Digital Disease Surveillance for Emerging Infectious Diseases: An Early Warning System Using the Internet and Social Media Data for COVID-19 Forecasting in Canada.
Stud Health Technol Inform
; 302: 861-865, 2023 May 18.
Article
in English
| MEDLINE | ID: covidwho-2327217
ABSTRACT
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, "cough," "runny nose," and "anosmia" were strong signals with high cross-correlation coefficients >0.8 ( rCough = 0.825, t - 9; rRunnyNose = 0.816, t - 11; rAnosmia = 0.812, t - 3 ), showing that searching for "cough," "runny nose," and "anosmia" 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 >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.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Communicable Diseases, Emerging
/
Social Media
/
COVID-19
Type of study:
Diagnostic study
/
Observational study
/
Prognostic study
/
Randomized controlled trials
Topics:
Long Covid
Limits:
Humans
Language:
English
Journal:
Stud Health Technol Inform
Journal subject:
Medical Informatics
/
Health Services Research
Year:
2023
Document Type:
Article
Affiliation country:
Shti230290
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