Investigating HLB control strategies using Genetic Algorithms: A two-orchard model approach with ACP Dispersal

TL;DR

This paper uses genetic algorithms within a two-orchard model to optimize control strategies against HLB, considering ACP dispersal and disease dynamics for improved citrus orchard management.

Contribution

It introduces a novel two-orchard model incorporating ACP mobility and derives a global reproduction number for sensitivity analysis, optimizing control strategies with genetic algorithms.

Findings

Optimal control parameters identified for mechanical and chemical strategies.

ACP mobility significantly influences disease spread and control effectiveness.

Mathematical expressions for cost and efficiency of strategies are provided.

Abstract

This study focuses on the use of genetic algorithms to optimize control parameters in two potential strategies called mechanical and chemical control, for mitigating the spread of Huanglongbing (HLB) in citrus orchards. By developing a two-orchard model that incorporates the dispersal of the Asian Citrus Psyllid (ACP), the cost functions and objective function are explored to assess the effectiveness of the proposed control strategies. The mobility of ACP is also taken into account to capture the disease dynamics more realistically. Additionally, a mathematical expression for the global reproduction number ($R_{0}$) is derived, allowing for sensitivity analysis of the model parameters when ACP mobility is present. Furthermore, we mathematically express the cost function and efficiency of the strategy in terms of the final size and individual $R_{0}$ of each patch (i.e., when ACP mobility is absent). The results obtained through the genetic algorithms reveal optimal parameters for each control strategy, providing valuable insights for decision-making in implementing effective control measures against HLB in citrus orchards. This study highlights the importance of optimizing control parameters in disease management in agriculture and provides a solid foundation for future research in developing disease control strategies based on genetic algorithms.