Abstract
The use of internet-based query data offers a novel approach to improve disease surveillance and provides timely disease information. This paper systematically reviewed the literature on infectious disease predictions using internet-based query data and climate factors, discussed the current research progress and challenges, and provided some recommendations for future studies. We searched the relevant articles in the PubMed, Scopus, and Web of Science databases between January 2000 and December 2019. We initially included studies that used internet-based query data to predict infectious disease epidemics, then we further filtered and appraised the studies that used both internet-based query data and climate factors. In total, 129 relevant papers were included in the review. The results showed that most studies used a simple descriptive approach (n=80; 62%) to detect epidemics of influenza (including influenza-like illness (ILI)) (n=88; 68%) and dengue (n=9; 7%). Most studies (n=61; 47%) purely used internet search metrics to predict the epidemics of infectious diseases, while only 3 out of the 129 papers included both climate variables and internet-based query data. Our research shows that including internet-based query data and climate variables could better predict climate-sensitive infectious disease epidemics; however, this method has not been widely used to date. Moreover, previous studies did not sufficiently consider the spatiotemporal uncertainty of infectious diseases. Our review suggests that further research should use both internet-based query and climate data to develop predictive models for climate-sensitive infectious diseases based on spatiotemporal models.
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Funding
Y.Z. was supported by the China Scholarship Council Postgraduate Scholarship and the Queensland University of Technology Higher Degree Research Tuition Fee Sponsorship. W. H. was supported by an Australian Research Council (ARC) Future Fellowship (award number FT140101216). K. M. was supported by an ARC Laureate Fellowship (award number FL150100150) and an ARC Centre of Excellence in Mathematical and Statistical Frontiers (award number CE140100049).
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W. H. designed this study. Y. Z. collected and analysed the data and drafted this manuscript with W.H. assistance. W. H., H. B., K. M., and S. T. interpreted the results and revised the manuscript.
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Zhang, Y., Bambrick, H., Mengersen, K. et al. Using internet-based query and climate data to predict climate-sensitive infectious disease risks: a systematic review of epidemiological evidence. Int J Biometeorol 65, 2203–2214 (2021). https://doi.org/10.1007/s00484-021-02155-4
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DOI: https://doi.org/10.1007/s00484-021-02155-4