Biodiversity Institute, University of Kansas, Lawrence, KS 66045, USA.
Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS 66045, USA.
J Med Entomol. 2022 Jan 12;59(1):355-362. doi: 10.1093/jme/tjab161.
Mosquito-borne diseases account for human morbidity and mortality worldwide, caused by the parasites (e.g., malaria) or viruses (e.g., dengue, Zika) transmitted through bites of infected female mosquitoes. Globally, billions of people are at risk of infection, imposing significant economic and public health burdens. As such, efficient methods to monitor mosquito populations and prevent the spread of these diseases are at a premium. One proposed technique is to apply acoustic monitoring to the challenge of identifying wingbeats of individual mosquitoes. Although researchers have successfully used wingbeats to survey mosquito populations, implementation of these techniques in areas most affected by mosquito-borne diseases remains challenging. Here, methods utilizing easily accessible equipment and encouraging community-scientist participation are more likely to provide sufficient monitoring. We present a practical, community-science-based method of monitoring mosquito populations using smartphones. We applied deep-learning algorithms (TensorFlow Inception v3) to spectrogram images generated from smartphone recordings associated with six mosquito species to develop a multiclass mosquito identification system, and flag potential invasive vectors not present in our sound reference library. Though TensorFlow did not flag potential invasive species with high accuracy, it was able to identify species present in the reference library at an 85% correct identification rate, an identification rate markedly higher than similar studies employing expensive recording devices. Given that we used smartphone recordings with limited sample sizes, these results are promising. With further optimization, we propose this novel technique as a way to accurately and efficiently monitor mosquito populations in areas where doing so is most critical.
蚊媒疾病在全球范围内造成了人类的发病率和死亡率，这些疾病是由寄生虫（如疟疾）或病毒（如登革热、寨卡热）通过感染的雌性蚊子叮咬传播的。在全球范围内，数十亿人面临感染风险，这给经济和公共卫生带来了巨大负担。因此，高效监测蚊媒种群并防止这些疾病传播的方法非常重要。一种被提议的技术是将声学监测应用于识别个体蚊子翅膀拍打声的挑战中。尽管研究人员已经成功地使用翅膀拍打声来调查蚊子种群，但在受蚊媒疾病影响最严重的地区实施这些技术仍然具有挑战性。在这里，利用易于获取的设备和鼓励社区科学家参与的方法更有可能提供充分的监测。我们提出了一种使用智能手机监测蚊子种群的实用社区科学方法。我们将深度学习算法（TensorFlow Inception v3）应用于智能手机记录的声谱图像，以开发一种多类蚊子识别系统，并标记不在我们声音参考库中的潜在入侵媒介。虽然 TensorFlow 没有以高精度标记潜在的入侵物种，但它能够以 85%的正确识别率识别参考库中存在的物种，这一识别率明显高于使用昂贵记录设备的类似研究。鉴于我们使用智能手机记录的样本数量有限，这些结果是有希望的。通过进一步优化，我们提出这项新技术作为一种在最关键地区准确高效监测蚊子种群的方法。
