{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T22:42:05Z","timestamp":1779144125677,"version":"3.51.4"},"reference-count":31,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,5,24]],"date-time":"2023-05-24T00:00:00Z","timestamp":1684886400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"111 project","award":["B17040"],"award-info":[{"award-number":["B17040"]}]},{"name":"111 project","award":["2022KDZ05"],"award-info":[{"award-number":["2022KDZ05"]}]},{"name":"111 project","award":["CX2022363"],"award-info":[{"award-number":["CX2022363"]}]},{"name":"Ministry of Education","award":["B17040"],"award-info":[{"award-number":["B17040"]}]},{"name":"Ministry of Education","award":["2022KDZ05"],"award-info":[{"award-number":["2022KDZ05"]}]},{"name":"Ministry of Education","award":["CX2022363"],"award-info":[{"award-number":["CX2022363"]}]},{"name":"Graduate Innovative Fund of Wuhan Institute of Technology","award":["B17040"],"award-info":[{"award-number":["B17040"]}]},{"name":"Graduate Innovative Fund of Wuhan Institute of Technology","award":["2022KDZ05"],"award-info":[{"award-number":["2022KDZ05"]}]},{"name":"Graduate Innovative Fund of Wuhan Institute of Technology","award":["CX2022363"],"award-info":[{"award-number":["CX2022363"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Since 2020, COVID-19 has repeatedly arisen around the world, which has had a significant impact on the global economy and culture. The prediction of the COVID-19 epidemic will help to deal with the current epidemic and similar risks that may arise in the future. So, this paper proposes a hybrid prediction model based on particle swarm optimization variational mode decomposition (PSO-VMD), Long Short-Term Memory Network (LSTM) and AdaBoost algorithm. To address the issue of determining the optimal number of modes K and the penalty factor (\u03b1) in the variational mode decomposition (VMD), an adaptive value for particle swarm optimization (PSO) is proposed. Specifically, the weighted average sample entropy of the relevant coefficients is utilized to determine the adaptive value. First, the epidemic data are decomposed into multiple modal components, known as intrinsic mode functions (IMFs), using PSO-VMD. These components, along with policy-based factors, are integrated to form a multivariate forecast dataset. Next, each IMF is predicted using AdaBoost-LSTM. Finally, the prediction results of all the IMF components are reconstructed to obtain the final prediction result. Our proposed method is validated by the cumulative confirmed data of Hubei and Hebei provinces. Specifically, in the case of cumulative confirmation data, the coefficient of determination (R2) of the mixed model is increased compared to the control model, and the average mean absolute error (MAE) and root mean square error (RMSE) decreased. The experimental results demonstrate that the VMD\u2013AdaBoost\u2013LSTM model achieves the highest prediction accuracy, thereby offering a new approach to COVID-19 epidemic prediction.<\/jats:p>","DOI":"10.3390\/ijgi12060215","type":"journal-article","created":{"date-parts":[[2023,5,25]],"date-time":"2023-05-25T02:00:55Z","timestamp":1684980055000},"page":"215","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Hybrid Prediction Model Based on Decomposed and Synthesized COVID-19 Cumulative Confirmed Data"],"prefix":"10.3390","volume":"12","author":[{"given":"Zongyou","family":"Xia","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2095-0823","authenticated-orcid":false,"given":"Gonghao","family":"Duan","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting","family":"Xu","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 443002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,24]]},"reference":[{"key":"ref_1","first-page":"258","article-title":"On the reliability of predictions on COVID-19 dynamics: A systematic and critical review of modelling techniques","volume":"6","author":"Gnanvi","year":"2021","journal-title":"Infect. Dis. Model."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1038\/s41746-020-00372-6","article-title":"Machine learning-based prediction of COVID-19 diagnosis based on symptoms","volume":"4","author":"Zoabi","year":"2021","journal-title":"NPJ Digit. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3161","DOI":"10.1016\/j.aej.2021.01.025","article-title":"Building a sensible SIR estimation model for COVID-19 outspread in Kuwait","volume":"60","author":"Alenezi","year":"2021","journal-title":"Alex. Eng. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4725","DOI":"10.1038\/s41598-021-84055-6","article-title":"Inefficiency of SIR models in forecasting COVID-19 epidemic: A case study of Isfahan","volume":"11","author":"Moein","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"110057","DOI":"10.1016\/j.chaos.2020.110057","article-title":"A SIR model assumption for the spread of COVID-19 in different communities","volume":"139","author":"Cooper","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1007\/s11071-020-05743-y","article-title":"SEIR modeling of the COVID-19 and its dynamics","volume":"101","author":"He","year":"2020","journal-title":"Nonlinear Dyn."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"110072","DOI":"10.1016\/j.chaos.2020.110072","article-title":"Stability analysis and numerical simulation of SEIR model for pandemic COVID-19 spread in Indonesia","volume":"139","author":"Annas","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"21256","DOI":"10.1038\/s41598-020-77628-4","article-title":"Management strategies in a SEIR-type model of COVID 19 community spread","volume":"10","author":"Williams","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ren, X., Weisel, C.P., and Georgopoulos, P.G. (2021). Modeling effects of spatial heterogeneities and layered exposure interventions on the spread of COVID-19 across New Jersey. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph182211950"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"101489","DOI":"10.1109\/ACCESS.2020.2997311","article-title":"COVID-19 future forecasting using supervised machine learning models","volume":"8","author":"Rustam","year":"2020","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"110512","DOI":"10.1016\/j.chaos.2020.110512","article-title":"Data analysis of COVID-19 pandemic and short-term cumulative case forecasting using machine learning time series methods","volume":"142","year":"2021","journal-title":"Chaos Solitons Fractals"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"109864","DOI":"10.1016\/j.chaos.2020.109864","article-title":"Time series forecasting of COVID-19 transmission in Canada using LSTM networks","volume":"135","author":"Chimmula","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chandra, R., Jain, A., and Chauhan, D.S. (2022). Deep learning via LSTM models for COVID-19 infection forecasting in India. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0262708"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Shahin, A.I., and Almotairi, S. (2021). A deep learning BiLSTM encoding-decoding model for COVID-19 pandemic spread forecasting. Fractal Fract., 5.","DOI":"10.3390\/fractalfract5040175"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"104509","DOI":"10.1016\/j.rinp.2021.104509","article-title":"On the accuracy of ARIMA based prediction of COVID-19 spread","volume":"27","author":"Alabdulrazzaq","year":"2021","journal-title":"Results Phys."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1016\/j.jiph.2020.06.001","article-title":"Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions","volume":"13","author":"Alzahrani","year":"2020","journal-title":"J. Infect. Public Health"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Delli Compagni, R., Cheng, Z., Russo, S., and Van Boeckel, T.P. (2022). A hybrid Neural Network-SEIR model for forecasting intensive care occupancy in Switzerland during COVID-19 epidemics. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0263789"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"109866","DOI":"10.1016\/j.chaos.2020.109866","article-title":"Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19","volume":"135","author":"Singh","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, S., Wan, Y., Yang, W., Tan, A., Jian, J., and Lei, X. (2022). A Hybrid Model for Coronavirus Disease 2019 Forecasting Based on Ensemble Empirical Mode Decomposition and Deep Learning. Int. J. Environ. Res. Public Health, 20.","DOI":"10.3390\/ijerph20010617"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"109440","DOI":"10.1016\/j.knosys.2022.109440","article-title":"An ensemble multi-step M-RMLSSVR model based on VMD and two-group strategy for day-ahead short-term load forecasting","volume":"252","author":"Yuan","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"112254","DOI":"10.1016\/j.enconman.2019.112254","article-title":"A new prediction method based on VMD-PRBF-ARMA-E model considering wind speed characteristic","volume":"203","author":"Zhang","year":"2020","journal-title":"Energy Convers. Manag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"104713","DOI":"10.1016\/j.engappai.2022.104713","article-title":"Intelligent fault diagnosis of train axle box bearing based on parameter optimization VMD and improved DBN","volume":"110","author":"Jin","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1126\/science.aba9757","article-title":"The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak","volume":"368","author":"Chinazzi","year":"2020","journal-title":"Science"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"eabd6370","DOI":"10.1126\/sciadv.abd6370","article-title":"Differential effects of intervention timing on COVID-19 spread in the United States","volume":"6","author":"Pei","year":"2020","journal-title":"Sci. Adv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.psep.2021.10.047","article-title":"A new hybrid prediction model of cumulative COVID-19 confirmed data","volume":"157","author":"Li","year":"2022","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1109\/TSP.2013.2288675","article-title":"Variational mode decomposition","volume":"62","author":"Dragomiretskiy","year":"2013","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"21715","DOI":"10.1038\/s41598-021-01119-3","article-title":"Spatio-temporal prediction of the COVID-19 pandemic in US counties: Modeling with a deep LSTM neural network","volume":"11","author":"Nikparvar","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"19428","DOI":"10.1007\/s11356-020-08452-6","article-title":"A hybrid prediction model for forecasting wind energy resources","volume":"27","author":"Zhang","year":"2020","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4563","DOI":"10.1007\/s12206-022-0819-x","article-title":"Rolling bearing composite fault diagnosis method based on EEMD fusion feature","volume":"36","author":"Zhao","year":"2022","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107513","DOI":"10.1016\/j.compchemeng.2021.107513","article-title":"Crude oil price prediction: A comparison between AdaBoost-LSTM and AdaBoost-GRU for improving forecasting performance","volume":"155","author":"Busari","year":"2021","journal-title":"Comput. Chem. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London","volume":"454","author":"Huang","year":"1998","journal-title":"Ser. A Math. Phys. Eng. Sci."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/6\/215\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:41:27Z","timestamp":1760125287000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/6\/215"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,24]]},"references-count":31,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["ijgi12060215"],"URL":"https:\/\/doi.org\/10.3390\/ijgi12060215","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,24]]}}}