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.

使用离地光学仪器监测蚊子生态系统和病媒控制策略 传感器. PI：B.托马斯- NIH R21 项目概要： 病媒控制战略仍然是保护人类免受 许多蚊子传播的疾病，如疟疾、登革热、寨卡病毒或西尼罗河病毒。 通常使用物理陷阱监测苔藓虫种群，然而这种方法存在许多缺点， 缺点它需要由合格的人员进行长期和昂贵的实验室分析， 减少了观察到的昆虫的数量以及诱捕器部署的时间。陷阱也提供了一个贫穷的估计 因为诱捕器的吸引范围通常是未知的， 并且可以随天气条件而改变。这些局限性是我们评估 各种病媒控制战略（化学品、生物、环境改造）的有效性 等）。不一定能识别出最终导致传染性疾病传播的劣质方法。 疾病在这种情况下，我们认为，新的方法来监测昆虫种群动态是关键， 为改善控制程序性能所做的必要努力。 来自新泽西理工学院的一个团队与哈德逊莫斯特大学合作 该计划寻求支持，使用一种新的光学传感器进行一系列现场实验， 实时识别其视野中蚊子的科、种类和性别。基于激光的 仪器是双波长偏振敏感的支座传感器。对于每一个飞行昆虫过境 通过红外激光束，传感器可以检索机翼和机身的光学特性， 昆虫以及它的翅膀拍打频率。来自实验室原型和数值模拟的初步数据 模拟表明，该仪器，使用监督机器学习分类器，可以识别 蚊子的种类、性别和怀孕情况，最远可达300米。该仪器将部署在一个高 新泽西的蚊子密度区，以持续监测整个季节的蚊子数量 2021年4月至10月。连续的测量将允许识别一些昆虫， 比物理陷阱更高的数量级。由于探测的空气体积是已知的，数据分析将提供 每类昆虫的种群密度，从中可以得出种群动态。此外，本发明还提供了一种方法， 每个昆虫过境的时间和日期允许研究昼夜节律，高峰活动和行为， 由气象站测量的大气条件的函数。2022年，类似的实验将在 在同一地点进行，而哈德逊莫斯特计划将进行病媒控制运动 以库蚊和伊蚊为目标，这两种蚊子都是传播各种传染病的罪魁祸首。的 多次使用空气传播拟除虫菊酯类杀虫剂对目标昆虫和非目标昆虫的影响将 通过研究每个物种的死亡率和种群动态来评估。这两年的数据将是 与现场的物理陷阱相比，目前的金标准方法，用于进一步分析和验证。