tetano
Editor, Senior Moderator
J Clin Med
. 2024 Mar 27;13(7):1946.
doi: 10.3390/jcm13071946. Infodemiology of Influenza-like Illness: Utilizing Google Trends' Big Data for Epidemic Surveillance
Dong-Her Shih[SUP] 1 [/SUP], Yi-Huei Wu[SUP] 1 [/SUP], Ting-Wei Wu[SUP] 1 [/SUP], Shu-Chi Chang[SUP] 1 [/SUP], Ming-Hung Shih[SUP] 2 [/SUP]
Affiliations
Background: Influenza-like illness (ILI) encompasses symptoms similar to influenza, affecting population health. Surveillance, including Google Trends (GT), offers insights into epidemic patterns. Methods: This study used multiple regression models to analyze the correlation between ILI incidents, GT keyword searches, and climate variables during influenza outbreaks. It compared the predictive capabilities of time-series and deep learning models against ILI emergency incidents. Results: The GT searches for "fever" and "cough" were significantly associated with ILI cases (p < 0.05). Temperature had a more substantial impact on ILI incidence than humidity. Among the tested models, ARIMA provided the best predictive power. Conclusions: GT and climate data can forecast ILI trends, aiding governmental decision making. Temperature is a crucial predictor, and ARIMA models excel in forecasting ILI incidences.
Keywords: ARIMA; Google Trends; big data; deep learning; influenza-like illness; infodemiology.
. 2024 Mar 27;13(7):1946.
doi: 10.3390/jcm13071946. Infodemiology of Influenza-like Illness: Utilizing Google Trends' Big Data for Epidemic Surveillance
Dong-Her Shih[SUP] 1 [/SUP], Yi-Huei Wu[SUP] 1 [/SUP], Ting-Wei Wu[SUP] 1 [/SUP], Shu-Chi Chang[SUP] 1 [/SUP], Ming-Hung Shih[SUP] 2 [/SUP]
Affiliations
- PMID: 38610711
- PMCID: PMC11012909
- DOI: 10.3390/jcm13071946
Background: Influenza-like illness (ILI) encompasses symptoms similar to influenza, affecting population health. Surveillance, including Google Trends (GT), offers insights into epidemic patterns. Methods: This study used multiple regression models to analyze the correlation between ILI incidents, GT keyword searches, and climate variables during influenza outbreaks. It compared the predictive capabilities of time-series and deep learning models against ILI emergency incidents. Results: The GT searches for "fever" and "cough" were significantly associated with ILI cases (p < 0.05). Temperature had a more substantial impact on ILI incidence than humidity. Among the tested models, ARIMA provided the best predictive power. Conclusions: GT and climate data can forecast ILI trends, aiding governmental decision making. Temperature is a crucial predictor, and ARIMA models excel in forecasting ILI incidences.
Keywords: ARIMA; Google Trends; big data; deep learning; influenza-like illness; infodemiology.