Parameter Inference for an Individual Based Model of Chytridiomycosis in Frogs

TL;DR

This paper demonstrates a likelihood-based method for parameter inference in individual-based models, using a frog disease spread model as a case study, enabling standard statistical techniques like MCMC.

Contribution

It introduces a novel approach to perform likelihood-based parameter inference for IBMs, which was previously lacking in the field.

Findings

Likelihood can be derived analytically under certain conditions.

Standard MCMC techniques can be applied for inference.

The method is demonstrated on a disease spread model in frogs.

Abstract

Individual Based Models (IBMs) and Agent Based Models (ABMs) have become widely used tools to understand complex biological systems. However, general methods of parameter inference for IBMs are not available. In this paper we show that it is possible to address this problem with a traditional likelihood-based approach, using an example of an IBM developed to describe the spread of Chytridiomycosis in a population of frogs as a case study. We show that if the IBM satisfies certain criteria we can find the likelihood (or posterior) analytically, and use standard computational techniques, such as Markov Chain Monte Carlo (MCMC), for parameter inference.