Yuzhou Zhang1, Hilary Bambrick2, Kerrie Mengersen3, Shilu Tong4, Wenbiao Hu5. 1. School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia. Electronic address: yuzhou.zhang@hdr.qut.edu.au. 2. School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia. Electronic address: h.bambrick@qut.edu.au. 3. Science and Engineering Faculty, Mathematical and Statistical Science, Queensland University of Technology, Brisbane, Queensland, Australia. Electronic address: k.mengersen@qut.edu.au. 4. School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia; School of Public Health and Institute of Environment and Human Health, Anhui Medical University, Hefei, Anhui, China; Shanghai Children's Medical Centre, Shanghai Jiao-Tong University, Shanghai, China. Electronic address: s.tong@qut.edu.au. 5. School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia. Electronic address: w2.hu@qut.edu.au.
Abstract
BACKGROUND: The discovery of the dynamics of seasonal and non-seasonal influenza outbreaks remains a great challenge. Previous internet-based surveillance studies built purely on internet or climate data do have potential error. METHODS: We collected influenza notifications, temperature and Google Trends (GT) data between January 1st, 2011 and December 31st, 2016. We performed time-series cross correlation analysis and temporal risk analysis to discover the characteristics of influenza epidemics in the period. Then, the seasonal autoregressive integrated moving average (SARIMA) model and regression tree model were developed to track influenza epidemics using GT and climate data. RESULTS: Influenza infection was significantly corrected with GT at lag of 1-7 weeks in Brisbane and Gold Coast, and temperature at lag of 1-10 weeks for the two study settings. SARIMA models with GT and temperature data had better predictive performance. We identified autoregression (AR) for influenza was the most important determinant for influenza occurrence in both Brisbane and Gold Coast. CONCLUSIONS: Our results suggested internet search metrics in conjunction with temperature can be used to predict influenza outbreaks, which can be considered as a pre-requisite for constructing early warning systems using search and temperature data.
BACKGROUND: The discovery of the dynamics of seasonal and non-seasonal influenza outbreaks remains a great challenge. Previous internet-based surveillance studies built purely on internet or climate data do have potential error. METHODS: We collected influenza notifications, temperature and Google Trends (GT) data between January 1st, 2011 and December 31st, 2016. We performed time-series cross correlation analysis and temporal risk analysis to discover the characteristics of influenza epidemics in the period. Then, the seasonal autoregressive integrated moving average (SARIMA) model and regression tree model were developed to track influenza epidemics using GT and climate data. RESULTS: Influenza infection was significantly corrected with GT at lag of 1-7 weeks in Brisbane and Gold Coast, and temperature at lag of 1-10 weeks for the two study settings. SARIMA models with GT and temperature data had better predictive performance. We identified autoregression (AR) for influenza was the most important determinant for influenza occurrence in both Brisbane and Gold Coast. CONCLUSIONS: Our results suggested internet search metrics in conjunction with temperature can be used to predict influenza outbreaks, which can be considered as a pre-requisite for constructing early warning systems using search and temperature data.
Authors: Daniel Alejandro Gónzalez-Bandala; Juan Carlos Cuevas-Tello; Daniel E Noyola; Andreu Comas-García; Christian A García-Sepúlveda Journal: Int J Environ Res Public Health Date: 2020-06-24 Impact factor: 3.390
Authors: Joanna Kedra; Timothy Radstake; Aridaman Pandit; Xenofon Baraliakos; Francis Berenbaum; Axel Finckh; Bruno Fautrel; Tanja A Stamm; David Gomez-Cabrero; Christian Pristipino; Remy Choquet; Hervé Servy; Simon Stones; Gerd Burmester; Laure Gossec Journal: RMD Open Date: 2019-07-18
Authors: Zhijuan Song; Xiaocan Jia; Junzhe Bao; Yongli Yang; Huili Zhu; Xuezhong Shi Journal: Int J Environ Res Public Health Date: 2021-07-02 Impact factor: 3.390
Authors: Faris F Brkic; Gerold Besser; Martin Schally; Elisabeth M Schmid; Thomas Parzefall; Dominik Riss; David T Liu Journal: J Med Internet Res Date: 2021-06-24 Impact factor: 5.428
Authors: Albertus J Smit; Jennifer M Fitchett; Francois A Engelbrecht; Robert J Scholes; Godfrey Dzhivhuho; Neville A Sweijd Journal: Int J Environ Res Public Health Date: 2020-08-05 Impact factor: 3.390