Christopher Drovandi

Bayesian Parameter Balancing Enables Robust and Consistent Estimation of Kinetic Parameter Uncertainty

The paper introduces a Bayesian parameter balancing framework to estimate kinetic parameter uncertainty with robustness and consistency across studies.

Predicting animal movement with deepSSF: a deep learning step selection framework

This paper introduces deepSSF, a deep learning framework for predicting animal movement by learning from environmental and temporal data, showing it can capture complex habitat interactions and temporal dynamics.

EEMtoolbox: A user-friendly R package for flexible ensemble ecosystem modeling

EEMtoolbox is an R package that enables flexible ensemble ecosystem modeling using either accept-reject or sequential Monte Carlo methods, supporting multiple model structures for conservation applications.

Predicting fine-scale distributions and emergent spatiotemporal patterns from temporally dynamic step selection simulations

This study shows that incorporating fine-scale temporal dynamics improves both hourly and seasonal spatial predictions of water buffalo movement and habitat use.

Calibration of Agent Based Models for Monophasic and Biphasic Tumour Growth using Approximate Bayesian Computation

This paper calibrates agent-based models of tumour growth using approximate Bayesian computation to estimate parameters and quantify uncertainty in biphasic growth models.

Estimating a novel stochastic model for within-field disease dynamics of banana bunchy top virus via approximate Bayesian computation

This paper develops a stochastic network-based model for BBTV spread in banana plantations and uses approximate Bayesian computation to estimate parameters, showing seasonality influenced by environmental factors.

Minimising severity of dengue serotype 1 infection bytransmissible interfering particles

This paper models the interaction between dengue virus and transmissible interfering particles to assess their potential as an antiviral agent, showing that 80% effectiveness requires a high dose to reduce infection severity.