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优化杀虫剂部署策略,延迟蚊子种群的定量耐药性

Sylvère Kezeta-Bondja^1,2, Charles S Wondji^1,3, Ramsès Djidjou-Demasse^4,5

¹Centre for Research in Infectious Diseases, Yaoundé, Yaoundé, Cameroon.

Journal of mathematical biology

|February 22, 2026

概括

控制疟疾载体的最佳杀虫剂部署取决于耐药性水平. 高耐药性需要完全覆盖,而低耐药性受益于早期覆盖率的降低和不同杀虫剂的顺序使用,以延迟耐药性演变.

科学领域:

矢量控制是指向量控制的方法.
数学建模的数学建模
昆虫杀虫剂耐药性昆虫杀虫剂耐药性

背景情况:

杀虫剂对于控制疟疾载体至关重要.
蚊子中的定量杀虫剂耐药性 (qIR) 威胁着长期的有效性.
了解阻力演变是持续向量控制的关键.

研究的目的:

开发一个数学模型,以最好地部署两种杀虫剂.
确定减轻定量化杀虫剂耐药性 (qIR) 演变的策略.
为控制疟疾载体的杀虫剂进行适应性管理提供信息.

主要方法:

蚊子种群和耐药性的数学建模.
对连续时期的杀虫剂部署策略的分析.
基于突变变异和暴露史的耐药性演变的模拟.

主要成果:

高突变变异性有利于最有效的杀虫剂完全覆盖.
低突变变异性需要暂时减少覆盖范围,并考虑相对有效性和暴露.
顺序部署杀虫剂通常优于同时使用以延迟耐药性.

结论:

适应性策略对于管理抗虫剂耐药性至关重要.
最佳的杀虫剂部署因耐药水平和杀虫剂特性而有所不同.
连续使用杀虫剂延长了效果,并改善了长期的疟疾载体控制.

关键词:

适应性杀虫剂管理适应性杀虫剂管理非线性动态系统非线性动态系统最佳控制策略的最佳控制策略定量化杀虫剂耐药性的研究.结构化的数学模型.

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