天空中的眼睛:如何在疟疾矢量控制中使用无人机

A new study investigates the practicalities of using drones to identify按蚊马拉维的幼虫栖息地。

疟疾继续对马拉维的人们,尤其是居住在农村地区的人们产生重大影响。在农村地区,栖息地最适合主要的疟疾媒介,Anopheles funestusandAnopheles Gambiae。他们的栖息地偏好有所不同,女性一个。funestus蚊子更喜欢将鸡蛋放在带有紧急植被的大型永久性水体中,而一个。冈比亚females prefer shallow, artificial, or naturally forming pools of standing water.

影响当地的天气状况,蚊子abundance (and subsequent malaria risk) ebbs and flows throughout the year. During wet periods the landscape is carpeted with suitable larval habitat, which then dwindles to almost nothing once the rains recede. Yet, malaria transmission persists in these drier periods, albeit at lower rates. So, where does the female mosquito go to lay her eggs when its dry? The answer to this question would not only allow malaria control programmes to know who’s most at risk of malaria during the dry season but could also be used to help prioritise intervention activities in these higher risk areas.

Identifying potential larval habitat can be slow work, as mosquitoes can access areas more easily than humans can. Local knowledge can help, but how can we know if all possible sites have been found? This is where drones may have a role to play. Drones, or rather cameras attached to drones, can capture extremely detailed images of the landscape, opening the possibility of replacing the time-consuming hunt for mosquito larvae on the ground with identifying habitat through aerial imagery. There’s a price to pay in swapping out low tech ground-based searches with high tech drone-supported habitat mapping. Our study set out to explore the pros and cons of incorporating drones into the arsenal of malaria vector control tools, while learning more about生态影响在干燥季节疟疾传播中。

在2018年至2020年之间,该团队三次访问马拉维中部的Kasungu区,该地区是联合国儿童基金会和马拉维政府的一部分人道主义无人机测试走廊我们捕获了该地区众多水坝中10个的高分辨率航拍图像及其周围的景观,以了解有关使用该技术的实用性的更多信息,以准备一项更大的研究,该研究于2021年4月推出Maladrone– a collaboration between Liverpool School of Tropical Medicine and Malawi-Liverpool-Wellcome Trust Clinical Research Programme, supported by the Kasungu district health office. We wanted to know which type of drone should be used (fixed wing, or rotor), what sensors allowed us to identify water most accurately in the captured images (standard camera, near infrared sensor), and which method could most easily and accurately identify water in the image (manual review, or an ‘automatic’ classification method called基于对象的分类)。

除此之外,我们还收集了幼虫,以帮助我们更多地了解我们可能在何处和何时找到蚊子,并将我们在地面上看到的东西与我们从空中看到的东西联系起来。

An example of drone imagery captured during our ‘fact finding’ visits to the ‘Humanitarian drone testing corridor’ in Kasungu district, Malawi. Classification is undertaken using an object-based approach.

对于疾病队来说,这是一次很棒的学习经历。细节在paperpublished in疟疾杂志,但真正为我们带回家的消息是:

  1. 无人机图像非常有用,使我们能够确定储层的哪些部分包含与蚊子幼虫相关的水生植被,并在周围地区识别小水池,以及
  2. 由于我们在捕获和管理图像方面遇到的技术挑战,我们认为,与其直接努力将这项技术直接纳入疟疾控制计划,而是最好留给无人机专家!

幸运的是,在马拉维,大量的时间和金钱投入到捕获和分析无人机图像以及从头开始构建无人机的能力不断增长。一个这样的倡议是非洲无人机和数据学院(ADDA),,,,a collaboration between UNICEF, MUST (Malawi University of Science and Technology) and Virginia Tech (USA), with contributions from the Maladrone team.

Participants of ADDA courses have the opportunity to obtain formal drone pilot qualifications, and our project is now employing these pilots for the Maladrone project via the ‘crowd-droning’ organisationGlobhe。由于我们的团队由具有有限无人机技能的流行病学家和昆虫学家组成,因此我们到目前为止的经验表明,将本地无人机专业知识与国家疟疾和媒介控制计划的需求相结合可能是必不可少的!

在广泛的社区参与活动(左)之后,在社区志愿者的支持下,疾病小组继续在卡桑古(Kasungu)进行样品采样成人(中)和幼虫(右)蚊子,以了解有关旱季疟疾风险中的空间模式的更多信息。

现在,我们正在捕获纵向干旱季节的数据中幼虫存在和成年蚊子丰度的纵向数据,距离小水库0到2.5公里,以了解这种“人造”幼虫栖息地对周围疟疾风险的影响有多大。无人机图像使我们能够随着越来越干燥的范围捕获不断变化的环境的细节,帮助团队确定区域以集中我们的幼虫采样工作。

Preliminary analysis of the data indicates the effect of the reservoir on biting risk is quite striking, with mosquito abundance staying high in areas closest to the reservoir, and we hope to publish our results towards the end of the year.

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