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Article type: Research Article
Authors: Peng, Li-Linga | Bi, Xiao-Fenga | Fan, Guo-Fenga | Wang, Ze-Pinga | Hong, Wei-Chiangb; c; *
Affiliations: [a] School of Mathematics & Statistics Science, Ping Ding Shan University, Ping Ding Shan, Henan, China | [b] Department of Information Management, Asia Eastern University of Science and Technology, New Taipei, Taiwan | [c] Department of Information Management, Yuan Ze University, Zhongli, Taiwan
Correspondence: [*] Corresponding author. Wei-Chiang Hong. E-mail: samuelsonhong@gmail.com.
Abstract: This paper proposes a new epidemic prediction model that hybridizes several models, such as the autoregressive integrated moving average model (ARIMA), random forest (RF), and response surface method (RSM). The modeling process based on ensemble empirical mode decomposition (EEMD) is particularly suitable for dealing with non-stationary and nonlinear data. ARIMA’s timeliness and difference have strong deterministic information extraction ability. RF is robust and stable, with fast speed, and strong generalization ability. Under the adjustability and correspondence of the response surface, the comprehensiveness of the model is well demonstrated. Taking the United States as an example, the proposed ARIMA-RF-RSM model is used to explore the development mechanism of the early epidemic according to the data of the early epidemic of coronavirus disease 2019 (COVID-19). The proposed model has high prediction accuracy (mean absolute percentage error (MAPE) is 1.97% and root mean square error (RSME) is 7.24%). It helps to take effective prevention and control measures in time. In addition, the model has universal applicability to the analysis of disease transmission in relevant areas.
Keywords: COVID-19, random forest (RF), response surface method (RSM), average model
DOI: 10.3233/JIFS-231588
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 369-388, 2024
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