Monitoring mosquito eco-systems and vector-control strategies using a stand-off optical sensor.

使用远距离光学传感器监测蚊子生态系统和病媒控制策略。

• 批准号: 10215105
• 负责人: Benjamin P Thomas
• 金额: $ 18.71万
• 依托单位: NEW JERSEY INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10215105
• 关键词: Aedes; Affect; Air; Area; Behavior; Biological; Cessation of life; Chemicals; Circadian Rhythms; Collaborations; Communicable Diseases; Controlled Environment; Culex (Genus); Culicidae; Data; Data Analyses; Dengue Fever; Development; Disadvantaged; Ecosystem; Effectiveness; Event; Evolution; Family; Female; Flying body movement; Frequencies; Gender; General Population; Gold; Gravidity; Habitats; Health; Human; Human Resources; Inferior; Insecta; Insecticides; Institutes; Knowledge; Laboratories; Lasers; Location; Malaria; Measurement; Measures; Methodology; Methods; Modification; Monitor; Mosquito-borne infectious disease; New Jersey; Noise; Optics; Outcome; Output; Performance; Periodicity; Population; Population Density; Population Dynamics; Population Sizes; Price; Property; Research; Residual state; Resolution; Seasons; Series; Signal Transduction; Site; System; Technology; Testing; Time; United States National Institutes of Health; Validation; Weather; West Nile virus; Wing; Zika Virus; atmospheric conditions; base; density; effectiveness evaluation; efficacy evaluation; egg; experimental study; improved; instrument; learning classifier; mortality; novel; optical sensor; programs; prototype; pyrethroid; rate of change; sensor; sex; simulation; supervised learning; vector; vector control; weather stations

Monitoring mosquito ecosystems and vector-control strategies using a stand-off optical sensor. PI: B. Thomas – NIH R21 Project Summary: Vector control strategies remain one of the most effective ways to protect human populations from the large number of mosquito borne diseases such as malaria, dengue fever, zika virus, or West Nile virus. Mosquito populations are generally monitored using physical traps, however this method suffers from many disadvantages. It requires long and expensive laboratory analysis by qualified personnel which drastically reduces the number of observed insects as well as time of trap deployment. Traps also provide a poor estimate of the actual population size or population density because the attractive range of traps is generally unknown and may change with weather conditions. These limitations are strong drawbacks in our ability to evaluate the effectiveness of various types of vector-control strategies (chemicals, biological, environmental modifications etc.). Inferior methods are not necessarily identified which ultimately contributes to the spread of infectious diseases. In this context, we argue that new methodologies to monitor insect population dynamics is key in the necessary effort to improve control program performance. A team from the New Jersey Institute of Technology in collaboration with the Hudson Mosquito Program seeks support to carry out a series of field experiments using a new optical sensor capable of identifying in real-time the family, species, and gender of mosquitoes in its field of view. The laser-based instrument is a dual-wavelength polarization-sensitive stand-off sensor. For each flying insect transiting through the infrared laser beams, the sensor can retrieve the optical properties of the wings and body of the insect as well as its wing beat frequency. Preliminary data from a laboratory prototype and numerical simulations indicate that the instrument, using a supervised machine learning classifier, can identify the species, gender, and gravidity of mosquitoes up to 300 m away. The instrument will be deployed in a high mosquito density area in New Jersey to continuously monitor the mosquito population over the whole season from April to October 2021. Continuous measurements will allow to identify a number of insects that is orders a magnitude higher than physical traps. As the probed volume of air is known, data analysis will provide the population density for each class of insects from which the population dynamics will be derived. In addition, the time and date of each insect transit allow to study the circadian rhythm, peak activities, and behavior as a function of atmospheric conditions measured by a weather station. In 2022, a similar experiment will be conducted at the same location while the Hudson Mosquito Program will conduct a vector control campaign targeting Culex and Aedes mosquitoes, both responsible for the spread of various infectious diseases. The impact of multiple applications of airborne pyrethroid insecticide on targeted and non-targeted insects will be evaluated by studying the mortality rates and population dynamics for each species. Both years, the data will be compared to physical traps on site, the current gold standard method, for further analysis and validation.

使用对峙光学监测蚊子生态系统和矢量控制策略 传感器。 PI：B。Thomas - NIH R21 项目摘要： 向量控制策略仍然是保护人口免受人口免受的最有效方法之一 大量蚊子传播疾病，例如疟疾，登革热，寨卡病毒或西尼罗河病毒。 通常使用物理陷阱对蚊子种群进行监测，但是这种方法遭受了许多 缺点。它需要合格人员的长时间昂贵的实验室分析 减少观察到的绝缘材料的数量以及陷阱部署的时间。陷阱还提供差的估计值 实际的人口规模或人口密度，因为陷阱的吸引人范围通常是未知的 并可能随天气条件而改变。这些限制是我们评估能力的强大缺点 各种类型的矢量控制策略的有效性（化学，生物，环境修饰 ETC。）。不一定要识别劣等方法，这最终导致传染性的传播 疾病。在这种情况下，我们认为监测昆虫种群动态的新方法是关键 提高控制计划绩效的必要努力。 新泽西理工学院与哈德逊蚊子合作的团队 计划寻求支持使用能够的新光学传感器进行一系列现场实验 实时识别蚊子在其视野中的家庭，物种和性别。基于激光 仪器是一种双波长极化敏感的隔离传感器。对于每个飞行绝缘过渡 通过红外激光束，传感器可以检索机翼和主体的光学特性 昆虫及其机翼节拍频率。实验室原型和数值的初步数据 模拟表明该仪器使用监督的机器学习分类器可以识别 多达300 m的蚊子的物种，性别和急性。该仪器将部署在高度 新泽西州的蚊子密度区域，以不断监测整个季节的蚊子种群 从2021年4月到1021年10月。连续测量将允许确定一些隔离材料 比物理陷阱高。众所周知探测的空气量，数据分析将提供 将得出种群动态的每类昆虫的种群密度。此外， 每个昆虫过境的时间和日期允许研究昼夜节律，峰值活动和行为 通过气象站测量的大气条件的功能。在2022年，类似的实验将是 在哈德逊蚊子计划将进行矢量控制运动时，在同一地点进行 靶向库尔克斯和艾德斯蚊子，均导致各种传染病的传播。这 空气中拟除虫菊酯杀虫剂对靶向和非靶向昆虫的多种应用的影响将是 通过研究每个物种的死亡率和人群动态来评估。两年，数据将是 与现场的物理陷阱（当前的黄金标准方法）相比，用于进一步分析和验证。