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The Spatiotemporal Prediction Model of Opioids Spread Trend Based on Grey Correlation

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Complex, Intelligent, and Software Intensive Systems (CISIS 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 993))

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Abstract

With the rapid increasing of opioids abuse, it is critical to determine the influence factors and to predict the opioids spread trend. In this paper, a prediction method combining spatiotemporal characteristics and grey relational analysis model is proposed, and the spatiotemporal prediction model of opioids spread trend based on the grey correlation of multifactor is established by integrating various panel data. The time series model is used to identify and fit the multifactor panel data. The gray relational prediction model is established combining the spatial influence factors by Principal Component Analysis (PCA). Results of the simulated experiment show that the method is accurate and the model is feasible and reasonable.

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References

  1. Yu, Y., Nie, S.: Research progress on prediction and model selection of infectious diseases. Public Health Prev. Med. 29(05), 89–92 (2008)

    Google Scholar 

  2. Wang, Y., Li, K.: Evaluation method for Green jack-up drilling platform design scheme based on improved grey correlation analysis. Appl. Ocean Res. 85(08), 119–127 (2019)

    Google Scholar 

  3. Mohamadi, A., Chan, J.: Risk factors and pooled rate of prolonged opioid use following trauma or surgery. J. Bone Joint Surg. 100(15), 1332–1340 (2018)

    Article  Google Scholar 

  4. Qian, W., Wang, Y.: Grey matrix correlation model based on multi-index panel data and its application. Syst. Eng. 31(10), 70–74 (2013)

    Google Scholar 

  5. Yu, T., Zhou, Y.: Time series prediction based on gray GM(1,1) model. Microcomput. Appli. 31(13), 65–67 (2012)

    Google Scholar 

  6. Su, B., Cao, Y.: Grey prediction model of multi-factor time series. J. xi ‘an Univ. Archit. Techno. (natural science edition). 39(02), 289–292 (2007)

    Google Scholar 

  7. Shen, M., Xue, X., Zhang, X.: Selection of resolution coefficient in grey relational analysis. J. Air Force Eng. Univ. (natural science edition). 4(01), 68–70 (2003)

    Google Scholar 

  8. Dang, Y., Shang, Z.: A novel grey incidence model for the relationship between indicators of panel data and its application. Control and Decision 18(10), 15–38 (2018)

    Google Scholar 

  9. Jiang, K., Cai, Z., Lu, Z., Anterograde joint prediction model of chaotic time series based on RBF neural network. J. Wuhan Univ. Technol. (traffic science and engineering edition) 2(02), 259–261 + 340 (2007)

    Google Scholar 

  10. Ding, S., Sang, Y.: Multivariable grey prediction model based on interaction and its application. Syst. Eng. Electron. Technol. 40(03), 595–602 (2008)

    Google Scholar 

  11. Li, Y., Zhu, S.: Multi-attribute grey target decision method with three-parameter interval grey number. Grey Syst. 6(2), 270–280 (2016)

    Article  Google Scholar 

  12. Dan, R., Wang, S.: Research on combination forecasting model based on time series model and grey model. J. Yanshan Univ. 36(01), 79–83 (2012)

    Google Scholar 

  13. Liu, R., Gao, X.: Uncertain multiple attribute decision making method with interval index and weight based on grey entropy model and its application. Control and Decision 10(13), 13–19 (2018)

    Google Scholar 

  14. Higham, S., Bernstein, L.: Opioid distributors sued by West Virginia counties hit by drug crisis. Washington Post 6(02), 80–88 (2017)

    Google Scholar 

  15. Fang, Y., Li, G.: Decision-making evaluation method for regional rail transit system based on grey entropy. Syst. Eng. 33(02), 152–158 (2015)

    Google Scholar 

  16. Quan, J., Zeng, B.: Maximum entropy methods for weighted grey incidence analysis and applications. Grey Syst. 8(2), 144–155 (2018)

    Article  Google Scholar 

Download references

Acknowledgments

This research is supported by Chinese National College Students’ innovation and entrepreneurship training programs under grant number 201810500032.

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Correspondence to Caiquan Xiong .

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Rao, T., Xiong, C., Liang, Y., Deng, S. (2020). The Spatiotemporal Prediction Model of Opioids Spread Trend Based on Grey Correlation. In: Barolli, L., Hussain, F., Ikeda, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2019. Advances in Intelligent Systems and Computing, vol 993. Springer, Cham. https://doi.org/10.1007/978-3-030-22354-0_16

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