Spatial Bernoulli infection model ($P_{it}=1-e^{-\alpha A_{it}}$)

Given the currently infected set $I_t$ and susceptible set $S_t$, define $A_{it}(\eta )={\sum }_{j\in I_t}{K}_{\eta }(d_{ij})$ and $P_{it}=1-e^{-\alpha A_{it}}$. Conditionally independent new-infection indicators $Y_{it}$ have likelihoodA nonnegative infection intensity requires $\alpha \ge 0$; an unrestricted normal distribution prior distribution for $\alpha$ must therefore be restricted or replaced. With $K_{\beta }(d)=d^{-\beta }$ or $K_{\gamma }(d)=e^{-\gamma d}$ and positive distances, the conditional posterior kernels are not generally conjugate normal probability densities. Metropolis–Hastings algorithm updates, optionally on $\log \alpha$ with its Jacobian determinant, provide a direct sampling method.