Next generation mosquito control through technology-driven trap development and artificial intelligence guided detection of mosquito breeding habitats

通过技术驱动的诱捕器开发和人工智能引导的蚊子繁殖栖息地检测来控制下一代蚊子

• 批准号: 10490916
• 负责人: Sarah Murphy Gunter
• 金额: $ 74.97万
• 依托单位: UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-17 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10490916
• 关键词: Abate; Aedes; Algorithms; Aptitude; Arbovirus Infections; Arboviruses; Area; Artificial Intelligence; Bite; Breeding; Budgets; Centers for Disease Control and Prevention (U.S.); Cessation of life; Characteristics; Chikungunya virus; Cities; Climate; Clinical; Collection; Comparative Study; Competence; Complex; Compost; Culicidae; Data; Decision Making; Dengue; Dengue Virus; Detection; Development; Discipline; Disease; Disease Outbreaks; Ecology; Economics; Environment; Environmental Health; Environmental Impact; Epidemic; Evaluation; Event; Eye; Food; Gases; Geography; Gravid; Growth; Habitats; Health; Health Benefit; Household; Human; Human Bites; Image Analysis; Imagery; Impairment; Individual; Industry; Infrastructure; Insecticide Resistance; Insecticides; Intervention; Lasers; Life; Location; Machine Learning; Manuals; Maps; Methods; Microclimate; Mosquito Control; Mosquito-borne infectious disease; Natural Disasters; Neighborhoods; Neurologic; Oils; Outcome; Pattern; Persons; Pilot Projects; Population; Prevention; Preventive vaccine; Process; Protocols documentation; Public Health; Reaction; Recovery; Reproducibility; Resolution; Resources; Risk; Robotics; Security; Sentinel; Side; Site; Solid; Sorting - Cell Movement; Surveillance Methods; System; Techniques; Technology; Testing; Time; Toy; United States National Aeronautics and Space Administration; Visualization; Weather; ZIKA; Zika Virus; atmospheric sciences; base; chikungunya; climate change; combat; cost; cost efficient; data standards; high risk; improved; indexing; innovation; machine learning model; meteorological data; metropolitan; next generation; novel; operation; pathogen; predictive modeling; predictive signature; preference; preservation; programs; prospective; rapid detection; remote sensing; therapeutically effective; tool; transmission process; vector; vector control; wasting; weather patterns

Project Summary Each year, approximately 400 million people are infected with an arboviral disease from the bite of an Aedes spp mosquito. Aedes spp. mosquitoes are a leading public health threat due to their high competency to vector multiple pathogens, their preference to bite humans, and their ability to adapt to new domestic environments. In the US, reintroduction and establishment of Aedes aegypti and Aedes albopictus mosquito populations has resulted in local epidemics of Zika, dengue and chikungunya in the past decade. Unfortunately, mosquito control programs in the US generally operate with limited budgets, forcing the majority of insecticide spraying to be conducted in reaction to population exposure instead of targeted prevention, which has also contributed to considerable growth of insecticide resistant populations, yielding a widening gap of infrastructure vulnerability. Our current proposal aims to leverage existing technologies from non-health disciplines to advance mosquito detection and abatement. We propose to validate the use of technology-driven mosquito traps that allow for high- throughput identification and counting of Aedes mosquitos at various life stages to inform decision making when selecting areas for insecticide spraying and abatement. Additionally, we propose to develop rigorous remote sensing workflows for identification of neighborhood-level Aedes abundance risk and rapid detection of individual Aedes mosquito breeding habitats on a household-level. This innovative proposal uses multi-year and real-world mosquito data from two different metropolitan areas to statistically adjust for variances in geographic ecologies, urban microclimates, seasonal climate patterns, and annual weather events. Our study will result in low-cost tools immediately ready for broad distribution and integration by vector control agencies nationally. The outcomes of our study have promise to directly impact vector control agency’s decision-making processes for mosquito trapping site selection, inform preventative abetment protocols, and shorten the time required for mosquito collection and identification. Further, integration of our proposed technology traps and informed site selection maps will increase overall collection volumes while preserving scarce resources for local vector control agencies. This proposal has the potential to create a paradigm shift in how we approach vector control globally, with a targeted intervention resulting in significant economic, environmental, and clinical benefits.

项目摘要 每年，大约有4亿人从伊蚊中感染了丁香病毒疾病 SPP蚊子。艾德斯属。由于蚊子的较高能力，蚊子是领先的公共卫生威胁 多种病原体，他们对咬人的偏爱以及适应新的家庭环境的能力。 美国的重新引入和建立伊德斯埃及和艾德斯白化蚊子的人口 在过去的十年中，导致了Zika，Dengue和Chikungunya的当地情节。不幸的是，蚊子控制 美国的计划通常以有限的预算运作，迫使大多数杀虫剂喷涂是 以对人口暴露而不是针对性预防的反应，这也有助于 绝缘种群的大量增长，从而扩大了基础设施脆弱性的差距。 我们目前的建议旨在利用非健康学科的现有技术来推进蚊子 检测和减排。我们建议验证技术驱动的蚊子陷阱的使用，该陷阱允许高度 在各个生活阶段的吞吐量识别和对艾德斯蚊子的计数，以告知决策 选择用于喷涂和减排的区域。此外，我们建议发展严格的遥控器 感知工作流以识别邻里级别的伊德斯的丰富风险和个人快速检测 艾德斯蚊子繁殖栖息地在家庭层面上。该创新的建议使用多年和现实世界 从两个不同大都市地区的蚊子数据对地理生态学方差进行统计调整， 城市微气候，季节性气候模式和年度天气事件。我们的研究将导致低成本 立即准备在全国范围内进行媒介控制机构进行广泛分配和集成。 我们研究的结果有望直接影响向量控制机构的决策过程 蚊子诱捕站点选择，告知预防性临床方案，并缩短 蚊子收集和识别。此外，我们提出的技术陷阱和知情网站的整合 选择地图将增加总体收集量，同时保留用于本地矢量控制的稀缺资源 机构。该提案有可能在我们全球接近向量控制的方式上建立范式转变， 有针对性的干预，从而带来了巨大的经济，环境和临床益处。