{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T15:07:22Z","timestamp":1793113642680,"version":"4.5.0"},"reference-count":45,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2021,12,22]],"date-time":"2021-12-22T00:00:00Z","timestamp":1640131200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2018MH037"],"award-info":[{"award-number":["ZR2018MH037"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Hand, foot and mouth disease (HFMD) is an increasingly serious public health problem, and it has caused an outbreak in China every year since 2008. Predicting the incidence of HFMD and analyzing its influential factors are of great significance to its prevention. Now, machine learning has shown advantages in infectious disease models, but there are few studies on HFMD incidence based on machine learning that cover all the provinces in mainland China. In this study, we proposed two different machine learning algorithms, Random Forest and eXtreme Gradient Boosting (XGBoost), to perform our analysis and prediction. We first used Random Forest to examine the association between HFMD incidence and potential influential factors for 31 provinces in mainland China. Next, we established Random Forest and XGBoost prediction models using meteorological and social factors as the predictors. Finally, we applied our prediction models in four different regions of mainland China and evaluated the performance of them. Our results show that: 1) Meteorological factors and social factors jointly affect the incidence of HFMD in mainland China. Average temperature and population density are the two most significant influential factors; 2) Population flux has different delayed effect in affecting HFMD incidence in different regions. From a national perspective, the model using population flux data delayed for one month has better prediction performance; 3) The prediction capability of XGBoost model was better than that of Random Forest model from the overall perspective. XGBoost model is more suitable for predicting the incidence of HFMD in mainland China.<\/jats:p>","DOI":"10.1371\/journal.pone.0261629","type":"journal-article","created":{"date-parts":[[2021,12,22]],"date-time":"2021-12-22T18:24:44Z","timestamp":1640197484000},"page":"e0261629","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":46,"title":["Analysis and prediction of hand, foot and mouth disease incidence in China using Random Forest and XGBoost"],"prefix":"10.1371","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7742-5942","authenticated-orcid":true,"given":"Delin","family":"Meng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2919-6559","authenticated-orcid":true,"given":"Jijun","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2021,12,22]]},"reference":[{"issue":"4","key":"pone.0261629.ref001","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/S1473-3099(13)70342-6","article-title":"Hand, foot, and mouth disease in China, 2008\u201312: an epidemiological study","volume":"14","author":"W Xing","year":"2014","journal-title":"The Lancet Infectious Diseases"},{"issue":"1","key":"pone.0261629.ref002","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1186\/s12879-016-2008-y","article-title":"Age patterns and transmission characteristics of hand, foot and mouth disease in China","volume":"16","author":"J Zhao","year":"2016","journal-title":"BMC Infect Dis"},{"issue":"1","key":"pone.0261629.ref003","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1186\/s12879-019-4153-6","article-title":"The complex transmission seasonality of hand, foot, and mouth disease and its driving factors","volume":"19","author":"J Zhao","year":"2019","journal-title":"BMC Infect Dis"},{"issue":"7","key":"pone.0261629.ref004","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1016\/S1473-3099(17)30227-X","article-title":"Epidemiological features of and changes in incidence of infectious diseases in China in the first decade after the SARS outbreak: an observational trend study","volume":"17","author":"S Yang","year":"2017","journal-title":"The Lancet Infectious Diseases"},{"issue":"1","key":"pone.0261629.ref005","doi-asserted-by":"crossref","first-page":"4","DOI":"10.7774\/cevr.2017.6.1.4","article-title":"Enterovirus 71 infection and vaccines","volume":"6","author":"EJ Yi","year":"2017","journal-title":"Clin Exp Vaccine Res"},{"key":"pone.0261629.ref006","unstructured":"The Data-center of China Public Health Science [cited 2020 December 1st]. http:\/\/www.phsciencedata.cn\/Share\/en\/index.jsp."},{"key":"pone.0261629.ref007","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.ijid.2018.07.003","article-title":"Epidemiology and ARIMA model of positive-rate of influenza viruses among children in Wuhan, China: A nine-year retrospective study","volume":"74","author":"Z He","year":"2018","journal-title":"Int J Infect Dis"},{"issue":"2","key":"pone.0261629.ref008","doi-asserted-by":"crossref","first-page":"364","DOI":"10.4269\/ajtmh.2012.11-0472","article-title":"Application of an Autoregressive Integrated Moving Average Model for Predicting the Incidence of Hemorrhagic Fever with Renal Syndrome","volume":"87","author":"Q Li","year":"2012","journal-title":"American Journal of Tropical Medicine and Hygiene"},{"issue":"1","key":"pone.0261629.ref009","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":"L Liu","year":"2016","journal-title":"Epidemiol Infect"},{"key":"pone.0261629.ref010","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1186\/1471-2458-14-358","article-title":"Using an autologistic regression model to identify spatial risk factors and spatial risk patterns of hand, foot and mouth disease (HFMD) in Mainland China","volume":"14","author":"YC Bo","year":"2014","journal-title":"BMC Public Health"},{"issue":"Suppl 5","key":"pone.0261629.ref011","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1186\/s12859-016-1043-4","article-title":"Integration of multi-omics data for prediction of phenotypic traits using random forest","volume":"17","author":"A Acharjee","year":"2016","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"pone.0261629.ref012","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1145\/2398356.2398381","article-title":"Real-time human pose recognition in parts from single depth images","volume":"56","author":"J Shotton","year":"2013","journal-title":"Commun ACM"},{"issue":"35","key":"pone.0261629.ref013","doi-asserted-by":"crossref","first-page":"11114","DOI":"10.1073\/pnas.1423542112","article-title":"Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data","volume":"112","author":"A Wesolowski","year":"2015","journal-title":"Proc Natl Acad Sci U S A"},{"key":"pone.0261629.ref014","doi-asserted-by":"crossref","first-page":"216752","DOI":"10.1109\/ACCESS.2020.3038995","article-title":"Quantifying the Time-Lag Effects of Human Mobility on the COVID-19 Transmission: A Multi-City Study in China","volume":"8","author":"W Xi","year":"2020","journal-title":"IEEE Access"},{"issue":"2","key":"pone.0261629.ref015","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1002\/jmv.25282","article-title":"Understanding the epidemiological characteristics of EV71 and CVA16 infection to aid the diagnosis and treatment of hand, foot, and mouth disease","volume":"91","author":"B Wang","year":"2019","journal-title":"J Med Virol"},{"issue":"12","key":"pone.0261629.ref016","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":"E Ma","year":"2010","journal-title":"Epidemiol Infect"},{"issue":"1","key":"pone.0261629.ref017","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1186\/s12879-016-1846-y","article-title":"Short-term effects of meteorological factors on pediatric hand, foot, and mouth disease in Guangdong, China: a multi-city time-series analysis","volume":"16","author":"C Guo","year":"2016","journal-title":"BMC Infect Dis"},{"key":"pone.0261629.ref018","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\u2013411","author":"D Onozuka","year":"2011","journal-title":"Sci Total Environ"},{"issue":"3","key":"pone.0261629.ref019","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1007\/s00484-016-1225-9","article-title":"Impact of weather factors on hand, foot and mouth disease, and its role in short-term incidence trend forecast in Huainan City, Anhui Province","volume":"61","author":"D Zhao","year":"2017","journal-title":"Int J Biometeorol"},{"issue":"6","key":"pone.0261629.ref020","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1139\/m75-121","article-title":"Virus survival on inanimate surfaces","volume":"21","author":"MC Mahl","year":"1975","journal-title":"Canadian journal of microbiology"},{"issue":"2","key":"pone.0261629.ref021","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1093\/aje\/kwp116","article-title":"Dynamics and control of infections transmitted from person to person through the environment","volume":"170","author":"S Li","year":"2009","journal-title":"Am J Epidemiol"},{"issue":"7179","key":"pone.0261629.ref022","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1038\/nature06509","article-title":"The dynamics of measles in sub-Saharan Africa","volume":"451","author":"MJ Ferrari","year":"2008","journal-title":"Nature"},{"issue":"1676","key":"pone.0261629.ref023","first-page":"4111","article-title":"Seasonality and comparative dynamics of six childhood infections in pre-vaccination Copenhagen","volume":"276","author":"CJ Metcalf","year":"2009","journal-title":"Proc Biol Sci"},{"issue":"10","key":"pone.0261629.ref024","doi-asserted-by":"crossref","first-page":"e0163789","DOI":"10.1371\/journal.pone.0163789","article-title":"Determinants of the Transmission Variation of Hand, Foot and Mouth Disease in China","volume":"11","author":"J Zhao","year":"2016","journal-title":"PLoS One"},{"key":"pone.0261629.ref025","unstructured":"China Meteorological Data Service Centre [cited 2020 December 1st]. http:\/\/data.cma.cn."},{"key":"pone.0261629.ref026","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.apgeog.2017.07.014","article-title":"Difference of urban development in China from the perspective of passenger transport around Spring Festival","volume":"87","author":"J Xu","year":"2017","journal-title":"Applied Geography"},{"key":"pone.0261629.ref027","unstructured":"2018 China Statistical Yearbook [cited 2021 March 10th]. http:\/\/www.stats.gov.cn\/tjsj\/ndsj\/2018\/indexch.htm."},{"issue":"1","key":"pone.0261629.ref028","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"L. Breiman","year":"2001","journal-title":"Machine Learning"},{"key":"pone.0261629.ref029","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; San Francisco, California, USA: Association for Computing Machinery; 2016. p. 785\u201394.","DOI":"10.1145\/2939672.2939785"},{"issue":"1","key":"pone.0261629.ref030","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1186\/s12879-018-3066-0","article-title":"Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines","volume":"18","author":"TM Carvajal","year":"2018","journal-title":"BMC Infect Dis"},{"issue":"7","key":"pone.0261629.ref031","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1109\/TPAMI.2002.1017616","article-title":"An efficient k-means clustering algorithm: Analysis and implementation","volume":"24","author":"T Kanungo","year":"2002","journal-title":"Ieee Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"5","key":"pone.0261629.ref032","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1109\/TFUZZ.2020.2966182","article-title":"Determine the Number of Unknown Targets in Open World Based on Elbow Method","volume":"29","author":"F Liu","year":"2021","journal-title":"Ieee Transactions on Fuzzy Systems"},{"key":"pone.0261629.ref033","doi-asserted-by":"crossref","first-page":"52713","DOI":"10.1109\/ACCESS.2020.2980634","article-title":"A Multi-Stage Machine Learning Approach to Predict Dengue Incidence: A Case Study in Mexico","volume":"8","author":"A Appice","year":"2020","journal-title":"IEEE Access"},{"key":"pone.0261629.ref034","doi-asserted-by":"crossref","first-page":"100372","DOI":"10.1016\/j.sste.2020.100372","article-title":"Large-scale multivariate forecasting models for Dengue\u2014LSTM versus random forest regression","volume":"35","author":"E Mussumeci","year":"2020","journal-title":"Spat Spatiotemporal Epidemiol"},{"issue":"6","key":"pone.0261629.ref035","doi-asserted-by":"crossref","first-page":"e0006587","DOI":"10.1371\/journal.pntd.0006587","article-title":"Mapping dengue risk in Singapore using Random Forest","volume":"12","author":"J Ong","year":"2018","journal-title":"PLoS Negl Trop Dis"},{"issue":"1","key":"pone.0261629.ref036","doi-asserted-by":"crossref","first-page":"17362","DOI":"10.1038\/s41598-017-17749-5","article-title":"Comparative epidemiology of poliovirus transmission","volume":"7","author":"N Noori","year":"2017","journal-title":"Sci Rep"},{"issue":"12","key":"pone.0261629.ref037","doi-asserted-by":"crossref","first-page":"e039676","DOI":"10.1136\/bmjopen-2020-039676","article-title":"Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study","volume":"10","author":"M Alim","year":"2020","journal-title":"BMJ Open"},{"key":"pone.0261629.ref038","doi-asserted-by":"crossref","DOI":"10.1016\/j.imu.2020.100508","article-title":"Prediction of malaria incidence using climate variability and machine learning","volume":"22","author":"O Nkiruka","year":"2021","journal-title":"Informatics in Medicine Unlocked"},{"key":"pone.0261629.ref039","doi-asserted-by":"crossref","first-page":"138860","DOI":"10.1016\/j.scitotenv.2020.138860","article-title":"Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020","volume":"728","author":"S Gupta","year":"2020","journal-title":"Sci Total Environ"},{"key":"pone.0261629.ref040","doi-asserted-by":"crossref","first-page":"110137","DOI":"10.1016\/j.chaos.2020.110137","article-title":"Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches","volume":"138","author":"Z Malki","year":"2020","journal-title":"Chaos Solitons Fractals"},{"issue":"1","key":"pone.0261629.ref041","doi-asserted-by":"crossref","first-page":"17928","DOI":"10.1038\/s41598-019-54495-2","article-title":"A method for hand-foot-mouth disease prediction using GeoDetector and LSTM model in Guangxi, China","volume":"9","author":"J Gu","year":"2019","journal-title":"Sci Rep"},{"key":"pone.0261629.ref042","first-page":"1","article-title":"Meteorological factors and its association with hand, foot and mouth disease in Southeast and East Asia areas: a meta-analysis","author":"C Duan","year":"2018","journal-title":"Epidemiol Infect"},{"issue":"1","key":"pone.0261629.ref043","doi-asserted-by":"crossref","first-page":"e87916","DOI":"10.1371\/journal.pone.0087916","article-title":"Time series analysis of hand-foot-mouth disease hospitalization in Zhengzhou: establishment of forecasting models using climate variables as predictors","volume":"9","author":"H Feng","year":"2014","journal-title":"PLoS One"},{"issue":"10","key":"pone.0261629.ref044","doi-asserted-by":"crossref","first-page":"e016263","DOI":"10.1136\/bmjopen-2017-016263","article-title":"Predicting the hand, foot, and mouth disease incidence using search engine query data and climate variables: an ecological study in Guangdong, China","volume":"7","author":"Z Du","year":"2017","journal-title":"BMJ Open"},{"issue":"6","key":"pone.0261629.ref045","doi-asserted-by":"crossref","first-page":"e38978","DOI":"10.1371\/journal.pone.0038978","article-title":"Determinants of the incidence of hand, foot and mouth disease in China using geographically weighted regression models","volume":"7","author":"M Hu","year":"2012","journal-title":"PLoS One"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0261629","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,22]],"date-time":"2021-12-22T18:25:29Z","timestamp":1640197529000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0261629"}},"subtitle":[],"editor":[{"given":"Randeep","family":"Singh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2021,12,22]]},"references-count":45,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,12,22]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0261629","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,22]]}}}