Internet-based surveillance to track trends in seasonal allergies across the United States.

IF 2.2 Q2 MULTIDISCIPLINARY SCIENCES PNAS nexus Pub Date : 2024-10-29 eCollection Date: 2024-10-01 DOI:10.1093/pnasnexus/pgae430
Elias Stallard-Olivera, Noah Fierer
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Abstract

Over a quarter of adults in the United States suffer from seasonal allergies, yet the broader spatiotemporal patterns in seasonal allergy trends remain poorly resolved. This knowledge gap persists due to difficulties in quantifying allergies as symptoms are seldom severe enough to warrant hospital visits. We show that we can use machine learning to extract relevant data from Twitter posts and Google searches to examine population-level trends in seasonal allergies at high spatial and temporal resolution, validating the approach against hospital record data obtained from selected counties in California, United States. After showing that internet-derived data can be used as a proxy for aeroallergen exposures, we demonstrate the utility of our approach by mapping seasonal allergy-related online activity across the 144 most populous US counties at daily time steps over an 8-year period, highlighting the spatial and temporal dynamics in allergy trends across the continental United States.

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通过互联网监控,跟踪全美季节性过敏症的趋势。
在美国,超过四分之一的成年人患有季节性过敏症,但季节性过敏趋势的更广泛时空模式仍未得到很好的解决。由于过敏症状很少严重到需要到医院就诊,因此难以量化过敏症状,导致这一知识空白持续存在。我们的研究表明,我们可以利用机器学习从推特帖子和谷歌搜索中提取相关数据,以较高的空间和时间分辨率来研究人群层面的季节性过敏趋势,并根据从美国加利福尼亚州部分县获得的医院记录数据对该方法进行验证。在证明互联网数据可作为接触空气过敏原的替代数据后,我们通过绘制美国人口最多的 144 个县的季节性过敏相关在线活动图,以每天的时间步长展示了我们的方法在 8 年间的实用性,突出了美国大陆过敏趋势的空间和时间动态。
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