Laboratório de Parasitologia Médica e Biologia de Vetores, Área de Patologia, Faculdade de Medicina, Universidade de Brasília, Brasília, Brazil.
Faculdade de Medicina, Núcleo de Medicina Tropical, Universidade de Brasília, Brasília, Brazil.
PLoS One. 2021 Apr 22;16(4):e0248628. doi: 10.1371/journal.pone.0248628. eCollection 2021.
Correct identification of triatomine bugs is crucial for Chagas disease surveillance, yet available taxonomic keys are outdated, incomplete, or both. Here we present TriatoDex, an Android app-based pictorial, annotated, polytomous key to the Triatominae. TriatoDex was developed using Android Studio and tested by 27 Brazilian users. Each user received a box with pinned, number-labeled, adult triatomines (33 species in total) and was asked to identify each bug to the species level. We used generalized linear mixed models (with user- and species-ID random effects) and information-theoretic model evaluation/averaging to investigate TriatoDex performance. TriatoDex encompasses 79 questions and 554 images of the 150 triatomine-bug species described worldwide up to 2017. TriatoDex-based identification was correct in 78.9% of 824 tasks. TriatoDex performed better in the hands of trained taxonomists (93.3% vs. 72.7% correct identifications; model-averaged, adjusted odds ratio 5.96, 95% confidence interval [CI] 3.09-11.48). In contrast, user age, gender, primary job (including academic research/teaching or disease surveillance), workplace (including universities, a reference laboratory for triatomine-bug taxonomy, or disease-surveillance units), and basic training (from high school to biology) all had negligible effects on TriatoDex performance. Our analyses also suggest that, as TriatoDex results accrue to cover more taxa, they may help pinpoint triatomine-bug species that are consistently harder (than average) to identify. In a pilot comparison with a standard, printed key (370 tasks by seven users), TriatoDex performed similarly (84.5% correct assignments, CI 68.9-94.0%), but identification was 32.8% (CI 24.7-40.1%) faster on average-for a mean absolute saving of ~2.3 minutes per bug-identification task. TriatoDex holds much promise as a handy, flexible, and reliable tool for triatomine-bug identification; an updated iOS/Android version is under development. We expect that, with continuous refinement derived from evolving knowledge and user feedback, TriatoDex will substantially help strengthen both entomological surveillance and research on Chagas disease vectors.
正确识别锥蝽是进行恰加斯病监测的关键，但现有的分类学关键信息已经过时、不完整，或两者兼而有之。为此，我们开发了一款基于安卓系统的应用程序 TriatoDex，这是一种带注释的、多叉的锥蝽分类检索图。TriatoDex 是使用 Android Studio 开发的，并由 27 名巴西用户进行了测试。每位用户都收到一个盒子，里面装有别针固定的、编号的成虫锥蝽（共 33 种），并被要求识别出每只虫子的物种水平。我们使用广义线性混合模型（用户和物种 ID 为随机效应）和信息理论模型评估/平均来研究 TriatoDex 的性能。TriatoDex 包含 79 个问题和 554 张全世界截至 2017 年描述的 150 种锥蝽的图片。在 824 项任务中，基于 TriatoDex 的识别准确率为 78.9%。在受过训练的分类学家手中，TriatoDex 的表现更好（正确识别的比例为 93.3%，而 72.7%；模型平均调整后的优势比为 5.96，95%置信区间[CI]为 3.09-11.48）。相比之下，用户的年龄、性别、主要工作（包括学术研究/教学或疾病监测）、工作场所（包括大学、锥蝽分类学的参考实验室或疾病监测单位）和基本培训（从高中到生物学）都对 TriatoDex 的性能几乎没有影响。我们的分析还表明，随着 TriatoDex 结果的积累，涵盖更多的分类群，它们可能有助于确定那些始终更难（平均而言）识别的锥蝽物种。在与标准印刷关键信息（7 名用户的 370 项任务）的初步比较中，TriatoDex 的表现相似（正确识别的比例为 84.5%，CI 68.9-94.0%），但平均识别速度快 32.8%（CI 24.7-40.1%）-平均每次识别任务可节省约 2.3 分钟。TriatoDex 有望成为一种方便、灵活、可靠的锥蝽识别工具；正在开发一个更新的 iOS/Android 版本。我们预计，随着不断完善和用户反馈，TriatoDex 将极大地帮助加强锥蝽病媒介的昆虫学监测和研究。
