疟疾等蚊媒疾病,登革热,Zika,基孔肯雅病在全球范围内造成显著的发病率和死亡率,每年导致超过60万人死于疟疾,约3.6万人死于登革热,每年有数百万人被感染,造成了巨大的经济损失。现行的灭蚊措施,如长效杀虫网(LLINs)和室内滞留喷洒(IRS),有助于减少感染。然而,蚊媒疾病仍然是最致命的疾病之一,迫使我们改进现有的控制方法,同时寻找替代方法。先进的监测技术,包括遥感,和地理信息系统(GIS)大大提高了蚊子控制措施的效率和有效性。蚊子的行为特征,比如运动,血液喂养,和生育率是疾病传播和流行病学的关键决定因素。技术进步,比如高分辨率相机,红外成像,和人工智能(AI)驱动的对象检测模型,包括开创性的卷积神经网络,提供了有效和精确的选择来监测各种蚊子的行为,包括运动,产卵,生育力,寻找主人。然而,它们通常不用于基于蚊子的研究。这篇综述强调了行为监测工具的新颖和重大进步,主要来自过去十年,由于尖端的视频监控技术和人工智能。这些改进可以提供更高的准确性,效率,以及快速处理大量数据的能力,实现长时间和大样本量的详细行为分析,与传统的手工方法不同,容易出现人为错误和劳动密集型。行为测定技术的使用可以支持或取代现有的监测技术,并通过提供有关蚊子活动模式和对干预措施的反应的更准确和实时的数据,直接有助于改进控制措施。这种增强的理解可以帮助确定行为变化在改善流行病学模型中的作用,使它们更加精确和动态。因此,蚊子管理策略可以变得更具适应性和反应能力,导致更有效和有针对性的干预措施。最终,这将减少疾病传播并显著改善公共卫生结果.
Mosquito-borne diseases such as malaria, dengue, Zika, and chikungunya cause significant morbidity and mortality globally, resulting in over 600,000 deaths from malaria and around 36,000 deaths from dengue each year, with millions of people infected annually, leading to substantial economic losses. The existing mosquito control measures, such as long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS), helped to reduce the infections. However, mosquito-borne diseases are still among the deadliest diseases, forcing us to improve the existing control methods and look for alternative methods simultaneously. Advanced monitoring techniques, including remote sensing, and geographic information systems (GIS) have significantly enhanced the efficiency and effectiveness of mosquito control measures. Mosquitoes\' behavioural traits, such as locomotion, blood-feeding, and fertility are the key determinants of disease transmission and epidemiology. Technological advancements, such as high-resolution cameras, infrared imaging, and artificial intelligence (AI) driven object detection models, including groundbreaking convolutional neural networks, have provided efficient and precise options to monitor various mosquito behaviours, including locomotion,
oviposition, fertility, and host-seeking. However, they are not commonly employed in mosquito-based research. This review highlights the novel and significant advancements in behaviour-monitoring tools, mostly from the last decade, due to cutting-edge video monitoring technology and artificial intelligence. These advancements can offer enhanced accuracy, efficiency, and the ability to quickly process large volumes of data, enabling detailed behavioural analysis over extended periods and large sample sizes, unlike traditional manual methods prone to human error and labour-intensive. The use of behaviour-assaying techniques can support or replace existing monitoring techniques and directly contribute to improving control measures by providing more accurate and real-time data on mosquito activity patterns and responses to interventions. This enhanced understanding can help establish the role of behavioural changes in improving epidemiological models, making them more precise and dynamic. As a result, mosquito management strategies can become more adaptive and responsive, leading to more effective and targeted interventions. Ultimately, this will reduce disease transmission and significantly improve public health outcomes.