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Using big data to predict pertussis infections in Jinan city, China: a time series analysis

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Abstract

This study aims to use big data (climate data, internet query data and school calendar patterns (SCP)) to improve pertussis surveillance and prediction, and develop an early warning model for pertussis epidemics. We collected weekly pertussis notifications, SCP, climate and internet search query data (Baidu index (BI)) in Jinan, China between 2013 and 2017. Time series decomposition and temporal risk assessment were used for examining the epidemic features in pertussis infections. A seasonal autoregressive integrated moving average (SARIMA) model and regression tree model were developed to predict pertussis occurrence using identified predictors. Our study demonstrates clear seasonal patterns in pertussis epidemics, and pertussis activity was most significantly associated with BI at 2-week lag (rBI = 0.73, p < 0.05), temperature at 1-week lag (rtemp = 0.19, p < 0.05) and rainfall at 2-week lag (rrainfall = 0.27, p < 0.05). No obvious relationship between pertussis peaks and school attendance was found in the study. Pertussis cases were more likely to be temporally concentrated throughout the epidemics during the study period. SARIMA models with 2-week-lagged BI and 1-week-lagged temperature had better predictive performance (βsearch query = 0.06, p = 0.02; βtemp = 0.16, p = 0.03) with large correlation coefficients (r = 0.67, p < 0.01) and low root mean squared error (RMSE) value (r = 3.59). The regression tree model identified threshold values of potential predictors (search query, climate and SCP) for pertussis epidemics. Our results showed that internet query in conjunction with social and climatic data can predict pertussis epidemics, which is a foundation of using such data to develop early warning systems.

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Acknowledgements

We would like to express our gratitude to the Shandong Center for Disease Control and Prevention for providing pertussis surveillance data. Y.Z. was supported by the China Scholarship Council Postgraduate Scholarship and the Queensland University of Technology Higher Degree Research Tuition Fee Sponsorship. L. F. was supported by Shandong Medical and Health Science and Technology Development Programs (award no. 2015WS0271). W. H. was supported by an Australian Research Council Future Fellowship (award no. FT140101216). K. M. was supported by ARC Laureate Fellowship (award no. FL150100150) and an ARC Centre of Excellence in Mathematical and Statistical Frontiers (award no. CE140100049).

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Authors and Affiliations

  1. School of Public Health and Social Work; Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia

    Yuzhou Zhang, Hilary Bambrick, Shilu Tong & Wenbiao Hu

  2. School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia

    Kerrie Mengersen

  3. School of Public Health and Institute of Environment and Human Health, Anhui Medical University, Hefei, Anhui, China

    Shilu Tong

  4. Shanghai Children’s Medical Centre, Shanghai Jiao-Tong University, Shanghai, China

    Shilu Tong

  5. Shandong Provincial Centre of Disease Control and Prevention, Jinan, China

    Lei Feng, Li Zhang, Guifang Liu & Aiqiang Xu

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  1. Yuzhou Zhang
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Zhang, Y., Bambrick, H., Mengersen, K. et al. Using big data to predict pertussis infections in Jinan city, China: a time series analysis. Int J Biometeorol 64, 95–104 (2020). https://doi.org/10.1007/s00484-019-01796-w

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