title: Comparing approximations to spatio-temporal models for epidemics with local spread.
abstract: Analytical methods for predicting and exploring the dynamics of stochastic, spatially interacting populations have proven to have useful application in epidemiology and ecology. An important development has been the increasing interest in spatially explicit models, which require more advanced analytical techniques than the usual mean-field or mass-action approaches. The general principle is the derivation of differential equations describing the evolution of the expected population size and other statistics. As a result of spatial interactions no closed set of equations is obtained. Nevertheless, approximate solutions are possible using closure relations for truncation. Here we review and report recent progress on closure approximations applicable to lattice models with nearest-neighbour interactions, including cluster approximations and elaborations on the pair (or pairwise) approximation. This study is made in the context of an SIS model for plant-disease epidemics introduced in Filipe and Gibson (1998, Studying and approximating spatio-temporal models for epidemic spread and control, Phil. Trans. R. Soc. Lond. B 353, 2153-2162) of which the contact process [Harris, T. E. (1974), Contact interactions on a lattice, Ann. Prob. 2, 969] is a special case. The various methods of approximation are derived and explained and their predictions are compared and tested against simulation. The merits and limitations of the various approximations are discussed. A hybrid pairwise approximation is shown to provide the best predictions of transient and long-term, stationary behaviour over the whole parameter range of the model.
authors.affiliation: Biomathematics & Statistics Scotland, Edinburgh, UK. jf263@cam.ac.uk
authors.forename: J A
authors.initials: JA
authors.lastname: Filipe
authors.forename: G J
authors.initials: GJ
authors.lastname: Gibson
mesh_terms: D016000:Cluster Analysis; D004463:Ecology; D004812:Epidemiologic Methods; D008954:Models, Biological; D010935:Plant Diseases; D013269:Stochastic Processes