利用 SAIR-SI 分区模型、流行病最终规模和遗传算法优化柑橘园黄龙病的控制参数。

IF 2.2 4区 数学 Q2 BIOLOGY
Andrés Anzo Hernández, Uvencio José Giménez Mujica, Carlos Arturo Hernández Gracidas, José Jacobo Oliveros Oliveros
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引用次数: 0

摘要

黄龙病(HLB)是一种影响全球柑橘树的细菌性疾病。我们提出了一种创新方法,用于确定柑橘园 HLB 的最佳控制和风险措施。我们的方法以一个数学模型为基础,该模型结合了蛆树的数量和亚洲柑橘壁虱(ACP)动态的逻辑生长模型,亚洲柑橘壁虱是 HLB 的主要传播媒介。我们得出了以下表达式(1) 基本繁殖数量 R 0;(2) 招引树数量的最终规模;以及 (3) 传播风险。通过以上分析,我们提出了一个差分图方程,它能以较低的计算成本评估最终规模。我们在进化算法中使用该差值图来确定减少 HLB 传播的最有效控制参数值组合,包括套袋的时间和频率以及杀虫剂的使用。在这个意义上,我们提出了两种控制策略,分别称为以树为中心和以病媒为中心。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing control parameters for Huanglongbing disease in citrus orchards using SAIR-SI compartmental model, epidemic final size, and genetic algorithms.

Huanglongbing (HLB) is a bacterial disease that affects citrus trees worldwide. We present an innovative approach for identifying optimal control and risk measures for HLB in citrus orchards. Our method is based on a mathematical model that incorporates the number of roguing trees and a logistic growth model for the dynamic of the Asian Citrus Psyllid (ACP), the primary vector for HLB transmission. We derive an expression for: (1) the basic reproduction number R 0 ; (2) the final size for the number of roguing trees; and (3) the transmission risk. The above let us propose a difference map equation that assesses this final size with a low computational cost. We use this difference map in an evolutionary algorithm to identify the most effective combination of control parameter values for reducing HLB transmission, including the timing and frequency of roguing and the use of insecticides. In this sense, we propose two control strategies, which we called tree-centered and vector-centered.

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来源期刊
CiteScore
3.30
自引率
5.30%
发文量
120
审稿时长
6 months
期刊介绍: The Journal of Mathematical Biology focuses on mathematical biology - work that uses mathematical approaches to gain biological understanding or explain biological phenomena. Areas of biology covered include, but are not restricted to, cell biology, physiology, development, neurobiology, genetics and population genetics, population biology, ecology, behavioural biology, evolution, epidemiology, immunology, molecular biology, biofluids, DNA and protein structure and function. All mathematical approaches including computational and visualization approaches are appropriate.
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