Population dispersion and equilibrium infection frequency in a spatial epidemic

Spatially detailed epidemic models commonly invoke probabilistic cellular a utomata to predict population-level consequences of localized interactions between infectious and susceptible individuals. Most such models equate loc al and global host density; the resulting spatial uniformity implies that e ach individual interacts with the same number of neighbors. However, many n atural populations exhibit a heterogeneous spatial dispersion, so that the number of contacts capable of transmitting an infection will vary among int eraction neighborhoods. We analyze the impact of this variation with a prob abilistic cellular automaton that simulates a spatial epidemic with recover y. We find that increasing spatial heterogeneity in host density decreases the frequency of infection at endemic equilibrium, and consequently increas es the divergence between mean-field predictions and observed levels of inf ection. (C)1999 Elsevier Science B.V. All rights reserved.