{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T13:12:37Z","timestamp":1789737157687,"version":"4.0.1"},"reference-count":54,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T00:00:00Z","timestamp":1767139200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007957","name":"Chongqing Municipal Education Commission","doi-asserted-by":"publisher","award":["KJQN202202812; KJQN202402802"],"award-info":[{"award-number":["KJQN202202812; KJQN202402802"]}],"id":[{"id":"10.13039\/501100007957","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chongqing Shapingba District Science & Technology Bureau","award":["202325"],"award-info":[{"award-number":["202325"]}]},{"name":"Chongqing Medical and Pharmaceutical College","award":["ygz2023111"],"award-info":[{"award-number":["ygz2023111"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Pulmonary Tuberculosis (PTB) remains a serious infectious disease and a major global public health problem. Accurate prediction of PTB epidemics is essential to support health authorities in developing effective prevention and control strategies. This study proposed a novel two-stage hybrid prediction model that integrates a seasonal autoregressive integrated moving average (SARIMA) model and a support vector regression (SVR) model in parallel, followed in series by an extreme learning machine (ELM) optimized via the sparrow search algorithm. Furthermore, recognizing the notable spatial correlation characteristic of airborne PTB transmission, this study incorporates PTB incidence data from surrounding regions of the target area as additional input features to enhance the model with supplementary spatial information, thereby improving prediction accuracy. Validation using real-world PTB incidence data from Chongqing, China, demonstrates the superior performance of the proposed model, which reduces prediction errors by 18.47% to 77.38% compared to existing hybrid models. The inclusion of adjacent regional incidence data further significantly enhances predictive accuracy, reducing errors by 20.92% to 68.74%. The outcomes of this study are expected to facilitate earlier insights into PTB incidence trends and provide valuable support for public health decision-making in PTB prevention and control.<\/jats:p>","DOI":"10.1371\/journal.pone.0339453","type":"journal-article","created":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T18:26:31Z","timestamp":1767205591000},"page":"e0339453","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":2,"title":["A hybrid machine learning model for pulmonary tuberculosis forecasting of Chongqing with adjacent-region data"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-5801-2482","authenticated-orcid":true,"given":"Yilin","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongbo","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuangxueer","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5899-8673","authenticated-orcid":true,"given":"Xiaoying","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3066-3211","authenticated-orcid":true,"given":"Junjie","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,12,31]]},"reference":[{"key":"pone.0339453.ref001","volume-title":"Global tuberculosis report 2024","author":"World Health Organization","year":"2024"},{"issue":"9432","key":"pone.0339453.ref002","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/S0140-6736(04)16764-0","article-title":"The effect of tuberculosis control in China","volume":"364","author":"China Tuberculosis Control Collaboration","year":"2004","journal-title":"Lancet"},{"issue":"9934","key":"pone.0339453.ref003","doi-asserted-by":"crossref","first-page":"2057","DOI":"10.1016\/S0140-6736(13)62639-2","article-title":"Tuberculosis prevalence in China, 1990-2010; a longitudinal analysis of national survey data","volume":"383","author":"L Wang","year":"2014","journal-title":"Lancet"},{"key":"pone.0339453.ref004","unstructured":"China M. National TB Control Program (NTP) Guidelines in China. 2008."},{"issue":"1","key":"pone.0339453.ref005","doi-asserted-by":"crossref","first-page":"7425","DOI":"10.1038\/s41598-020-64387-5","article-title":"Identification and prediction of tuberculosis in Eastern China: analyses from 10-year population-based notification data in Zhejiang Province, China","volume":"10","author":"K Liu","year":"2020","journal-title":"Sci Rep"},{"key":"pone.0339453.ref006","first-page":"1","article-title":"Statistical methods for predicting tuberculosis incidence based on data from Guangxi, China","volume":"20","author":"Y Zheng","year":"2020","journal-title":"BMC Infect Dis"},{"issue":"5","key":"pone.0339453.ref007","doi-asserted-by":"crossref","DOI":"10.5812\/ircmj.11779","article-title":"Forecasting tuberculosis incidence in iran using box-jenkins models","volume":"16","author":"M Moosazadeh","year":"2014","journal-title":"Iran Red Crescent Med J"},{"issue":"5","key":"pone.0339453.ref008","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":"Q Mao","year":"2018","journal-title":"J Infect Public Health"},{"issue":"3","key":"pone.0339453.ref009","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0211402","article-title":"Financial time series forecasting using twin support vector regression","volume":"14","author":"D Gupta","year":"2019","journal-title":"PLoS One"},{"issue":"1","key":"pone.0339453.ref010","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0277314","article-title":"A comparative study of three models to analyze the impact of air pollutants on the number of pulmonary tuberculosis cases in Urumqi, Xinjiang","volume":"18","author":"Y Wang","year":"2023","journal-title":"PLoS One"},{"issue":"5","key":"pone.0339453.ref011","doi-asserted-by":"crossref","first-page":"3910","DOI":"10.3390\/ijerph20053910","article-title":"Machine learning prediction model of tuberculosis incidence based on meteorological factors and air pollutants","volume":"20","author":"N Tang","year":"2023","journal-title":"Int J Environ Res Public Health"},{"issue":"1","key":"pone.0339453.ref012","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1186\/s40249-020-00771-7","article-title":"Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China","volume":"9","author":"Z-Q Li","year":"2020","journal-title":"Infect Dis Poverty"},{"issue":"1","key":"pone.0339453.ref013","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1186\/s12879-023-08609-x","article-title":"A hybrid model for tuberculosis forecasting based on empirical mode decomposition in China","volume":"23","author":"R Zhao","year":"2023","journal-title":"BMC Infect Dis"},{"issue":"9","key":"pone.0339453.ref014","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0310084","article-title":"A hybrid Transformer-LSTM model apply to glucose prediction","volume":"19","author":"Q Bian","year":"2024","journal-title":"PLoS One"},{"issue":"5","key":"pone.0339453.ref015","article-title":"A hybrid TCN-XGBoost model for agricultural product market price forecasting","volume":"20","author":"T Zhao","year":"2025","journal-title":"PLoS One"},{"issue":"11","key":"pone.0339453.ref016","article-title":"Application of a hybrid model for predicting the incidence of tuberculosis in Hubei, China","volume":"8","author":"G Zhang","year":"2013","journal-title":"PLoS One"},{"key":"pone.0339453.ref017","doi-asserted-by":"crossref","first-page":"2311","DOI":"10.2147\/IDR.S207809","article-title":"Forecasting the seasonality and trend of pulmonary tuberculosis in Jiangsu Province of China using advanced statistical time-series analyses","volume":"12","author":"Q Liu","year":"2019","journal-title":"Infect Drug Resist"},{"key":"pone.0339453.ref018","article-title":"Applying SARIMA, ETS, and hybrid models for prediction of tuberculosis incidence rate in Taiwan","volume":"10","author":"M-M Kuan","year":"2022","journal-title":"PeerJ"},{"key":"pone.0339453.ref019","doi-asserted-by":"crossref","first-page":"4383","DOI":"10.1007\/s10586-018-1906-8","article-title":"Prediction model of pulmonary tuberculosis based on gray kernel AR-SVM model","volume":"22","author":"W Jue","year":"2018","journal-title":"Cluster Comput"},{"issue":"1","key":"pone.0339453.ref020","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1186\/s12879-022-07462-8","article-title":"A multivariate multi-step LSTM forecasting model for tuberculosis incidence with model explanation in Liaoning Province, China","volume":"22","author":"E Yang","year":"2022","journal-title":"BMC Infect Dis"},{"key":"pone.0339453.ref021","doi-asserted-by":"crossref","first-page":"1941","DOI":"10.2147\/IDR.S299704","article-title":"Forecasting the tuberculosis incidence using a novel ensemble empirical mode decomposition-based data-driven hybrid model in Tibet, China","author":"J Li","year":"2021","journal-title":"Infect Drug Resist"},{"issue":"15","key":"pone.0339453.ref022","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.ifacol.2021.10.293","article-title":"Robust Kalman filter for tuberculosis incidence time series forecasting","volume":"54","author":"AL Jutinico","year":"2021","journal-title":"IFAC-PapersOnLine"},{"issue":"4","key":"pone.0339453.ref023","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1177\/0954411920987964","article-title":"An optimal brain tumor detection by convolutional neural network and Enhanced Sparrow Search Algorithm","volume":"235","author":"T Liu","year":"2021","journal-title":"Proc Inst Mech Eng H"},{"key":"pone.0339453.ref024","doi-asserted-by":"crossref","first-page":"106674","DOI":"10.1016\/j.compbiomed.2023.106674","article-title":"A novel prediction model based on decomposition-integration and error correction for COVID-19 daily confirmed and death cases","volume":"156","author":"H Yang","year":"2023","journal-title":"Comput Biol Med"},{"key":"pone.0339453.ref025","doi-asserted-by":"crossref","first-page":"129460","DOI":"10.1016\/j.jhydrol.2023.129460","article-title":"An enhanced monthly runoff time series prediction using extreme learning machine optimized by salp swarm algorithm based on time varying filtering based empirical mode decomposition","volume":"620","author":"W Wang","year":"2023","journal-title":"J Hydrol"},{"issue":"3","key":"pone.0339453.ref026","doi-asserted-by":"crossref","first-page":"943","DOI":"10.2166\/hydro.2023.172","article-title":"Improved monthly runoff time series prediction using the SOA\u2013SVM model based on ICEEMDAN\u2013WD decomposition","volume":"25","author":"D Xu","year":"2023","journal-title":"J Hydroinform"},{"issue":"1","key":"pone.0339453.ref027","first-page":"22","article-title":"A novel swarm intelligence optimization approach: sparrow search algorithm","volume":"8","author":"J Xue","year":"2020","journal-title":"SSCE"},{"issue":"10","key":"pone.0339453.ref028","first-page":"2519","article-title":"A novel tuberculosis prediction model by extracting radiological features present in chest X-ray images using modified discrete grey wolf optimizer based segmentation","volume":"11","author":"J Senthil Kumar","year":"2021","journal-title":"J Med Imaging Health Inform"},{"issue":"3","key":"pone.0339453.ref029","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1007\/s10916-009-9369-3","article-title":"Tuberculosis disease diagnosis using artificial neural network trained with genetic algorithm","volume":"35","author":"E Elveren","year":"2011","journal-title":"J Med Syst"},{"issue":"9","key":"pone.0339453.ref030","doi-asserted-by":"crossref","first-page":"5385","DOI":"10.3390\/ijerph19095385","article-title":"Modeling and predicting pulmonary tuberculosis incidence and its association with air pollution and meteorological factors using an ARIMAX model: an ecological study in Ningbo of China","volume":"19","author":"Y-P Chen","year":"2022","journal-title":"Int J Environ Res Public Health"},{"issue":"4","key":"pone.0339453.ref031","doi-asserted-by":"crossref","first-page":"442","DOI":"10.4103\/ijmy.ijmy_182_21","article-title":"Prediction of tuberculosis cases based on sociodemographic and environmental factors in Gombak, Selangor, Malaysia: a comparative assessment of multiple linear regression and artificial neural network models","volume":"10","author":"NA Mohidem","year":"2021","journal-title":"Int J Mycobacteriol"},{"issue":"11","key":"pone.0339453.ref032","article-title":"Spatial dependency of tuberculosis incidence in Taiwan","volume":"7","author":"I-C Ng","year":"2012","journal-title":"PLoS One"},{"key":"pone.0339453.ref033","unstructured":"Chongqing Center for Disease Control. Chongqing tuberculosis report; 2023. Available from: https:\/\/www.cqcdc.org\/"},{"key":"pone.0339453.ref034","unstructured":"Public Health Science Data Center. National tuberculosis report; 2020. Available from: https:\/\/www.phsciencedata.cn\/Share\/"},{"issue":"9","key":"pone.0339453.ref035","doi-asserted-by":"crossref","first-page":"2762","DOI":"10.3390\/s25092762","article-title":"Impact of temporal resolution on autocorrelative features of cerebral physiology from invasive and non-invasive sensors in acute traumatic neural injury: insights from the CAHR-TBI cohort","volume":"25","author":"N Vakitbilir","year":"2025","journal-title":"Sensors (Basel)"},{"issue":"1","key":"pone.0339453.ref036","first-page":"3619063","article-title":"Predictive study of tuberculosis incidence by ARMA model combined with air pollution variables","volume":"2020","author":"Y Zheng","year":"2020","journal-title":"Complexity"},{"issue":"1","key":"pone.0339453.ref037","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1186\/s12889-023-14994-4","article-title":"Application of SARIMA model in forecasting and analyzing inpatient cases of acute mountain sickness","volume":"23","author":"J Liu","year":"2023","journal-title":"BMC Public Health"},{"key":"pone.0339453.ref038","doi-asserted-by":"crossref","unstructured":"Zhan Y, Zhang H, Liu Y. Forecast of meteorological and hydrological features based on SVR model. 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE); 2021. p. 579\u201383.","DOI":"10.1109\/AEMCSE51986.2021.00123"},{"issue":"7","key":"pone.0339453.ref039","article-title":"New approaches for calculating Moran\u2019s index of spatial autocorrelation","volume":"8","author":"Y Chen","year":"2013","journal-title":"PLoS One"},{"key":"pone.0339453.ref040","unstructured":"Python Software Foundation. Python language reference, version 3.9.10. Available from: http:\/\/www.python.org\/"},{"key":"pone.0339453.ref041","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.puhe.2021.09.025","article-title":"Predicting the trend of infectious diseases using grey self-memory system model: a case study of the incidence of tuberculosis","volume":"201","author":"X Guo","year":"2021","journal-title":"Public Health"},{"issue":"2","key":"pone.0339453.ref042","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0262734","article-title":"The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China","volume":"17","author":"D Zhao","year":"2022","journal-title":"PLoS One"},{"key":"pone.0339453.ref043","doi-asserted-by":"crossref","first-page":"3503","DOI":"10.2147\/IDR.S367528","article-title":"Application of the ARIMA model in forecasting the incidence of tuberculosis in Anhui during COVID-19 pandemic from 2021 to 2022","volume":"15","author":"S Chen","year":"2022","journal-title":"Infect Drug Resist"},{"issue":"9","key":"pone.0339453.ref044","doi-asserted-by":"crossref","first-page":"4690","DOI":"10.3390\/app15094690","article-title":"Effective epileptic seizure detection with hybrid feature selection and SMOTE-based data balancing using SVM classifier","volume":"15","author":"HF Atlam","year":"2025","journal-title":"Appl Sci"},{"issue":"12","key":"pone.0339453.ref045","doi-asserted-by":"crossref","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"},{"issue":"8","key":"pone.0339453.ref046","doi-asserted-by":"crossref","first-page":"757","DOI":"10.3390\/ijerph13080757","article-title":"Seasonality and trend forecasting of tuberculosis prevalence data in Eastern Cape, South Africa, using a hybrid model","volume":"13","author":"A Azeez","year":"2016","journal-title":"Int J Environ Res Public Health"},{"issue":"9","key":"pone.0339453.ref047","doi-asserted-by":"crossref","first-page":"4957","DOI":"10.3390\/app15094957","article-title":"Applicability of machine learning and mathematical equations to the prediction of total organic carbon in Cambrian Shale, Sichuan Basin, China","volume":"15","author":"M Zheng","year":"2025","journal-title":"Appl Sci"},{"key":"pone.0339453.ref048","doi-asserted-by":"crossref","first-page":"115975","DOI":"10.1016\/j.apenergy.2020.115975","article-title":"Wind speed big data forecasting using time-variant multi-resolution ensemble model with clustering auto-encoder","volume":"280","author":"H Liu","year":"2020","journal-title":"Appl Energy"},{"key":"pone.0339453.ref049","volume-title":"The \u201c13th Five-Year\u201d National TB Control Plan","author":"State Council of the People\u2019s Republic of China","year":"2017"},{"key":"pone.0339453.ref050","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The elements of statistical learning: data mining, inference, and prediction","author":"T Hastie","year":"2009","edition":"2"},{"key":"pone.0339453.ref051","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: theory and applications","volume":"70","author":"G-B Huang","year":"2006","journal-title":"Neurocomputing"},{"issue":"3","key":"pone.0339453.ref052","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A tutorial on support vector regression","volume":"14","author":"AJ Smola","year":"2004","journal-title":"Stat Comput"},{"key":"pone.0339453.ref053","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-0716-1418-1","volume-title":"An introduction to statistical learning with applications in R","author":"G James","year":"2021","edition":"2"},{"issue":"5","key":"pone.0339453.ref054","doi-asserted-by":"crossref","first-page":"102","DOI":"10.53759\/7669\/jmc202505008","article-title":"HDLHDGAI: hybridization of deep learning model for heart disease prediction using generative artificial intelligence","volume":"5","author":"SL Choudhary","year":"2025","journal-title":"J Mach Comput"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0339453","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T18:26:40Z","timestamp":1767205600000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0339453"}},"subtitle":[],"editor":[{"given":"Salim","family":"Heddam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,12,31]]},"references-count":54,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12,31]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0339453","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,31]]}}}