背景:疟疾,一种由疟原虫原虫寄生虫引起的致命疾病,通过被感染的雌性按蚊叮咬传播,在撒哈拉以南非洲仍然是一个重大的公共卫生挑战。消除疟疾的努力越来越侧重于使用杀虫剂控制病媒。然而,疟疾媒介中杀虫剂抗性(IR)的出现构成了一个巨大的障碍,当前的红外映射模型保持静态,依靠固定系数。这项研究引入了一种动态的时空方法来表征冈比亚按蚊复合体和阿拉伯按蚊的表型抗性。我们开发了一个细胞自动机(CA)模型,并将其应用于从埃塞俄比亚收集的数据,尼日利亚,喀麦隆,乍得,布基纳法索。数据包括地理参考记录,详细说明了各种杀虫剂中蚊子媒介种群的IR水平。在表征已确认抗性的动态模式时,我们通过相关性分析确定了关键驱动因素,卡方检验,和广泛的文献综述。
结果:CA模型在捕获矢量种群中确认的IR状态的时空动力学方面表现出鲁棒性。在我们的模型中,主要驱动因素包括杀虫剂的使用,农业活动,人口密度,土地利用和土地覆盖(LULC)特征,和环境变量。
结论:所开发的CA模型为在疟疾媒介中确认IR的数据有限的国家提供了一个强有力的工具。采用动态建模方法,并考虑不断变化的条件和影响,有助于更深入地理解红外动力学,并可以为疟疾病媒控制提供有效的策略,以及在面临这一关键健康挑战的地区进行预防。
BACKGROUND: Malaria, a deadly disease caused by Plasmodium protozoa parasite and transmitted through bites of infected female Anopheles mosquitoes, remains a significant public health challenge in sub-Saharan Africa. Efforts to eliminate malaria have increasingly focused on vector control using insecticides. However, the emergence of insecticide resistance (IR) in malaria vectors pose a formidable obstacle, and the current IR mapping models remain static, relying on fixed coefficients. This study introduces a dynamic spatio-temporal approach to characterize phenotypic resistance in Anopheles gambiae complex and Anopheles arabiensis. We developed a cellular automata (CA) model and applied it to data collected from Ethiopia, Nigeria, Cameroon, Chad, and Burkina Faso. The data encompasses georeferenced records detailing IR levels in mosquito vector populations across various classes of insecticides. In characterizing the dynamic patterns of confirmed resistance, we identified key driving factors through correlation analysis, chi-square tests, and extensive literature review.
RESULTS: The CA model demonstrated robustness in capturing the spatio-temporal dynamics of confirmed IR states in the vector populations. In our model, the key driving factors included insecticide usage, agricultural activities, human population density, Land Use and Land Cover (LULC) characteristics, and environmental variables.
CONCLUSIONS: The CA model developed offers a robust tool for countries that have limited data on confirmed IR in malaria vectors. The embrace of a dynamical modeling approach and accounting for evolving conditions and influences, contribute to deeper understanding of IR dynamics, and can inform effective strategies for malaria vector control, and prevention in regions facing this critical health challenge.