	model 
	{
		for (i in 1:I)  {
			cases[i]        ~ dpois(mu[i])
			log(mu[i])     <- log(pyr[i]) + alpha[age[i]] + beta[year[i]]
		}
		betamean[1]    <- 2 * beta[2] - beta[3]
		Nneighs[1]     <- 1
		betamean[2]    <- (2 * beta[1] + 4 * beta[3] - beta[4]) / 5
		Nneighs[2]     <- 5
		for (k in 3 : K - 2)  {
			betamean[k]    <- (4 * beta[k - 1] + 4 * beta[k + 1]- beta[k - 2] - beta[k + 2]) / 6
			Nneighs[k]     <- 6
		}
		betamean[K - 1]  <- (2 * beta[K] + 4 * beta[K - 2] - beta[K - 3]) / 5
		Nneighs[K - 1]   <- 5
		betamean[K]    <- 2 * beta[K - 1] - beta[K - 2]  
		Nneighs[K]     <- 1
		for (k in 1 : K)  {
			betaprec[k]    <- Nneighs[k] * tau
		}
		for (k in 1 : K)  {
			beta[k]        ~ dnorm(betamean[k], betaprec[k])
			logRR[k]      <- beta[k] - beta[5]
			tau.like[k]   <- Nneighs[k] * beta[k] * (beta[k] - betamean[k])
		}
		alpha[1]      <- 0.0
		for (j in 2 : Nage)  {
			alpha[j]       ~ dnorm(0, 1.0E-6)
		}
		d <- 0.0001 + sum(tau.like[]) / 2
		r <- 0.0001 + K / 2
		tau  ~ dgamma(r, d)
		sigma <- 1 / sqrt(tau)
	}
