Smart Mosquito-Nets: A Natural Approach to Controlling Malaria Using Larvicidal Plant Extracts and Internet of Things

IF 2 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Juliet Onyinye Nwigwe, Kennedy Chinedu Okafor, Ogonna Christiana Ani, Titus Ifeanyi Chinebu, Okafor Ijeoma Peace, Omowunmi Mary Longe, Kelvin Anoh
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Abstract

Malaria mosquitoes, Anopheles, are well-known for carrying and spreading the malaria pathogens, known as Plasmodium. The public health challenge it brings has remained a global health challenge, of which the most robust control measures include mosquito-treated nets and electronic mosquito killer lamps. Due to health and cost problems, for example, in developing countries, these methods are not suitable for controlling mosquitoes and their plasmodiumic pathogens. In this study, we propose the use of two natural plant (e.g., Petiveria alliacea and Hyptis suavolens leaf) extracts that are cheap, ubiquitous, and effective for the control of mosquitoes, especially in temperate regions such as sub-Saharan Africa. On top of that, the study uses memory, non-locality, and fractal properties of fractal-fractional derivatives from compartmental modeling to capture susceptibility of infected persons, wider coverage, and heterogeneous breeding of mosquitoes, respectively, to evaluate the effectiveness of the two leaf extracts as natural larvicides against Anopheles mosquitoes. To measure the effectiveness of the two plant extracts in controlling malaria, this study develops a basic reproduction number model of Anopheles mosquitoes and evaluates the endemic points of the model. Comparing the results of larvicidal control with those of mosquito-treated nets, the proposed larvicidal control achieved 94.86% efficacy when applied alone and 96.83% efficacy when combined with mosquito nets, each outperforming mosquito nets (83.33%). These findings position compartmental fractal fractional-order modeling as an innovative tool for bioinformatic disease vector control. The study also presents a smart mosquito-net model where data collected from the host nodes on the performance of larvicides in mosquito and malaria control are transmitted via the Internet of Things infrastructure to the edge and cloud servers for computation, processing, artificial intelligence analytics, and policy-making.

Abstract Image

智能蚊帐:利用杀幼虫植物提取物和物联网控制疟疾的自然方法
疟蚊,即按蚊,以携带和传播疟疾病原体(即疟原虫)而闻名。它带来的公共卫生挑战仍然是一项全球卫生挑战,其中最有力的控制措施包括经蚊处理的蚊帐和电子灭蚊灯。例如,由于健康和成本问题,在发展中国家,这些方法不适合控制蚊子及其疟原虫病原体。在这项研究中,我们建议使用两种天然植物(例如,葱茎小藤和小泡藤叶)提取物,这两种植物廉价、普遍且有效地控制蚊子,特别是在温带地区,如撒哈拉以南非洲。在此基础上,本研究利用区室模型的分形-分数阶衍生物的记忆性、非局域性和分形特性,分别捕捉感染者的易感性、更广泛的覆盖范围和蚊子的异质繁殖,以评估两种叶提取物作为天然杀幼虫剂对按蚊的有效性。为了衡量两种植物提取物对疟疾的控制效果,本研究建立了按蚊基本繁殖数模型,并对模型的流行点进行了评价。与经蚊处理的蚊帐对照,单用和联用蚊帐的杀幼虫效果分别为94.86%和96.83%,均优于蚊帐的83.33%。这些发现将分形分数阶模型定位为生物信息学病媒控制的创新工具。该研究还提出了一种智能蚊帐模型,通过物联网基础设施将从主机节点收集的杀幼虫剂在蚊虫和疟疾控制方面的性能数据传输到边缘和云服务器,用于计算、处理、人工智能分析和决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
5.10
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审稿时长
19 weeks
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