{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T07:54:30Z","timestamp":1771660470852,"version":"3.50.1"},"reference-count":36,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2021,3,16]],"date-time":"2021-03-16T00:00:00Z","timestamp":1615852800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81202254 and 71974199"],"award-info":[{"award-number":["81202254 and 71974199"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Health and Medical Big Data Research Project of China Medical University","award":["HMB201903105"],"award-info":[{"award-number":["HMB201903105"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\"><jats:title>Objective<\/jats:title><jats:p>Hemorrhagic fever with renal syndrome (HFRS), one of the main public health concerns in mainland China, is a group of clinically similar diseases caused by hantaviruses. Statistical approaches have always been leveraged to forecast the future incidence rates of certain infectious diseases to effectively control their prevalence and outbreak potential. Compared to the use of one base model, model stacking can often produce better forecasting results. In this study, we fitted the monthly reported cases of HFRS in mainland China with a model stacking approach and compared its forecasting performance with those of five base models.<\/jats:p><\/jats:sec><jats:sec id=\"sec002\"><jats:title>Method<\/jats:title><jats:p>We fitted the monthly reported cases of HFRS ranging from January 2004 to June 2019 in mainland China with an autoregressive integrated moving average (ARIMA) model; the Holt-Winter (HW) method, seasonal decomposition of the time series by LOESS (STL); a neural network autoregressive (NNAR) model; and an exponential smoothing state space model with a Box-Cox transformation; ARMA errors; and trend and seasonal components (TBATS), and we combined the forecasting results with the inverse rank approach. The forecasting performance was estimated based on several accuracy criteria for model prediction, including the mean absolute percentage error (MAPE), root-mean-squared error (RMSE) and mean absolute error (MAE).<\/jats:p><\/jats:sec><jats:sec id=\"sec003\"><jats:title>Result<\/jats:title><jats:p>There was a slight downward trend and obvious seasonal periodicity inherent in the time series data for HFRS in mainland China. The model stacking method was selected as the best approach with the best performance in terms of both fitting (RMSE 128.19, MAE 85.63, MAPE 8.18) and prediction (RMSE 151.86, MAE 118.28, MAPE 13.16).<\/jats:p><\/jats:sec><jats:sec id=\"sec004\"><jats:title>Conclusion<\/jats:title><jats:p>The results showed that model stacking by using the optimal mean forecasting weight of the five abovementioned models achieved the best performance in terms of predicting HFRS one year into the future. This study has corroborated the conclusion that model stacking is an easy way to enhance prediction accuracy when modeling HFRS.<\/jats:p><\/jats:sec>","DOI":"10.1371\/journal.pone.0248597","type":"journal-article","created":{"date-parts":[[2021,3,16]],"date-time":"2021-03-16T17:52:51Z","timestamp":1615917171000},"page":"e0248597","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":7,"title":["Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach"],"prefix":"10.1371","volume":"16","author":[{"given":"Guo-hua","family":"Ye","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mirxat","family":"Alim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Guan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"De-sheng","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bao-sen","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5535-1682","authenticated-orcid":true,"given":"Wei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2021,3,16]]},"reference":[{"issue":"12","key":"pone.0248597.ref001","doi-asserted-by":"crossref","first-page":"12129","DOI":"10.3390\/ijerph111212129","article-title":"Spatiotemporal heterogeneity analysis of hemorrhagic fever with renal syndrome in China using geographically weighted regression models","volume":"11","author":"S. Li","year":"2014","journal-title":"International journal of environmental research and public health"},{"key":"pone.0248597.ref002","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.ijid.2016.01.003","article-title":"Haemorrhagic fever with renal syndrome: literature review and distribution analysis in China","volume":"43","author":"L.-X. Zou","year":"2016","journal-title":"International journal of infectious diseases: IJID: official publication of the International Society for Infectious Diseases"},{"issue":"1\u20132","key":"pone.0248597.ref003","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/S0166-3542(02)00205-X","article-title":"Hantavirus","volume":"57","author":"J.P. Clement","year":"2003","journal-title":"Antiviral research"},{"issue":"5","key":"pone.0248597.ref004","first-page":"472","article-title":"Epidemiological progresses of hemorrhagic fever with renal syndrome in China","volume":"112","author":"G. Song","year":"1999","journal-title":"Chinese medical journal"},{"issue":"9","key":"pone.0248597.ref005","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.3201\/eid1309.061481","article-title":"Landscape elements and Hantaan virus-related hemorrhagic fever with renal syndrome, People\u2019s Republic of China","volume":"13","author":"L. Yan","year":"2007","journal-title":"Emerging infectious diseases"},{"issue":"4","key":"pone.0248597.ref006","doi-asserted-by":"crossref","first-page":"e61536","DOI":"10.1371\/journal.pone.0061536","article-title":"Investigating the effects of food available and climatic variables on the animal host density of hemorrhagic Fever with renal syndrome in changsha, china","volume":"8","author":"H. Xiao","year":"2013","journal-title":"PloS one"},{"key":"pone.0248597.ref007","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1186\/1471-2334-11-218","article-title":"Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model","volume":"11","author":"Q. Liu","year":"2011","journal-title":"BMC infectious diseases"},{"key":"pone.0248597.ref008","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1186\/s12879-016-1404-7","article-title":"Prevalence of hemorrhagic fever with renal syndrome in Yiyuan County, China, 2005\u20132014","volume":"16","author":"T. Wang","year":"2016","journal-title":"BMC infectious diseases"},{"issue":"12","key":"pone.0248597.ref009","first-page":"1394","article-title":"Application of nonlinear autoregressive neural network in predicting incidence tendency of hemorrhagic fever with renal syndrome","volume":"36","author":"W. Wu","year":"2015","journal-title":"Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi"},{"issue":"8","key":"pone.0248597.ref010","doi-asserted-by":"crossref","first-page":"e0135492","DOI":"10.1371\/journal.pone.0135492","article-title":"Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China","volume":"10","author":"W. Wu","year":"2015","journal-title":"PLoS One"},{"issue":"5","key":"pone.0248597.ref011","first-page":"4393","volume":"53","author":"J.S. Rice","year":"2017","journal-title":"How are streamflow responses to the El Nino Southern Oscillation affected by watershed characteristics?"},{"key":"pone.0248597.ref012","first-page":"142760","article-title":"Improving the robustness of beach water quality modeling using an ensemble machine learning approach","author":"L. Wang","year":"2020","journal-title":"Science of The Total Environment"},{"key":"pone.0248597.ref013","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1016\/j.scitotenv.2018.04.040","article-title":"Development of a stacked ensemble model for forecasting and analyzing daily average PM2.5 concentrations in Beijing, China","volume":"635","author":"B. Zhai","year":"2018","journal-title":"Science of The Total Environment"},{"issue":"8","key":"pone.0248597.ref014","doi-asserted-by":"crossref","first-page":"1596","DOI":"10.3390\/ijerph15081596","article-title":"Predicting Infectious Disease Using Deep Learning and Big Data","volume":"15","author":"S. Chae","year":"2018","journal-title":"International journal of environmental research and public health"},{"issue":"4","key":"pone.0248597.ref015","first-page":"436","article-title":"Application of ARIMA model to predict number of malaria cases in China","volume":"29","author":"H. Hui-Yu","year":"2017","journal-title":"Zhongguo xue xi chong bing fang zhi za zhi = Chinese journal of schistosomiasis control"},{"issue":"1","key":"pone.0248597.ref016","first-page":"128","article-title":"Disease management with ARIMA model in time series","volume":"11","author":"R.C. Sato","year":"2013","journal-title":"Einstein (Sao Paulo, Brazil)"},{"issue":"2","key":"pone.0248597.ref017","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1093\/biomet\/65.2.297","article-title":"On a Measure of Lack of Fit in Time Series Models","volume":"65","author":"G.M. Ljung","year":"1978","journal-title":"Biometrika"},{"issue":"1","key":"pone.0248597.ref018","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.ijforecast.2003.09.015","article-title":"Forecasting seasonals and trends by exponentially weighted moving averages","volume":"20","author":"C.C. Holt","year":"2004","journal-title":"International Journal of Forecasting"},{"issue":"3","key":"pone.0248597.ref019","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1287\/mnsc.6.3.324","article-title":"Forecasting Sales by Exponentially Weighted Moving Averages","volume":"6","author":"P.R. Winters","year":"1960","journal-title":"Management Science"},{"issue":"3","key":"pone.0248597.ref020","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1002\/for.1125","article-title":"Robust forecasting with exponential and Holt\u2013Winters smoothing","volume":"29","author":"S. Gelper","year":"2010","journal-title":"Journal of Forecasting"},{"issue":"1","key":"pone.0248597.ref021","first-page":"3","article-title":"STL: a seasonal-trend decomposition","volume":"6","author":"R.B. Cleveland","year":"1990","journal-title":"Journal of official statistics"},{"issue":"12","key":"pone.0248597.ref022","first-page":"1198","article-title":"Preliminary application of Back-Propagation artificial neural network model on the prediction of infectious diarrhea incidence in Shanghai","volume":"34","author":"J. Li","year":"2013","journal-title":"Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi"},{"issue":"40","key":"pone.0248597.ref023","doi-asserted-by":"crossref","first-page":"7563","DOI":"10.1016\/j.atmosenv.2010.11.049","article-title":"Non-methane hydrocarbons in the atmosphere of a Metropolitan City and a background site in South Korea: Sources and health risk potentials","volume":"45","author":"E. Choi","year":"2011","journal-title":"Atmospheric Environment"},{"key":"pone.0248597.ref024","doi-asserted-by":"crossref","first-page":"109387","DOI":"10.1016\/j.ecoenv.2019.109387","article-title":"Spatiotemporal variations and characterization of the chronic cancer risk associated with benzene exposure","volume":"182","author":"M. Sakizadeh","year":"2019","journal-title":"Ecotoxicology and environmental safety"},{"issue":"10","key":"pone.0248597.ref025","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1287\/mnsc.31.10.1237","article-title":"Forecasting Trends in Time Series","volume":"31","author":"E.S. Gardner","year":"1985","journal-title":"Management Science"},{"issue":"1","key":"pone.0248597.ref026","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.jeconom.2005.07.015","article-title":"Persistence in forecasting performance and conditional combination strategies","volume":"135","author":"M. Aiolfi","year":"2006","journal-title":"Journal of Econometrics"},{"issue":"1","key":"pone.0248597.ref027","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/0169-2070(92)90008-W","article-title":"Error measures for generalizing about forecasting methods: Empirical comparisons","volume":"8","author":"J.S. Armstrong","year":"1992","journal-title":"International Journal of Forecasting"},{"key":"pone.0248597.ref028","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.ijid.2015.02.010","article-title":"Transmission of haemorrhagic fever with renal syndrome in china and the role of climate factors: a review","volume":"33","author":"A. Hansen","year":"2015","journal-title":"Int J Infect Dis"},{"key":"pone.0248597.ref029","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1016\/j.scitotenv.2018.04.407","article-title":"Impact of meteorological factors on hemorrhagic fever with renal syndrome in 19 cities in China, 2005\u20132014","volume":"636","author":"J. Xiang","year":"2018","journal-title":"Sci Total Environ"},{"issue":"11","key":"pone.0248597.ref030","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1093\/infdis\/jiu336","article-title":"The role of mites in the transmission and maintenance of Hantaan virus (Hantavirus: Bunyaviridae)","volume":"210","author":"X.J. Yu","year":"2014","journal-title":"J Infect Dis"},{"issue":"1","key":"pone.0248597.ref031","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1186\/s12879-017-2506-6","article-title":"The influence of climatic factors on the development of hemorrhagic fever with renal syndrome and leptospirosis during the peak season in Korea: an ecologic study","volume":"17","author":"Y.P. Joshi","year":"2017","journal-title":"BMC Infect Dis"},{"issue":"9","key":"pone.0248597.ref032","doi-asserted-by":"crossref","first-page":"e14640","DOI":"10.1097\/MD.0000000000014640","article-title":"Effects of climate factors on hemorrhagic fever with renal syndrome in Changchun, 2013 to 2017","volume":"98","author":"Q. Zhao","year":"2019","journal-title":"Medicine (Baltimore)"},{"issue":"6","key":"pone.0248597.ref033","doi-asserted-by":"crossref","first-page":"e025773","DOI":"10.1136\/bmjopen-2018-025773","article-title":"Comparison of autoregressive integrated moving average model and generalised regression neural network model for prediction of haemorrhagic fever with renal syndrome in China: a time-series study","volume":"9","author":"Y.W. Wang","year":"2019","journal-title":"BMJ Open"},{"issue":"13","key":"pone.0248597.ref034","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.1017\/S0950268818002030","article-title":"Spatiotemporal analysis and forecasting model of hemorrhagic fever with renal syndrome in mainland China","volume":"146","author":"L. Sun","year":"2018","journal-title":"Epidemiol Infect"},{"issue":"1","key":"pone.0248597.ref035","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1186\/s12874-020-01130-8","article-title":"SARFIMA model prediction for infectious diseases: application to hemorrhagic fever with renal syndrome and comparing with SARIMA","volume":"20","author":"C. Qi","year":"2020","journal-title":"BMC Med Res Methodol"},{"issue":"11","key":"pone.0248597.ref036","doi-asserted-by":"crossref","first-page":"e0207518","DOI":"10.1371\/journal.pone.0207518","article-title":"A new Seasonal Difference Space-Time Autoregressive Integrated Moving Average (SD-STARIMA) model and spatiotemporal trend prediction analysis for Hemorrhagic Fever with Renal Syndrome (HFRS)","volume":"13","author":"Y. Zhao","year":"2018","journal-title":"PLoS One"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0248597","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T02:57:18Z","timestamp":1724641038000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0248597"}},"subtitle":[],"editor":[{"given":"Chi-Hua","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,3,16]]},"references-count":36,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,3,16]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0248597","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,16]]}}}