Department of Zoology, University of Otago, P.O. Box 56, Dunedin, New Zealand.
Department of Anatomy, University of Otago, P.O. Box 56, Dunedin, New Zealand.
Parasitology. 2021 Sep;148(11):1313-1319. doi: 10.1017/S0031182021000962. Epub 2021 Jun 9.
Every internet search query made out of curiosity by anyone who observed something in nature, as well as every photo uploaded to the internet, constitutes a data point of potential use to scientists. Researchers have now begun to exploit the vast online data accumulated through passive crowdsourcing for studies in ecology and epidemiology. Here, we demonstrate the usefulness of iParasitology, i.e. the use of internet data for tests of parasitological hypotheses, using hairworms (phylum Nematomorpha) as examples. These large worms are easily noticeable by people in general, and thus likely to generate interest on the internet. First, we show that internet search queries (collated with Google Trends) and photos uploaded to the internet (specifically, to the iNaturalist platform) point to parts of North America with many sightings of hairworms by the public, but few to no records in the scientific literature. Second, we demonstrate that internet searches predict seasonal peaks in hairworm abundance that accurately match scientific data. Finally, photos uploaded to the internet by non-scientists can provide reliable data on the host taxa that hairworms most frequently parasitize, and also identify hosts that appear to have been neglected by scientific studies. Our findings suggest that for any parasite group likely to be noticeable by non-scientists, information accumulating through internet search activity, photo uploads, social media or any other format available online, represents a valuable source of data that can complement traditional scientific data sources in parasitology.
任何观察到自然界中某些事物的人出于好奇而进行的每一次互联网搜索查询，以及上传到互联网的每张照片，都构成了对科学家可能有用的数据点。研究人员现在已经开始利用通过被动众包积累的大量在线数据来进行生态学和流行病学研究。在这里，我们展示了 i 寄生虫学的实用性，即利用互联网数据来检验寄生虫学假设，以线虫（线虫门）为例。这些大型蠕虫通常很容易被人们注意到，因此很可能会在互联网上引起关注。首先，我们表明，互联网搜索查询（与 Google Trends 一起整理）和上传到互联网的照片（特别是上传到 iNaturalist 平台）指出了北美的许多地区，公众在这些地区观察到了很多线虫，但在科学文献中几乎没有记录。其次，我们证明，互联网搜索可以预测线虫丰度的季节性高峰，这些高峰与科学数据非常吻合。最后，非科学家上传到互联网的照片可以提供关于线虫最常寄生的宿主类群的可靠数据，还可以识别那些似乎被科学研究忽视的宿主。我们的研究结果表明，对于任何可能被非科学家注意到的寄生虫群体，通过互联网搜索活动、照片上传、社交媒体或任何其他在线可用格式积累的信息，代表了寄生虫学中一种有价值的数据来源，可以补充传统的科学数据源。
