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Accumulatively Confirmed Cases Used for Grey Modeling Prediction of the Medium and Long Term Future Epidemic Trend of Infectious Diseases

Received: 16 April 2023     Accepted: 4 May 2023     Published: 17 May 2023
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Abstract

The author used the actual data obtained from the prevention and control of COVID-19 in China and the cumulative number of confirmed cases obtained at different time intervals to predict the medium and long term (20 days, 40 days and 60 days) future epidemic trend by grey modeling. Research objectives: Grey modeling theory is applied to the modeling and prediction of infectious diseases, appropriate data obtained from the prevention and control of infectious diseases are selected for the simulation and prediction of the medium and long term future epidemic trend of infectious diseases, and an effective method for the prediction of the future epidemic trend of infectious diseases is sought. Research Methods: The author used the actual data obtained from the prevention and control of COVID-19 in China. The trend curve was drawn by statistical data, the trend of epidemic was visually analyzed and observed, and the best series for grey modeling prediction was determined. Then GM(1,1) grey modeling was carried out on the selected series, and the error and accuracy of the built model were tested. Finally, the predicted value of the model was actually verified. Research results: According to the series graph, we selected the cumulative number of confirmed cases with time intervals of 20 days, 40 days and 60 days to model and forecast the future medium and long term epidemic trend of COVID-19 in China, and built the prediction models of cumulative confirmed cases respectively. The average error of the GM(1,1) prediction model established by the cumulative number of confirmed cases at the time node with a time interval of 40 days is too high, reaching 0.6422, and the simulation accuracy is only 37%. It has no practical significance for forecasting. The prediction model of GM(1,1), established by the cumulative number of confirmed cases with a time interval of 20 days, has a large average simulation error of 0.3336 and a simulation accuracy of 67%. Through practical verification, the prediction accuracy of GM(1,1) can reach 99.54%, which has a certain practical value for prediction. The prediction model of GM (1,1) based on the cumulative number of confirmed cases at time nodes with a time interval of 60 days, the average simulation error of GM (1,1) prediction model was 0.01167, and the simulation accuracy was 98.83%. Multiple parameters in the accuracy analysis reached the index of the first-level model. The actual verification of the model showed that the cumulative number of confirmed cases at the predicted time node was 102271. In practice, 107094 cases were recorded, and the predicted number was 4823 cases less than the actual number. The relative error was 0.045, and the prediction accuracy reached 95.49%. Satisfactory gray modeling prediction effect was obtained.

Published in European Journal of Preventive Medicine (Volume 11, Issue 3)
DOI 10.11648/j.ejpm.20231103.11
Page(s) 32-36
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Cumulative Confirmed Cases, Gray Modeling, Epidemic Trend, Prediction

References
[1] Li Ming Quan. Prevention and Control Effects of Different Prevention and Control Mechanisms on the Epidemic of COVID-19 [J]. European Journal of Preventive Medicine, 2022, 10 (6).
[2] Li Ming Quan. Research on the Application of Grey Modeling Theory in the Prediction of Future Epidemic Trend of Infectious Diseases [J]. European Journal of Preventive Medicine, 2023, 11 (1).
[3] Deng Julong, Foundation of Grey Theory (M) Huazhong University of Science and Technology Press, 2002.02.
[4] Liu Sifeng Xie Naiming et al. Grey System Theory and Its Implications (M), Science Press, 2008.
[5] Bernard Rosner (United States) Basis of biostatistics (M) Press: Science Press Time: 2004.
[6] Zhao X F. (2011). Overview of grey system theory (J). Journal of Jilin Provincial Institute of Education (03), 152-154. doi: 10.16083/j.cnki.1671-1580.2011.03.002.
[7] Hu Kun. Research on Grey Prediction Evaluation Method and Application [D]. Nanjing University of Aeronautics and Astronautics, 2004.
[8] Zhang Hu. Research and Application of Time Series Short-term prediction model [D]. Huazhong University of Science and Technology, 2013.
[9] Jia L Y. Grey increment model of population prediction and its application (J). Nanjing University of Information Science & Technology, 2006.
[10] Sun Jing. Research on Product Modeling Design Method Based on Image [D]. Wuhan University of Technology, 2007.
[11] Ma Le. Research on Grey Theory Modeling Method [D]. Dongbei University of Finance and Economics, 2005.
[12] Zhai Xidong. Research on Prediction Model of Port Container Throughput [D]. Dalian University of Technology, 2006.
[13] Rong Luqing, Huang Peihua. Prediction of Cold chain Logistics Demand of Fruit and vegetable in Guangxi and Its Influencing Factors Based on Grey Theory [J]. China, Agricultural Resources and Regional Planning, 2017, 38 (12): 227-234.
[14] Guo Deyong, Li Nian-you, PEI Da-wen, ZHENG Deng-feng. Coal and gas outburst prediction grey theory - neural network [J]. Journal of Beijing university of science and technology, 2007, No. 156 (4): 354-357. The DOI: 10.13374 / j.i ssn1001-053 - x. 2007.04.002.
[15] Wang Zhiming. Research and Application of Multivariate Grey Prediction Model [D]. China University of Mining and Technology2022. DOI: 10.27623/d.cnki.gzkyu.2022.000458.
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  • APA Style

    Li Ming Quan. (2023). Accumulatively Confirmed Cases Used for Grey Modeling Prediction of the Medium and Long Term Future Epidemic Trend of Infectious Diseases. European Journal of Preventive Medicine, 11(3), 32-36. https://doi.org/10.11648/j.ejpm.20231103.11

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    ACS Style

    Li Ming Quan. Accumulatively Confirmed Cases Used for Grey Modeling Prediction of the Medium and Long Term Future Epidemic Trend of Infectious Diseases. Eur. J. Prev. Med. 2023, 11(3), 32-36. doi: 10.11648/j.ejpm.20231103.11

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    AMA Style

    Li Ming Quan. Accumulatively Confirmed Cases Used for Grey Modeling Prediction of the Medium and Long Term Future Epidemic Trend of Infectious Diseases. Eur J Prev Med. 2023;11(3):32-36. doi: 10.11648/j.ejpm.20231103.11

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  • @article{10.11648/j.ejpm.20231103.11,
      author = {Li Ming Quan},
      title = {Accumulatively Confirmed Cases Used for Grey Modeling Prediction of the Medium and Long Term Future Epidemic Trend of Infectious Diseases},
      journal = {European Journal of Preventive Medicine},
      volume = {11},
      number = {3},
      pages = {32-36},
      doi = {10.11648/j.ejpm.20231103.11},
      url = {https://doi.org/10.11648/j.ejpm.20231103.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ejpm.20231103.11},
      abstract = {The author used the actual data obtained from the prevention and control of COVID-19 in China and the cumulative number of confirmed cases obtained at different time intervals to predict the medium and long term (20 days, 40 days and 60 days) future epidemic trend by grey modeling. Research objectives: Grey modeling theory is applied to the modeling and prediction of infectious diseases, appropriate data obtained from the prevention and control of infectious diseases are selected for the simulation and prediction of the medium and long term future epidemic trend of infectious diseases, and an effective method for the prediction of the future epidemic trend of infectious diseases is sought. Research Methods: The author used the actual data obtained from the prevention and control of COVID-19 in China. The trend curve was drawn by statistical data, the trend of epidemic was visually analyzed and observed, and the best series for grey modeling prediction was determined. Then GM(1,1) grey modeling was carried out on the selected series, and the error and accuracy of the built model were tested. Finally, the predicted value of the model was actually verified. Research results: According to the series graph, we selected the cumulative number of confirmed cases with time intervals of 20 days, 40 days and 60 days to model and forecast the future medium and long term epidemic trend of COVID-19 in China, and built the prediction models of cumulative confirmed cases respectively. The average error of the GM(1,1) prediction model established by the cumulative number of confirmed cases at the time node with a time interval of 40 days is too high, reaching 0.6422, and the simulation accuracy is only 37%. It has no practical significance for forecasting. The prediction model of GM(1,1), established by the cumulative number of confirmed cases with a time interval of 20 days, has a large average simulation error of 0.3336 and a simulation accuracy of 67%. Through practical verification, the prediction accuracy of GM(1,1) can reach 99.54%, which has a certain practical value for prediction. The prediction model of GM (1,1) based on the cumulative number of confirmed cases at time nodes with a time interval of 60 days, the average simulation error of GM (1,1) prediction model was 0.01167, and the simulation accuracy was 98.83%. Multiple parameters in the accuracy analysis reached the index of the first-level model. The actual verification of the model showed that the cumulative number of confirmed cases at the predicted time node was 102271. In practice, 107094 cases were recorded, and the predicted number was 4823 cases less than the actual number. The relative error was 0.045, and the prediction accuracy reached 95.49%. Satisfactory gray modeling prediction effect was obtained.},
     year = {2023}
    }
    

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  • TY  - JOUR
    T1  - Accumulatively Confirmed Cases Used for Grey Modeling Prediction of the Medium and Long Term Future Epidemic Trend of Infectious Diseases
    AU  - Li Ming Quan
    Y1  - 2023/05/17
    PY  - 2023
    N1  - https://doi.org/10.11648/j.ejpm.20231103.11
    DO  - 10.11648/j.ejpm.20231103.11
    T2  - European Journal of Preventive Medicine
    JF  - European Journal of Preventive Medicine
    JO  - European Journal of Preventive Medicine
    SP  - 32
    EP  - 36
    PB  - Science Publishing Group
    SN  - 2330-8230
    UR  - https://doi.org/10.11648/j.ejpm.20231103.11
    AB  - The author used the actual data obtained from the prevention and control of COVID-19 in China and the cumulative number of confirmed cases obtained at different time intervals to predict the medium and long term (20 days, 40 days and 60 days) future epidemic trend by grey modeling. Research objectives: Grey modeling theory is applied to the modeling and prediction of infectious diseases, appropriate data obtained from the prevention and control of infectious diseases are selected for the simulation and prediction of the medium and long term future epidemic trend of infectious diseases, and an effective method for the prediction of the future epidemic trend of infectious diseases is sought. Research Methods: The author used the actual data obtained from the prevention and control of COVID-19 in China. The trend curve was drawn by statistical data, the trend of epidemic was visually analyzed and observed, and the best series for grey modeling prediction was determined. Then GM(1,1) grey modeling was carried out on the selected series, and the error and accuracy of the built model were tested. Finally, the predicted value of the model was actually verified. Research results: According to the series graph, we selected the cumulative number of confirmed cases with time intervals of 20 days, 40 days and 60 days to model and forecast the future medium and long term epidemic trend of COVID-19 in China, and built the prediction models of cumulative confirmed cases respectively. The average error of the GM(1,1) prediction model established by the cumulative number of confirmed cases at the time node with a time interval of 40 days is too high, reaching 0.6422, and the simulation accuracy is only 37%. It has no practical significance for forecasting. The prediction model of GM(1,1), established by the cumulative number of confirmed cases with a time interval of 20 days, has a large average simulation error of 0.3336 and a simulation accuracy of 67%. Through practical verification, the prediction accuracy of GM(1,1) can reach 99.54%, which has a certain practical value for prediction. The prediction model of GM (1,1) based on the cumulative number of confirmed cases at time nodes with a time interval of 60 days, the average simulation error of GM (1,1) prediction model was 0.01167, and the simulation accuracy was 98.83%. Multiple parameters in the accuracy analysis reached the index of the first-level model. The actual verification of the model showed that the cumulative number of confirmed cases at the predicted time node was 102271. In practice, 107094 cases were recorded, and the predicted number was 4823 cases less than the actual number. The relative error was 0.045, and the prediction accuracy reached 95.49%. Satisfactory gray modeling prediction effect was obtained.
    VL  - 11
    IS  - 3
    ER  - 

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Author Information
  • Nanchong City Committee Office of the Chinese People's Political Consultative Conference, Nanchong, China

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