{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:04:40Z","timestamp":1792789480992,"version":"4.5.0"},"reference-count":38,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T00:00:00Z","timestamp":1652313600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Major Science and Technology Projects of the 13th five-year plan of China","award":["2018ZX10715009"],"award-info":[{"award-number":["2018ZX10715009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>Acquired immune deficiency syndrome (AIDS) is a serious public health problem. This study aims to establish a combined model of seasonal autoregressive integrated moving average (SARIMA) and Prophet models based on an L1-norm to predict the incidence of AIDS in Henan province, China. The monthly incidences of AIDS in Henan province from 2012 to 2020 were obtained from the Health Commission of Henan Province. A SARIMA model, a Prophet model, and two combined models were adopted to fit the monthly incidence of AIDS using the data from January 2012 to December 2019. The data from January 2020 to December 2020 was used to verify. The mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used to compare the prediction effect among the models. The results showed that the monthly incidence fluctuated from 0.05 to 0.50 per 100,000 individuals, and the monthly incidence of AIDS had a certain periodicity in Henan province. In addition, the prediction effect of the Prophet model was better than SARIMA model, the combined model was better than the single models, and the combined model based on the L1-norm had the best effect values (MSE = 0.0056, MAE = 0.0553, MAPE = 43.5337). This indicated that, compared with the L2-norm, the L1-norm improved the prediction accuracy of the combined model. The combined model of SARIMA and Prophet based on the L1-norm is a suitable method to predict the incidence of AIDS in Henan. Our findings can provide theoretical evidence for the government to formulate policies regarding AIDS prevention.<\/jats:p>","DOI":"10.3390\/ijerph19105910","type":"journal-article","created":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T21:46:53Z","timestamp":1652392013000},"page":"5910","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["A Combined Model of SARIMA and Prophet Models in Forecasting AIDS Incidence in Henan Province, China"],"prefix":"10.3390","volume":"19","author":[{"given":"Zixiao","family":"Luo","sequence":"first","affiliation":[{"name":"Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0895-2957","authenticated-orcid":false,"given":"Xiaocan","family":"Jia","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junzhe","family":"Bao","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijuan","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huili","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengying","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongli","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuezhong","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,12]]},"reference":[{"key":"ref_1","unstructured":"(2021, April 01). Joint United Nations Programme on HIV\/AIDS. Available online: https:\/\/www.unaids.org\/en."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1007\/s11904-019-00478-x","article-title":"Optimizing Treatment for Adults with HIV\/AIDS in China: Successes over Two Decades and Remaining Challenges","volume":"17","author":"Cao","year":"2020","journal-title":"Curr. HIV AIDS Rep."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/S0140-6736(05)71551-8","article-title":"\u201cBlood heads\u201d and AIDS haunt China\u2019s countryside","volume":"357","author":"Beach","year":"2001","journal-title":"Lancet"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/S0140-6736(95)92698-4","article-title":"HIV-1 infection in commercial plasma donors in China","volume":"346","author":"Wu","year":"1995","journal-title":"Lancet"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1038\/sj.cr.7290362","article-title":"Current progress of China\u2019s free ART program","volume":"15","author":"Zhang","year":"2005","journal-title":"Cell Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.ijid.2019.02.016","article-title":"Epidemiological analyses of regional and age differences of HIV\/AIDS prevalence in China, 2004\u20132016","volume":"81","author":"Qiao","year":"2019","journal-title":"Int. J. Infect. Dis."},{"key":"ref_7","unstructured":"(2021, April 01). Health Commission of Henan Province, Available online: http:\/\/wsjkw.henan.gov.cn\/."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1007\/s40273-019-00817-1","article-title":"Structural Design and Data Requirements for Simulation Modelling in HIV\/AIDS: A Narrative Review","volume":"37","author":"Zang","year":"2019","journal-title":"PharmacoEconomics"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e194","DOI":"10.1017\/S095026881900075X","article-title":"Application of a long short-term memory neural network: A burgeoning method of deep learning in forecasting HIV incidence in Guangxi, China","volume":"147","author":"Wang","year":"2019","journal-title":"Epidemiol. Infect."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, Z., and Li, Y. (2020). A comparative study on the prediction of the BP artificial neural network model and the ARIMA model in the incidence of AIDS. BMC Med. Inform. Decis. Mak., 20.","DOI":"10.1186\/s12911-020-01157-3"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"135491","DOI":"10.1016\/j.scitotenv.2019.135491","article-title":"Relationship of meteorological factors and human brucellosis in Hebei province, China","volume":"703","author":"Cao","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2929","DOI":"10.1007\/s00521-020-05434-0","article-title":"ARIMA models for predicting the end of COVID-19 pandemic and the risk of second rebound","volume":"33","author":"Malki","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.psep.2020.10.048","article-title":"Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia","volume":"149","author":"Elsheikh","year":"2021","journal-title":"Process Saf. Environ. Prot. Trans. Inst. Chem. Eng. Part B"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1080\/00031305.2017.1380080","article-title":"Forecasting at Scale","volume":"72","author":"Taylor","year":"2018","journal-title":"Am. Stat."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"102494","DOI":"10.1016\/j.bspc.2021.102494","article-title":"Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods","volume":"66","author":"Talkhi","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_16","first-page":"1","article-title":"An Eye on the Future of COVID-19: Prediction of Likely Positive Cases and Fatality in India over a 30-Day Horizon Using the Prophet Model","volume":"18","author":"Tulshyan","year":"2020","journal-title":"Disaster Med. Public Health Prep."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Reich, N.G., McGowan, C.J., Yamana, T.K., Tushar, A., Ray, E.L., Osthus, D., Kandula, S., Brooks, L.C., Crawford-Crudell, W., and Gibson, G.C. (2019). Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S. PLoS Comput. Biol., 15.","DOI":"10.1371\/journal.pcbi.1007486"},{"key":"ref_18","first-page":"3","article-title":"Stacked generalization and simulated evolution","volume":"39","author":"English","year":"1996","journal-title":"Bio Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1016\/j.envpol.2018.09.025","article-title":"Optimal-combined model for air quality index forecasting: 5 cities in North China","volume":"243","author":"Zhu","year":"2018","journal-title":"Environ. Pollut."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yamana, T.K., Kandula, S., and Shaman, J. (2017). Individual versus superensemble forecasts of seasonal influenza outbreaks in the United States. PLoS Comput. Biol., 13.","DOI":"10.1371\/journal.pcbi.1005801"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1672","DOI":"10.1109\/TPAMI.2008.114","article-title":"Principal component analysis based on l1-norm maximization","volume":"30","author":"Kwak","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.neunet.2019.01.016","article-title":"Robust capped L1-norm twin support vector machine","volume":"114","author":"Wang","year":"2019","journal-title":"Neural Netw. Off. J. Int. Neural Netw. Soc."},{"key":"ref_23","first-page":"1","article-title":"An L\u2081-and-L\u2082-Norm-Oriented Latent Factor Model for Recommender Systems","volume":"22","author":"Wu","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.jenvman.2019.05.124","article-title":"A combined forecasting structure based on the L(1) norm: Application to the air quality","volume":"246","author":"Wang","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1023\/A:1015663013620","article-title":"Use of poisson regression and box-jenkins models to evaluate the short-term effects of environmental noise levels on daily emergency admissions in Madrid, Spain","volume":"17","author":"Tobias","year":"2001","journal-title":"Eur. J. Epidemiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.chemosphere.2017.03.029","article-title":"Evaluation of a multiple linear regression model and SARIMA model in forecasting (7)Be air concentrations","volume":"177","author":"Bas","year":"2017","journal-title":"Chemosphere"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xia, Y., Liao, C., Wu, D., and Liu, Y. (2020). Dynamic Analysis and Prediction of Food Nitrogen Footprint of Urban and Rural Residents in Shanghai. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17051760"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"103817","DOI":"10.1016\/j.rinp.2021.103817","article-title":"Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?","volume":"21","author":"Devaraj","year":"2021","journal-title":"Results Phys."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1097\/COH.0000000000000659","article-title":"The interplay between HIV and COVID-19: Summary of the data and responses to date","volume":"16","author":"Brown","year":"2021","journal-title":"Curr. Opin. HIV AIDS"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"(2020). How to stop COVID-19 fuelling a resurgence of AIDS, malaria and tuberculosis. Nature, 584, 169.","DOI":"10.1038\/d41586-020-02334-0"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Xu, B., Li, J., and Wang, M. (2020). Epidemiological and time series analysis on the incidence and death of AIDS and HIV in China. BMC Public Health, 20.","DOI":"10.1186\/s12889-020-09977-8"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1016\/j.jiph.2018.04.009","article-title":"Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model","volume":"11","author":"Mao","year":"2018","journal-title":"J. Infect. Public Health"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lu, J., and Meyer, S. (2020). Forecasting Flu Activity in the United States: Benchmarking an Endemic-Epidemic Beta Model. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17041381"},{"key":"ref_34","first-page":"2052","article-title":"Forecasting of COVID-19 pandemic in Nigeria using real statistical data","volume":"2021","author":"Peter","year":"2021","journal-title":"Commun. Math. Biol. Neurosci."},{"key":"ref_35","first-page":"1","article-title":"A Hybrid Residual Dilated LSTM and Exponential Smoothing Model for Midterm Electric Load Forecasting","volume":"8","author":"Dudek","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Shuyu, L., and Xuan, Y.L.R. (2019). Forecasting coal consumption in India by 2030: Using linear modified linear (MGM-ARIMA) and linear modified nonlinear (BP-ARIMA) combined models. Sustainability, 11.","DOI":"10.3390\/su11030695"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"786983","DOI":"10.3389\/fams.2021.786983","article-title":"Modeling of COVID-19 Pandemic vis-\u00e0-vis Some Socio-Economic Factors","volume":"7","author":"Oshinubi","year":"2022","journal-title":"Front. Appl. Math. Stat."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Oshinubi, K., Rachdi, M., and Demongeot, J. (2021). Analysis of Reproduction Number R0 of COVID-19 Using Current Health Expenditure as Gross Domestic Product Percentage (CHE\/GDP) across Countries. Healthcare, 9.","DOI":"10.3390\/healthcare9101247"}],"container-title":["International Journal of Environmental Research and Public Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1660-4601\/19\/10\/5910\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:10:01Z","timestamp":1760137801000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1660-4601\/19\/10\/5910"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,12]]},"references-count":38,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["ijerph19105910"],"URL":"https:\/\/doi.org\/10.3390\/ijerph19105910","relation":{},"ISSN":["1660-4601"],"issn-type":[{"value":"1660-4601","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,12]]}}}