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

项目摘要 每年大约有4亿人被伊蚊叮咬而感染虫媒病毒 蚊种伊蚊属蚊子是一种主要的公共卫生威胁，这是由于它们的高能力， 多种病原体，它们喜欢叮咬人类，以及它们适应新的家庭环境的能力。在 在美国，埃及伊蚊和白纹伊蚊种群的重新引入和建立， 在过去十年中，寨卡病毒、登革热和基孔肯雅热在当地流行。不幸的是， 美国的项目通常在有限的预算下运作，迫使大多数杀虫剂喷洒被 针对人群接触而不是有针对性的预防，这也有助于 对杀虫剂有抗药性的人口大量增加，导致基础设施脆弱性差距不断扩大。 我们目前的建议旨在利用非卫生学科的现有技术来促进蚊子 检测和消除。我们建议验证采用科技诱蚊器的可行性， 在不同生命阶段的伊蚊的吞吐量识别和计数， 选择喷洒和消除杀虫剂的地区。此外，我们建议开发严格的远程 用于识别邻居级伊蚊丰度风险和快速检测个体的传感工作流程 伊蚊繁殖栖息地在户级。这一创新提案使用多年和现实世界 来自两个不同大都市地区的蚊子数据，以根据地理生态的差异进行统计调整， 城市小气候、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 立即准备好各种工具，供全国病媒控制机构广泛分发和整合。的 我们的研究结果有望直接影响病媒控制机构的决策过程， 蚊子诱捕地点的选择，告知预防教唆协议，并缩短所需的时间， 蚊子的收集和鉴定。此外，我们建议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，同时为当地病媒控制保留稀缺资源 社.这一提议有可能在我们如何在全球范围内进行病媒控制方面产生范式转变， 有针对性的干预导致显著的经济、环境和临床效益。

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.