Department of Physics of Complex System, ELTE Eötvös Loránd University, Budapest, Hungary.
Centre d'Estudis Avançats de Blanes (CEAB-CSIC), 17300, Girona, Spain.
Sci Rep. 2021 Feb 25;11(1):4718. doi: 10.1038/s41598-021-83657-4.
Global monitoring of disease vectors is undoubtedly becoming an urgent need as the human population rises and becomes increasingly mobile, international commercial exchanges increase, and climate change expands the habitats of many vector species. Traditional surveillance of mosquitoes, vectors of many diseases, relies on catches, which requires regular manual inspection and reporting, and dedicated personnel, making large-scale monitoring difficult and expensive. New approaches are solving the problem of scalability by relying on smartphones and the Internet to enable novel community-based and digital observatories, where people can upload pictures of mosquitoes whenever they encounter them. An example is the Mosquito Alert citizen science system, which includes a dedicated mobile phone app through which geotagged images are collected. This system provides a viable option for monitoring the spread of various mosquito species across the globe, although it is partly limited by the quality of the citizen scientists' photos. To make the system useful for public health agencies, and to give feedback to the volunteering citizens, the submitted images are inspected and labeled by entomology experts. Although citizen-based data collection can greatly broaden disease-vector monitoring scales, manual inspection of each image is not an easily scalable option in the long run, and the system could be improved through automation. Based on Mosquito Alert's curated database of expert-validated mosquito photos, we trained a deep learning model to find tiger mosquitoes (Aedes albopictus), a species that is responsible for spreading chikungunya, dengue, and Zika among other diseases. The highly accurate 0.96 area under the receiver operating characteristic curve score promises not only a helpful pre-selector for the expert validation process but also an automated classifier giving quick feedback to the app participants, which may help to keep them motivated. In the paper, we also explored the possibilities of using the model to improve future data collection quality as a feedback loop.
全球病媒监测无疑成为当人口增长和流动增加、国际商业交流增多以及气候变化扩大许多病媒物种栖息地时的迫切需求。传统的蚊子监测（许多疾病的传播媒介）依赖于捕获，这需要定期进行人工检查和报告，并需要专门的人员，这使得大规模监测变得困难且昂贵。新方法通过依赖智能手机和互联网来解决可扩展性问题，从而实现新的基于社区和数字化的观测站，人们可以在遇到蚊子时随时上传蚊子的图片。一个例子是 Mosquito Alert 公民科学系统，该系统包括一个专用的手机应用程序，通过该应用程序可以收集地理标记的图像。该系统为监测各种蚊子在全球的传播提供了可行的选择，尽管它部分受到公民科学家照片质量的限制。为了使该系统对公共卫生机构有用，并为志愿公民提供反馈，提交的图像由昆虫学专家进行检查和标记。尽管基于公民的数据集收集可以大大拓宽疾病媒介监测的范围，但从长远来看，对每张图像进行人工检查并不是一个易于扩展的选择，并且可以通过自动化来改进该系统。基于 Mosquito Alert 经过专家验证的蚊子照片的精心策划数据库，我们训练了一个深度学习模型来发现白纹伊蚊（Aedes albopictus），这种蚊子负责传播基孔肯雅热、登革热和寨卡等疾病。高度准确的 0.96 接收器工作特征曲线下面积分数不仅有望成为专家验证过程的有用预选器，还可以成为自动分类器，为应用程序参与者提供快速反馈，这可能有助于保持他们的积极性。在本文中，我们还探讨了使用该模型作为反馈循环来提高未来数据收集质量的可能性。
