nyoung)), c("Sex", "XLoc", "YLoc", "Tum.Vol", "Age", "Infected", "Death", "Dispersed", "Genotype"))
for(n in 1:nyoung){
offspring$Sex[n] = sample(Sex, 1)
offspring$XLoc[n] = ind.mother_release$XLoc[m]
offspring$YLoc[n] = ind.mother_release$YLoc[m]
offspring$Tum.Vol[n] = 0
offspring$Infected[n] = 0
offspring$Age[n] = sample(32:36, 1)
offspring$Death[n] = 0
offspring$Dispersed[n] = 0
offspring$Genotype[n] = sample(c(fgenotype, mgenotype), 1)
}
inds <- rbind(inds, offspring) # add new offspring to population
}
}
}
}
return(inds)
}
# Generate populations ---------------------------------------------------------
NewPop = function(startpop, poslocs, population){
inds = setNames(data.frame(matrix(ncol = 9, nrow = 0)), c("Sex", "XLoc", "YLoc", "Tum.Vol", "Age", "Infected", "Death", "Dispersed", "Genotype"))
while(nrow(inds) < startpop){
inds[nrow(inds)+1,] <- NA
}
for(i in 1:startpop){
inds$Sex[i] = sample(Sex, 1)
inds$XLoc[i] = sample(poslocs, 1)
inds$YLoc[i] = sample(poslocs, 1)
inds$Tum.Vol[i] = 0
inds$Infected[i] = 0
inds$Age[i] = as.numeric(sample(32:364, 1))
inds$Death[i] = 0
inds$Dispersed[i] = 0
inds$Genotype = population
}
return(inds)
}
# Measure genetic diversity --------------------------------------------------------------
genedivers = function(inds){
JScore = 0
for(y in unique(inds$Genotype)){
PiScore = nrow(inds[inds$Genotype == y,]) / nrow(inds)
JScore = JScore + PiScore^2
}
GenDiversity = 1 - JScore
return(GenDiversity)
}
# Run Simulation ---------------------------------------------------------------
RunModel = function(TumGrRate, DispDist, A, B, BP, K, MaxAge, patchDims, Sex, startpop, Time, g, R, Z){
print(paste("Contact Distance:", A, "Dispersal Probability:", B, "Bite Probability:", BP))
NData2 = setNames(data.frame(matrix(ncol = 9, nrow = 1)), c("TumGrowthRate", "Infect.Dist", "Diff.Dist", "DispProb", "MedianPop", "MedianInf", "Stability","Edges", "GenDiv"))
InfDist = A
Dispersal = B
# Define boundary of space
Boundary_X = list()
Boundary_Y = list()
poslocs = list()
for(s in 1:vcount(g)){
Boundary_X[[s]] <- c(0, patchDims[s])
Boundary_Y[[s]] <- c(0, patchDims[s])
poslocs[[s]] = 0:patchDims[s]
}
#lists for later
indslist = list()
poplist = list()
inflist = list()
agelist = list()
sexlist = list()
diverselist = list()
for(e in 1:vcount(g)){
poplist[[e]] = c(0,0,0,0)
inflist[[e]] = c(0,0,0,0)
agelist[[e]] = c(0,0,0,0)
sexlist[[e]] = c(0,0,0,0)
diverselist[[e]] = c(0,0,0,0)
}
#Set up populations
for(k in 1:vcount(g)){
indslist[[k]] = NewPop(startpop[k], poslocs[[k]], k)
}
#For each time step...
start <- Sys.time()
for(t in Time){
if(t == DFTDStart){
if(nrow(indslist[[84]][indslist[[84]]$Age > 52,]) > 3){
#Decide who's getting infected first
Popinitial = 84
Vinitial = sample(1:nrow(indslist[[Popinitial]][indslist[[Popinitial]]$Age > 52,]), 3, replace = FALSE)
for(e in 1:3){
indslist[[Popinitial]]$Tum.Vol[Vinitial[e]] = 0.0001
indslist[[Popinitial]]$Infected[Vinitial[e]] = 1
}
} else{ break() }
}
if(t == PatchIsolate){
nodes = V(g)
removenodes = as.numeric(sample(nodes, Z))
removeedges = E(g)[ from(removenodes) ]
g = delete_edges(g, removeedges)
g$AdjList = get.adjlist(g, mode="out")
print("edges removed")
}
print(t)
for(p in 1:vcount(g)){
#Dispersal between populations
if(nrow(indslist[[p]]) > 0){
neighbours <- g$AdjList[[p]]
Ninds = nrow(indslist[[p]])
DensDisp = Dispersal * min(Ninds/startpop[p], 1)
for(z in 1:Ninds){
if(indslist[[p]]$Age[z] < 52){
DProb = DensDisp*10
}else{
DProb = DensDisp
}
indslist[[p]]$Dispersed[z] = sample(c(1,0), 1, prob = c(DProb, 1 - DProb))
if(indslist[[p]]$Dispersed[z] == 1 & length(neighbours) > 0){
if(length(neighbours) == 1){disploc = as.numeric(neighbours)}
else{disploc = as.numeric(sample(neighbours, 1))}
disperser = data.frame(Sex = as.character(indslist[[p]]$Sex[z]),
XLoc = as.numeric(sample(poslocs[[disploc]], 1)),
YLoc = as.numeric(sample(poslocs[[disploc]], 1)),
Tum.Vol = indslist[[p]]$Tum.Vol[z],
Age = indslist[[p]]$Age[z],
Infected = indslist[[p]]$Infected[z],
Death = 0,
Dispersed = 0,
Genotype = indslist[[p]]$Genotype[z])
indslist[[disploc]] <- rbind(indslist[[disploc]], disperser)
}
}
}
indslist[[p]] = indslist[[p]][indslist[[p]]$Dispersed == 0,]
}
for(s in 1:vcount(g)){
#Within population stuff
if(nrow(indslist[[s]]) > 0){indslist[[s]] = Infection(indslist[[s]], InfDist, BP)}
if(nrow(indslist[[s]]) > 0){indslist[[s]] = Move.inds(indslist[[s]], DispDist, Boundary_X[[s]], Boundary_Y[[s]])}
if(nrow(indslist[[s]]) > 0){indslist[[s]] = TumDeath(indslist[[s]])}
if(nrow(indslist[[s]]) > 0){indslist[[s]] = TumGrowth(indslist[[s]], TumGrRate)}
if(nrow(indslist[[s]]) > 0){indslist[[s]] = OldAge(indslist[[s]], MaxAge, K[s])}
if(nrow(indslist[[s]]) > 0){indslist[[s]]$Age = indslist[[s]]$Age + 1}
if(nrow(indslist[[s]]) > 0){
if(t == 48){indslist[[s]] = Births(indslist[[s]], Sex)}
if((t %% 52) == 48){indslist[[s]] = Births(indslist[[s]], Sex)}
}
poplist[[s]][t] = nrow(indslist[[s]])
inflist[[s]][t] = sum(indslist[[s]]$Infected)
sexlist[[s]][t] = sum(as.numeric(factor(indslist[[s]]$Sex, levels=c('Female', 'Male')))-1)/nrow(indslist[[s]])
agelist[[s]][t] = mean(indslist[[s]]$Age)
diverselist[[s]][t] = genedivers(indslist[[s]])
}
}
print(Sys.time() - start)
#Graphs!
popultotal = c()
infectedstotal = c()
diversetotal = c()
for(h in Time){
popultotal[h]=0
infectedstotal[h] = 0
diversetotal[h] = 0
for(w in 1:vcount(g)){
popultotal[h] = popultotal[h] + poplist[[w]][h]
infectedstotal[h] = infectedstotal[h] + inflist[[w]][h]
diversetotal[h] = diversetotal[h] + diverselist[[w]][h]
}
}
propinf = infectedstotal / popultotal
islanddiversity = diversetotal / vcount(g)
NData2$TumGrowthRate[1] = TumGrRate
NData2$Infect.Dist[1] = InfDist
NData2$Diff.Dist[1] = DispDist
NData2$MedianPop[1] = median(tail(popultotal, 520))
NData2$MedianInf[1] = median(tail(propinf, 520))
NData2$DispProb[1] = Dispersal
NData2$Stability[1] = sd(tail(popultotal, 520))/mean(tail(popultotal, 520))
NData2$Edges[1] = ecount(g)
NData2$GenDiv[1] = median(tail(islanddiversity, 520))
save(agelist, sexlist, poplist, inflist, indslist, diverselist, NData2, file=paste0("Random_noDFTD_rep_",R,"_step_",Z,".rda"))
}
# Run --------------------------------------------------------------
#A = Contact distance
#B = dispersal probability
#BP = bite probability
#R = reps
Time = 1:(52*35)
DFTDStart = 52*10 #when disease is introduced
PatchIsolate = 52*20 #when patches are isolated
A = 0
B = 0.009
BP = 0
g = graph.data.frame(relations, directed=F)
g$AdjList = get.adjlist(g, mode="out")
RunModel(TumGrRate, DispDist, A, B, BP, K, MaxAge, patchDims, Sex, startpop, Time, g, R, Z)