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

TL;DR

This paper introduces a stochastic network-based model for BBTV spread in banana plantations, using approximate Bayesian computation to incorporate seasonality and inform disease management strategies.

Contribution

A novel stochastic model for BBTV dynamics that accounts for seasonal variations and uses ABC for parameter estimation, improving understanding of disease spread.

Findings

Seasonality affects all model parameters.

Model captures the influence of temperature and aphid activity.

Supports disease monitoring and policy decision-making.

Abstract

The Banana Bunchy Top Virus (BBTV) is one of the most economically important vector-borne banana diseases throughout the Asia-Pacific Basin and presents a significant challenge to the agricultural sector. Current models of BBTV are largely deterministic, limited by an incomplete understanding of interactions in complex natural systems, and the appropriate identification of parameters. A stochastic network-based Susceptible-Infected model has been created which simulates the spread of BBTV across the subsections of a banana plantation, parameterising nodal recovery, neighbouring and distant infectivity across summer and winter. Findings from posterior results achieved through Markov Chain Monte Carlo approach to approximate Bayesian computation suggest seasonality in all parameters, which are influenced by correlated changes in inspection accuracy, temperatures and aphid activity. This paper demonstrates how the model may be used for monitoring and forecasting of various disease management strategies to support policy-level decision making.