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]<-sigma[k]*inprod(tDelta[1:k,i],MatDis[1:k,k])
		}
	}

	for(j in 1:Ndiseases){
  		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
	MatDis[1,1]<-1/sqrt(3);	MatDis[2,1]<-1/sqrt(3);	MatDis[3,1]<-1/sqrt(3)
	MatDis[1,2]<-1/sqrt(2);	MatDis[2,2]<- -1/sqrt(2); MatDis[3,2]<-0	
	MatDis[1,3]<--1/sqrt(6); MatDis[2,3]<- -1/sqrt(6); MatDis[3,3]<-2/sqrt(6)

	#Other priors
	for (k in 1:Ndiseases) {
		alpha[k] ~ dflat()
		sigma[k] ~ dunif(0,10)
	}
}