Outputs (3)

Accelerating Bayesian inference for stochastic epidemic models using incidence data (2023)
Journal Article
Golightly, A., Wadkin, L. E., Whitaker, S. A., Baggaley, A. W., Parker, N. G., & Kypraios, T. (2023). Accelerating Bayesian inference for stochastic epidemic models using incidence data. Statistics and Computing, 33(6), Article 134. https://doi.org/10.1007/s11222-023-10311-6

We consider the case of performing Bayesian inference for stochastic epidemic compartment models, using incomplete time course data consisting of incidence counts that are either the number of new infections or removals in time intervals of fixed len... Read More about Accelerating Bayesian inference for stochastic epidemic models using incidence data.

Accelerating inference for stochastic kinetic models (2023)
Journal Article
Lowe, T., Golightly, A., & Sherlock, C. (2023). Accelerating inference for stochastic kinetic models. Computational Statistics & Data Analysis, 185, Article 107760. https://doi.org/10.1016/j.csda.2023.107760

Stochastic kinetic models (SKMs) are increasingly used to account for the inherent stochasticity exhibited by interacting populations of species in areas such as epidemiology, population ecology and systems biology. Species numbers are modelled using... Read More about Accelerating inference for stochastic kinetic models.

Quantifying Invasive Pest Dynamics through Inference of a Two-Node Epidemic Network Model (2023)
Journal Article
Wadkin, L. E., Golightly, A., Branson, J., Hoppit, A., Parker, N. G., & Baggaley, A. W. (2023). Quantifying Invasive Pest Dynamics through Inference of a Two-Node Epidemic Network Model. Diversity, 15(4), Article 496. https://doi.org/10.3390/d15040496

Invasive woodland pests have substantial ecological, economic, and social impacts, harming biodiversity and ecosystem services. Mathematical modelling informed by Bayesian inference can deepen our understanding of the fundamental behaviours of invasi... Read More about Quantifying Invasive Pest Dynamics through Inference of a Two-Node Epidemic Network Model.