model {
 	# Likelihood
	for (i in 1:Nareas) {
		for (k in 1:Ndiseases) {
			Y[i, k] ~ dpois(mu[i, k])
			log(mu[i, k]) <- log(E[i, k]) + alpha[k] + S[i,k]
  			RR[i,k] <- exp(alpha[k] + S[i,k])
		}
  	}

	for (i in 1:Nareas){
		for(k in 1:Ndiseases){
			S[i,k]<-inprod(tDelta[1:nAxes,i],MatDis[1:nAxes,k])
		}
	}

	for(j in 1:nAxes){
		tDelta[j,1:Nareas]~car.proper(ceros[],C[],adj[],num[],M[],1,gamma[j])
		gamma[j]~dunif(gamma.inf,gamma.sup)
	}
  	for(i in 1:Nareas){ceros[i]<-0}
	gamma.inf<-min.bound(C[],adj[],num[],M[])
	gamma.sup<-max.bound(C[],adj[],num[],M[])

	#Between-diseases structure
	#factor (one-factor) structure matrix for between-disease covariance
	#diagonal
	MatDis[1,1]~dunif(0,10);MatDis[1,2]~dunif(-10,10);MatDis[1,3]~dunif(-10,10)
	MatDis[2,1]~dunif(0,10);MatDis[3,2]~dunif(0,10);MatDis[4,3]~dunif(0,10)
	MatDis[2,2]<-0;MatDis[2,3]<-0;MatDis[3,1]<-0;MatDis[3,3]<-0;MatDis[4,1]<-0;MatDis[4,2]<-0

	# Other priors
	for (k in 1:Ndiseases) {
		alpha[k] ~ dflat()
	}
}