{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T12:39:06Z","timestamp":1784119146417,"version":"3.55.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643683881","type":"print"},{"value":"9781643683898","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T00:00:00Z","timestamp":1684368000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,18]]},"abstract":"<jats:p>Background: Emerging Infectious Diseases (EID) are a significant threat to population health globally. We aimed to examine the relationship between internet search engine queries and social media data on COVID-19 and determine if they can predict COVID-19 cases in Canada. Methods: We analyzed Google Trends (GT) and Twitter data from 1\/1\/2020 to 3\/31\/2020 in Canada and used various signal-processing techniques to remove noise from the data. Data on COVID-19 cases was obtained from the COVID-19 Canada Open Data Working Group. We conducted time-lagged cross-correlation analyses and developed the long short-term memory model for forecasting daily COVID-19 cases. Results: Among symptom keywords, \u201ccough,\u201d \u201crunny nose,\u201d and \u201canosmia\u201d were strong signals with high cross-correlation coefficients &gt;0.8 ( rCough = 0.825, t - 9; rRunnyNose = 0.816, t - 11; rAnosmia = 0.812, t - 3 ), showing that searching for \u201ccough,\u201d \u201crunny nose,\u201d and \u201canosmia\u201d on GT correlated with the incidence of COVID-19 and peaked 9, 11, and 3 days earlier than the incidence peak, respectively. For symptoms- and COVID-related Tweet counts, the cross-correlations of Tweet signals and daily cases were rTweetSymptoms = 0.868, t - 11 and tTweetCOVID = 0.840, t - 10, respectively. The LSTM forecasting model achieved the best performance (MSE = 124.78, R2 = 0.88, adjusted R2 = 0.87) using GT signals with cross-correlation coefficients &gt;0.75. Combining GT and Tweet signals did not improve the model performance. Conclusion: Internet search engine queries and social media data can be used as early warning signals for creating a real-time surveillance system for COVID-19 forecasting, but challenges remain in modelling.<\/jats:p>","DOI":"10.3233\/shti230290","type":"book-chapter","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T08:48:03Z","timestamp":1684486083000},"source":"Crossref","is-referenced-by-count":4,"title":["Digital Disease Surveillance for Emerging Infectious Diseases: An Early Warning System Using the Internet and Social Media Data for COVID-19 Forecasting in Canada"],"prefix":"10.3233","author":[{"given":"Yang","family":"Yang","sequence":"first","affiliation":[{"name":"School of Public Health Sciences, University of Waterloo, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu-Feng","family":"Tsao","sequence":"additional","affiliation":[{"name":"School of Public Health Sciences, University of Waterloo, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad A.","family":"Basri","sequence":"additional","affiliation":[{"name":"Systems Design Engineering, University of Waterloo, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helen H.","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Public Health Sciences, University of Waterloo, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahid A.","family":"Butt","sequence":"additional","affiliation":[{"name":"School of Public Health Sciences, University of Waterloo, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Caring is Sharing \u2013 Exploiting the Value in Data for Health and Innovation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI230290","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T15:02:08Z","timestamp":1685545328000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI230290"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,18]]},"ISBN":["9781643683881","9781643683898"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti230290","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,18]]}}}