title: A many-body field theory approach to stochastic models in population biology.
abstract: BACKGROUND
  Many models used in theoretical ecology, or mathematical epidemiology are stochastic, and may also be spatially-explicit. Techniques from quantum field theory have been used before in reaction-diffusion systems, principally to investigate their critical behavior. Here we argue that they make many calculations easier and are a possible starting point for new approximations.
  
  
  METHODS
  We review the many-body field formalism for Markov processes and illustrate how to apply it to a 'Brownian bug' population model, and to an epidemic model. We show how the master equation and the moment hierarchy can both be written in particularly compact forms. The introduction of functional methods allows the systematic computation of the effective action, which gives the dynamics of mean quantities. We obtain the 1-loop approximation to the effective action for general (space-) translation invariant systems, and thus approximations to the non-equilibrium dynamics of the mean fields.
  
  
  CONCLUSIONS
  The master equations for spatial stochastic systems normally take a neater form in the many-body field formalism. One can write down the dynamics for generating functional of physically-relevant moments, equivalent to the whole moment hierarchy. The 1-loop dynamics of the mean fields are the same as those of a particular moment-closure.
authors.affiliation: Department of Infectious Disease Epidemiology, MRC Centre for Outbreak Analysis and Modelling, Imperial College London, London, United Kingdom. peter.dodd@imperial.ac.uk
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Dodd
authors.forename: Neil M
authors.initials: NM
authors.lastname: Ferguson
mesh_terms: D000465:Algorithms; D000818:Animals; D001695:Biology / Q000379:methods*; D003198:Computer Simulation; D004058:Diffusion; D004196:Disease Outbreaks; D004463:Ecology; D004813:Epidemiology; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D008962:Models, Theoretical; D011157:Population Dynamics; D013269:Stochastic Processes