Google unveils a glimpse of allergic rhinitis in the real world

Kang, Min; Song, Woo Jung; Choi, Sue In; Kim, H.; Ha, Heggtveit; Kim, Su Hwan; Cho, Sang Heon; Min, Kyung Up; Yoon, Sung-Soo; Chang, Yoon-Seok

doi:10.1111/all.12528

Cited by 37 publications

(32 citation statements)

References 11 publications

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“…Recent studies have suggested the utility of GT for assessing the seasonality of allergic diseases [12-17] and for detecting influenza epidemics [18]. GT data may closely reflect the real-world epidemiology of allergic rhinitis (AR) and could potentially be used as a monitoring tool for AR [19]. However, besides the translation of the terms in different languages, cultural differences and different methods of accessing knowledge exist between countries and the same terms are not used similarly in all countries [20].…”

Section: Introductionmentioning

confidence: 99%

Differences in Reporting the Ragweed Pollen Season Using Google Trends across 15 Countries

Bousquet

Agache

Berger

et al. 2018

Int Arch Allergy Immunol

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Background: Google Trends (GT) searches trends of specific queries in Google, which potentially reflect the real-life epidemiology of allergic rhinitis. We compared GT terms related to ragweed pollen allergy in American and European Union countries with a known ragweed pollen season. Our aim was to assess seasonality and the terms needed to perform the GT searches and to compare these during the spring and summer pollen seasons. Methods: We examined GT queries from January 1, 2011, to January 4, 2017. We included 15 countries with a known ragweed pollen season and used the standard 5-year GT graphs. We used the GT translation for all countries and the untranslated native terms for each country. Results: The results of “pollen,” “ragweed,” and “allergy” searches differed between countries, but “ragweed” was clearly identified in 12 of the 15 countries. There was considerable heterogeneity of findings when the GT translation was used. For Croatia, Hungary, Romania, Serbia, and Slovenia, the GT translation was inappropriate. The country patterns of “pollen,” “hay fever,” and “allergy” differed in 8 of the 11 countries with identified “ragweed” queries during the spring and the summer, indicating that the perception of tree and grass pollen allergy differs from that of ragweed pollen. Conclusions: To investigate ragweed pollen allergy using GT, the term “ragweed” as a plant is required and the translation of “ragweed” in the native language needed.

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Section: Introductionmentioning

confidence: 99%

Differences in Reporting the Ragweed Pollen Season Using Google Trends across 15 Countries

Bousquet

Agache

Berger

et al. 2018

Int Arch Allergy Immunol

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“…27 Previous studies have also found that allergic rhinitis ( which is related to allergic conjunctivitis), assessed through Google Trends, peaked in the spring, similar to our findings for allergic conjunctivitis. 28 This finding suggests some overlap of allergic conjunctivitis and allergic rhinitis in social media data and clinical diagnoses. 5 We also found differences in allergic vs nonallergic conjunctivitis where EMR data on nonallergic conjunctivitis correlated strongly with Google USA search results for pink eye but not significantly with UCSF diagnoses of allergic conjunctivitis or Google USA search results for eye allergy .…”

Section: Discussionmentioning

confidence: 93%

“…Other studies have investigated allergic rhinitis, which has an ocular component, and have suggested a strong correlation of allergic rhinitis with internet-based Google searches, web traffic logs, and other related terms such as medications . 28 We compare the seasonality of conjunctivitis in online searches in the United States with the seasonality observed in an EMR system from a tertiary care center. We compare that correlation of conjunctivitis-related seasonality with that of other eye-related and of non–eye-related online searches and EMR data as well as with Tweets about pink eye.…”

mentioning

confidence: 99%

Surveillance Tools Emerging From Search Engines and Social Media Data for Determining Eye Disease Patterns

et al. 2016

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IMPORTANCE Internet-based search engine and social media data may provide a novel complementary source for better understanding the epidemiologic factors of infectious eye diseases, which could better inform eye health care and disease prevention.OBJECTIVE To assess whether data from internet-based social media and search engines are associated with objective clinic-based diagnoses of conjunctivitis.DESIGN, SETTING, AND PARTICIPANTS Data from encounters of 4143 patients diagnosed with conjunctivitis from June 3, 2012, to April 26, 2014, at the University of California San Francisco (UCSF) Medical Center, were analyzed using Spearman rank correlation of each weekly observation to compare demographics and seasonality of nonallergic conjunctivitis with allergic conjunctivitis. Data for patient encounters with diagnoses for glaucoma and influenza were also obtained for the same period and compared with conjunctivitis. Temporal patterns of Twitter and Google web search data, geolocated to the United States and associated with these clinical diagnoses, were compared with the clinical encounters. The a priori hypothesis was that weekly internet-based searches and social media posts about conjunctivitis may reflect the true weekly clinical occurrence of conjunctivitis.MAIN OUTCOMES AND MEASURES Weekly total clinical diagnoses at UCSF of nonallergic conjunctivitis, allergic conjunctivitis, glaucoma, and influenza were compared using Spearman rank correlation with equivalent weekly data on Tweets related to disease or disease-related keyword searches obtained from Google Trends.RESULTS Seasonality of clinical diagnoses of nonallergic conjunctivitis among the 4143 patients (2364 females [57.1%] and 1776 males [42.9%]) with 5816 conjunctivitis encounters at UCSF correlated strongly with results of Google searches in the United States for the term pink eye (ρ, 0.68 [95% CI, 0.52 to 0.78]; P < .001) and correlated moderately with Twitter results about pink eye (ρ, 0.38 [95% CI, 0.16 to 0.56]; P < .001) and with clinical diagnosis of influenza (ρ, 0.33 [95% CI, 0.12 to 0.49]; P < .001), but did not significantly correlate with seasonality of clinical diagnoses of allergic conjunctivitis diagnosis at UCSF (ρ, 0.21 [95% CI, −0.02 to 0.42]; P = .06) or with results of Google searches in the United States for the term eye allergy (ρ, 0.13 [95% CI, −0.06 to 0.32]; P = .19). Seasonality of clinical diagnoses of allergic conjunctivitis at UCSF correlated strongly with results of Google searches in the United States for the term eye allergy (ρ, 0.44 [95% CI, 0.24 to 0.60]; P < .001) and eye drops (ρ, 0.47 [95% CI, 0.27 to 0.62]; P < .001). CONCLUSIONS AND RELEVANCEInternet-based search engine and social media data may reflect the occurrence of clinically diagnosed conjunctivitis, suggesting that these data sources can be leveraged to better understand the epidemiologic factors of conjunctivitis.

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“…For example, we exclude metrics based on seasonality [11,130], pollen counts [58,165], over-the-counter drug sales volume [82,98], and emergency department visits [52]. …”

Section: Related Workmentioning

confidence: 99%

Measuring Global Disease with Wikipedia

Priedhorsky

Osthus

Daughton

et al. 2017

Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing

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Effective disease monitoring provides a foundation for effective public health systems. This has historically been accomplished with patient contact and bureaucratic aggregation, which tends to be slow and expensive. Recent internet-based approaches promise to be real-time and cheap, with few parameters. However, the question of when and how these approaches work remains open. We addressed this question using Wikipedia access logs and category links. Our experiments, replicable and extensible using our open source code and data, test the effect of semantic article filtering, amount of training data, forecast horizon, and model staleness by comparing across 6 diseases and 4 countries using thousands of individual models. We found that our minimal-configuration, language-agnostic article selection process based on semantic relatedness is effective for improving predictions, and that our approach is relatively insensitive to the amount and age of training data. We also found, in contrast to prior work, very little forecasting value, and we argue that this is consistent with theoretical considerations about the nature of forecasting. These mixed results lead us to propose that the currently observational field of internet-based disease surveillance must pivot to include theoretical models of information flow as well as controlled experiments based on simulations of disease.

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Google unveils a glimpse of allergic rhinitis in the real world

Cited by 37 publications

References 11 publications

Differences in Reporting the Ragweed Pollen Season Using Google Trends across 15 Countries

Differences in Reporting the Ragweed Pollen Season Using Google Trends across 15 Countries

Surveillance Tools Emerging From Search Engines and Social Media Data for Determining Eye Disease Patterns

Measuring Global Disease with Wikipedia

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