Fitting a managed population model using ABC

Jones, Owen ORCID: https://orcid.org/0000-0002-7300-5510, Cable, Joanne ORCID: https://orcid.org/0000-0002-8510-7055 and Hayes, Laura 2025. Fitting a managed population model using ABC. Triantafyllou, Ioannis S., Malefaki, Sonia and Karagrigoriou, Alex, eds. Stochastic Modeling and Statistical Methods: Advances and Applications, Advances in Reliability Science, Elsevier, pp. 299-314. (10.1016/B978-0-44-331694-4.00020-7)

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Abstract

In many practical situations, we can construct stochastic models, which concisely reflect our understanding of the system's dynamics, but which are nonetheless too complicated to investigate analytically. Approximate Bayesian computation (ABC) can provide a route for estimation and inference in such situations, as it only requires the ability to simulate from the model as opposed to being able to calculate the likelihood. Even so, successful application of ABC requires adaptation to the problem at hand—in particular with regards to how we measure the distance between real and simulated data—and we illustrate this with a stochastic population model. The Cardiff University Pathogen Lab has been culturing different groups of Gyrodactylus populations since 1997. The parasite populations are closely managed so that they do not go extinct or place an inhumane burden on their fish hosts. Parasites are also periodically removed from the culture to provide subjects for various experiments. In this work, we use ABC to estimate the population dynamics of three different groups of gyrodactylids—lab-bred Gyrodactylus turnbulli, wild-type Gyrodactylus turnbulli, and Gyrodactylus bullatarudis—using lab records of parasite population numbers and the deaths of host fish. Our model includes a novel mechanism that captures population management interventions.

Item Type:	Book Section
Date Type:	Publication
Status:	Published
Schools:	Schools > Biosciences Schools > Mathematics
Publisher:	Elsevier
ISBN:	978-0-443-31694-4
Last Modified:	03 Nov 2025 09:39
URI:	https://orca.cardiff.ac.uk/id/eprint/179321

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