{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T23:56:58Z","timestamp":1793577418261,"version":"4.5.0"},"reference-count":37,"publisher":"Cambridge University Press (CUP)","license":[{"start":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T00:00:00Z","timestamp":1701648000000},"content-version":"unspecified","delay-in-days":337,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Epidemiol. Infect."],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:title>Abstract<\/jats:title>\n\t  <jats:p>Hand, foot, and mouth disease (HFMD) is a common childhood infectious disease. The incidence of HFMD has a pronounced seasonal tendency and is closely related to meteorological factors such as temperature, rainfall, and wind speed. In this paper, we propose a combined SARIMA-XGBoost model to improve the prediction accuracy of HFMD in 15 regions of Xinjiang, China. The SARIMA model is used for seasonal trends, and the XGBoost algorithm is applied for the nonlinear effects of meteorological factors. The geographical and temporal weighted regression model is designed to analyze the influence of meteorological factors from temporal and spatial perspectives. The analysis results show that the HFMD exhibits seasonal characteristics, peaking from May to August each year, and the HFMD incidence has significant spatial heterogeneity. The meteorological factors affecting the spread of HFMD vary among regions. Temperature and daylight significantly impact the transmission of the disease in most areas. Based on the verification experiment of forecasting, the proposed SARIMA-XGBoost model is superior to other models in accuracy, especially in regions with a high incidence of HFMD.<\/jats:p>","DOI":"10.1017\/s0950268823001905","type":"journal-article","created":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T09:02:16Z","timestamp":1701680536000},"update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":14,"title":["Analysis of a SARIMA-XGBoost model for hand, foot, and mouth disease in Xinjiang, China"],"prefix":"10.1017","volume":"151","author":[{"given":"Haojie","family":"Man","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanting","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuangyan","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6570-6325","authenticated-orcid":false,"given":"Zhiming","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2023,12,4]]},"reference":[{"key":"S0950268823001905_r32","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1093\/biomet\/52.3-4.591","article-title":"An analysis of variance test for normality (complete samples)","volume":"52","author":"Shapiro","year":"1965","journal-title":"Biometrika"},{"key":"S0950268823001905_r11","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.1017\/S0950268810002256","article-title":"Is hand, foot and mouth disease associated with meteorological parameters?","volume":"138","author":"Ma","year":"2010","journal-title":"Epidemiology and Infection"},{"key":"S0950268823001905_r20","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.3390\/e23101267","article-title":"Network autoregressive model for the prediction of COVID-19 considering the disease interaction in neighboring countries","volume":"23","author":"Sioofy Khoojine","year":"2021","journal-title":"Entropy"},{"key":"S0950268823001905_r7","unstructured":"[7] World Health Organization (2005) Using Climate to Predict Infectious Disease Epidemics. Available at https:\/\/www.who.int\/Westernpacific."},{"key":"S0950268823001905_r19","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1007\/s00484-017-1465-3","article-title":"Predicting the outbreak of hand, foot, and mouth disease in Nanjing, China: A time-series model based on weather variability","volume":"62","author":"Liu","year":"2018","journal-title":"International Journal of Biometeorology"},{"key":"S0950268823001905_r26","doi-asserted-by":"crossref","first-page":"2959","DOI":"10.3390\/ijerph18062959","article-title":"Optimized neural network based on genetic algorithm to construct hand-foot-and-mouth disease prediction and early-warning model","volume":"18","author":"Lin","year":"2021","journal-title":"International Journal of Environmental Research and Public Health"},{"key":"S0950268823001905_r2","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.clindermatol.2014.12.011","article-title":"Update on hand-foot-and-mouth disease","volume":"33","author":"Ventarola","year":"2015","journal-title":"Clinics in Dermatology"},{"key":"S0950268823001905_r3","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1086\/314032","article-title":"Deaths of children during an outbreak of hand, foot, and mouth disease in Sarawak, Malaysia: Clinical and pathological characteristics of the disease","volume":"31","author":"Chan","year":"2000","journal-title":"Clinical Infectious Diseases"},{"key":"S0950268823001905_r12","doi-asserted-by":"crossref","first-page":"849","DOI":"10.4081\/gh.2020.849","article-title":"Spatiotemporal analysis of hand, foot and mouth disease data using time-lag geographically- weighted regression","volume":"15","author":"Hong","year":"2020","journal-title":"Geospatial Health"},{"key":"S0950268823001905_r17","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1017\/S0950268815001144","article-title":"Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA model","volume":"144","author":"Liu","year":"2016","journal-title":"Epidemiology and Infection"},{"key":"S0950268823001905_r4","doi-asserted-by":"crossref","first-page":"e0255222","DOI":"10.1371\/journal.pone.0255222","article-title":"Spatial-temporal heterogeneity and meteorological factors of hand-foot-and-mouth disease in Xinjiang, China from 2008 to 2016","volume":"16","author":"Xie","year":"2021","journal-title":"PLoS One"},{"key":"S0950268823001905_r15","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1542\/peds.2006-3331","article-title":"Epidemiologic features of hand-foot-mouth disease and herpangina caused by enterovirus 71 in Taiwan, 1998\u20132005","volume":"120","author":"Chen","year":"2007","journal-title":"Pediatrics"},{"key":"S0950268823001905_r18","doi-asserted-by":"crossref","first-page":"e82","DOI":"10.1017\/S095026881800362X","article-title":"Time-series modelling and forecasting of hand, foot and mouth disease cases in China from 2008 to 2018","volume":"147","author":"Tian","year":"2019","journal-title":"Epidemiology and Infection"},{"key":"S0950268823001905_r23","first-page":"265","article-title":"Incidence trend prediction of hand-foot-mouth disease based on long short-term memory neural network","volume":"41","author":"Ma","year":"2021","journal-title":"Journal of Computer Applications"},{"key":"S0950268823001905_r1","unstructured":"[1] World Health Organization (2011) A Guide to Clinical Management and Public Health Response for Hand, Foot and Mouth Disease (HFMD). Available at https:\/\/www.who.int\/Westernpacific."},{"key":"S0950268823001905_r28","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1145\/212094.212114","article-title":"Overfitting and undercomputing in machine learning","volume":"27","author":"Dietterich","year":"1995","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"S0950268823001905_r6","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1016\/S1286-4579(01)01429-0","article-title":"Climate change and emerging infectious diseases","volume":"3","author":"Epstein","year":"2001","journal-title":"Microbes and Infection"},{"key":"S0950268823001905_r14","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1097\/INF.0000000000001242","article-title":"The epidemiology of hand, foot and mouth disease in Asia: A systematic review and analysis","volume":"35","author":"Koh","year":"2016","journal-title":"The Pediatric Infectious Disease Journal"},{"key":"S0950268823001905_r16","first-page":"214","article-title":"Characterization of hand, foot, and mouth disease in China between 2008 and 2009","volume":"24","author":"Zhang","year":"2011","journal-title":"Biomedical and Environmental Sciences"},{"key":"S0950268823001905_r29","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1080\/13658810802672469","article-title":"Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices","volume":"24","author":"Huang","year":"2010","journal-title":"International Journal of Geographical Information Science"},{"key":"S0950268823001905_r22","doi-asserted-by":"crossref","first-page":"e0147054","DOI":"10.1371\/journal.pone.0147054","article-title":"Short-term effects of climatic variables on hand, foot, and mouth disease in mainland China, 2008\u20132013: A multilevel spatial Poisson regression model accounting for overdispersion","volume":"11","author":"Liao","year":"2016","journal-title":"PLoS One"},{"key":"S0950268823001905_r30","volume-title":"Time Series Analysis: Forecasting and Control","author":"Box","year":"2015"},{"key":"S0950268823001905_r33","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1080\/01621459.1970.10481180","article-title":"Distribution of residual autocorrelations in autoregressive-integrated moving average time series models","volume":"65","author":"Box","year":"1970","journal-title":"Journal of the American Statistical Association"},{"key":"S0950268823001905_r37","doi-asserted-by":"crossref","unstructured":"[37] Chen, T and Guestrin, C (2016) Xgboost: A scalable tree boosting system. In Proceedings of the Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794.","DOI":"10.1145\/2939672.2939785"},{"key":"S0950268823001905_r35","doi-asserted-by":"crossref","first-page":"3178","DOI":"10.1890\/0012-9658(2000)081[3178:CARTAP]2.0.CO;2","article-title":"Classification and regression trees: A powerful yet simple technique for ecological data analysis","volume":"81","author":"De\u2019ath","year":"2000","journal-title":"Ecology"},{"key":"S0950268823001905_r25","article-title":"Applying machine learning to predict hand-foot-mouth disease outbreaks in Vietnam","volume":"15","author":"Nguyen","year":"2021","journal-title":"Journal of Health Informatics in Developing Countries"},{"key":"S0950268823001905_r10","doi-asserted-by":"crossref","first-page":"e0254223","DOI":"10.1371\/journal.pone.0254223","article-title":"Spatiotemporal characters and influence factors of hand, foot and mouth epidemic in Xinjiang, China","volume":"16","author":"Sun","year":"2021","journal-title":"PloS One"},{"key":"S0950268823001905_r36","first-page":"172","article-title":"The state of boosting","author":"Ridgeway","year":"1999","journal-title":"Computing Science and Statistics"},{"key":"S0950268823001905_r27","doi-asserted-by":"crossref","first-page":"e10","DOI":"10.1017\/S0950268821002508","article-title":"Comparison of different predictive models on HFMD based on weather factors in Zibo city, Shandong Province, China","volume":"150","author":"Liu","year":"2022","journal-title":"Epidemiology and Infection"},{"key":"S0950268823001905_r21","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/S0925-2312(01)00702-0","article-title":"Time series forecasting using a hybrid ARIMA and neural network model","volume":"50","author":"Zhang","year":"2003","journal-title":"Neurocomputing"},{"key":"S0950268823001905_r34","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":"Ljung","year":"1978","journal-title":"Biometrika"},{"key":"S0950268823001905_r8","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.scitotenv.2011.09.055","article-title":"The influence of temperature and humidity on the incidence of hand, foot, and mouth disease in Japan","volume":"410","author":"Onozuka","year":"2011","journal-title":"Science of the Total Environment"},{"key":"S0950268823001905_r13","doi-asserted-by":"crossref","first-page":"1954","DOI":"10.3390\/ijerph18041954","article-title":"Spatial and temporal characteristics of hand-foot-and-mouth disease and its response to climate factors in the Ili River valley region of China","volume":"18","author":"Yi","year":"2021","journal-title":"International Journal of Environmental Research and Public Health"},{"key":"S0950268823001905_r5","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1890\/08-0079.1","article-title":"The ecology of climate change and infectious diseases","volume":"90","author":"Lafferty","year":"2009","journal-title":"Ecology"},{"key":"S0950268823001905_r24","first-page":"1","article-title":"Development and evaluation of a deep learning approach for modeling seasonality and trends in hand-foot-mouth disease incidence in mainland China","volume":"9","author":"Wang","year":"2019","journal-title":"Scientific Reports"},{"key":"S0950268823001905_r9","doi-asserted-by":"crossref","first-page":"e16796","DOI":"10.1371\/journal.pone.0016796","article-title":"Short term effects of weather on hand, foot and mouth disease","volume":"6","author":"Hii","year":"2011","journal-title":"PloS One"},{"key":"S0950268823001905_r31","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1177\/0049124104268644","article-title":"Multimodel inference: Understanding AIC and BIC in model selection","volume":"33","author":"Burnham","year":"2004","journal-title":"Sociological Methods and Research"}],"container-title":["Epidemiology and Infection"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0950268823001905","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T09:54:47Z","timestamp":1702547687000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0950268823001905\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":37,"alternative-id":["S0950268823001905"],"URL":"https:\/\/doi.org\/10.1017\/s0950268823001905","relation":{},"ISSN":["0950-2688","1469-4409"],"issn-type":[{"value":"0950-2688","type":"print"},{"value":"1469-4409","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"\u00a9 The Author(s), 2023. Published by Cambridge University Press","name":"copyright","label":"Copyright","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http:\/\/creativecommons.org\/licenses\/by\/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.","name":"license","label":"License","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}],"article-number":"e200"}}