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For: Guan P, Huang DS, Zhou BS. Forecasting model for the incidence of hepatitis A based on artificial neural network. World J Gastroenterol 2004; 10(24): 3579-3582 [PMID: 15534910 DOI: 10.3748/wjg.v10.i24.3579]
URL: https://www.wjgnet.com/1007-9327/full/v10/i24/3579.htm
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Sandeep Samantaray, Potnuru Sumaan, Pravin Surin, Nihar Ranjan Mohanta, Abinash Sahoo. Proceedings of International Conference on Data Science and ApplicationsLecture Notes in Networks and Systems 2022; 288: 273 doi: 10.1007/978-981-16-5120-5_21
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Jun Xu, Jia Guo, Hai-qiang Yang, Qing-lian Ji, Rui-jie Song, Feng Hou, Hao-yu Liang, Shun-li Liu, Lan-tian Tian, He-xiang Wang. Preoperative contrast-enhanced CT-based radiomics nomogram for differentiating benign and malignant primary retroperitoneal tumorsEuropean Radiology 2023; 33(10): 6781 doi: 10.1007/s00330-023-09686-x
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Binish Fatimah, Priya Aggarwal, Pushpendra Singh, Anubha Gupta. A comparative study for predictive monitoring of COVID-19 pandemicApplied Soft Computing 2022; 122: 108806 doi: 10.1016/j.asoc.2022.108806
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Wei Liu, Xue Liu, Mei Peng, Gong-Quan Chen, Peng-Hua Liu, Xin-Wu Cui, Fan Jiang, Christoph F Dietrich. Artificial intelligence for hepatitis evaluationWorld Journal of Gastroenterology 2021; 27(34): 5715-5726 doi: 10.3748/wjg.v27.i34.5715
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L. Yang, Z.‐W. Bi, Z.‐Q. Kou, X.‐J. Li, M. Zhang, M. Wang, L.‐Y. Zhang, Z.‐T. Zhao. Time‐Series Analysis on Human Brucellosis During 2004–2013 in Shandong Province, ChinaZoonoses and Public Health 2015; 62(3): 228 doi: 10.1111/zph.12145
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Derya Avci. An Automatic Diagnosis System for Hepatitis Diseases Based on Genetic Wavelet Kernel Extreme Learning MachineJournal of Electrical Engineering and Technology 2016; 11(4): 993 doi: 10.5370/JEET.2016.11.4.993
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Pınar Cihan. Forecasting fully vaccinated people against COVID-19 and examining future vaccination rate for herd immunity in the US, Asia, Europe, Africa, South America, and the WorldApplied Soft Computing 2021; 111: 107708 doi: 10.1016/j.asoc.2021.107708
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Yuh-Feng Wang, Tsung-Ming Hu, Chia-Chao Wu, Fu-Chiu Yu, Chao-Ming Fu, Shih-Hua Lin, Wei-Hsin Huang, Jainn-Shiun Chiu. Prediction of target range of intact parathyroid hormone in hemodialysis patients with artificial neural networkComputer Methods and Programs in Biomedicine 2006; 83(2): 111 doi: 10.1016/j.cmpb.2006.06.001
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Wei Wu, Junqiao Guo, Shuyi An, Peng Guan, Yangwu Ren, Linzi Xia, Baosen Zhou, Hiroshi Nishiura. Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, ChinaPLOS ONE 2015; 10(8): e0135492 doi: 10.1371/journal.pone.0135492
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Cihan ÇILGIN, Mehmet Ozan ÖZDEMİR. TIME SERIES FORECASTING OF COVID-19 CONFIRMED CASES IN TURKEY WITH STACKING ENSEMBLE MODELSBingöl Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 2023; (26): 504 doi: 10.29029/busbed.1299248
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Rupinder Katoch, Arpit Sidhu. An Application of ARIMA Model to Forecast the Dynamics of COVID-19 Epidemic in IndiaGlobal Business Review 2021; : 097215092098865 doi: 10.1177/0972150920988653
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Qiyong Liu, Xiaodong Liu, Baofa Jiang, Weizhong Yang. Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA modelBMC Infectious Diseases 2011; 11(1) doi: 10.1186/1471-2334-11-218
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Da Hye Lee, Youn Su Kim, Young Youp Koh, Kwang Yoon Song, In Hong Chang. Forecasting COVID-19 Confirmed Cases Using Empirical Data Analysis in KoreaHealthcare 2021; 9(3): 254 doi: 10.3390/healthcare9030254
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Lingling Zhou, Jing Xia, Lijing Yu, Ying Wang, Yun Shi, Shunxiang Cai, Shaofa Nie. Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in HumansInternational Journal of Environmental Research and Public Health 2016; 13(4): 355 doi: 10.3390/ijerph13040355
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Philipp A. Dirkx, Thomas L.A. Heil. Investment factor timing: Harvesting the low-risk anomaly using artificial neural networksExpert Systems with Applications 2022; 189: 116093 doi: 10.1016/j.eswa.2021.116093
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J. Behnamian, S.M.T. Fatemi Ghomi. Development of a PSO–SA hybrid metaheuristic for a new comprehensive regression model to time-series forecastingExpert Systems with Applications 2010; 37(2): 974 doi: 10.1016/j.eswa.2009.05.079
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Zeynep Ceylan. Estimation of COVID-19 prevalence in Italy, Spain, and FranceScience of The Total Environment 2020; 729: 138817 doi: 10.1016/j.scitotenv.2020.138817
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Lijing Yu, Lingling Zhou, Li Tan, Hongbo Jiang, Ying Wang, Sheng Wei, Shaofa Nie, Lijun Rong. Application of a New Hybrid Model with Seasonal Auto-Regressive Integrated Moving Average (ARIMA) and Nonlinear Auto-Regressive Neural Network (NARNN) in Forecasting Incidence Cases of HFMD in Shenzhen, ChinaPLoS ONE 2014; 9(6): e98241 doi: 10.1371/journal.pone.0098241
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L. LIU, R. S. LUAN, F. YIN, X. P. ZHU, Q. LÜ. Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA modelEpidemiology and Infection 2016; 144(1): 144 doi: 10.1017/S0950268815001144
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Maher Ala’raj, Munir Majdalawieh, Nishara Nizamuddin. Modeling and forecasting of COVID-19 using a hybrid dynamic model based on SEIRD with ARIMA correctionsInfectious Disease Modelling 2021; 6: 98 doi: 10.1016/j.idm.2020.11.007
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Akif DOĞANTEKİN, Cafer BAL. Diyabet Hastalıkları İçin GA-DÇF-UÖM Tabanlı Uzman Tanı SistemiFırat Üniversitesi Mühendislik Bilimleri Dergisi 2022; 34(1): 473 doi: 10.35234/fumbd.1031302
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Michael L. Jackson. Confounding by Season in Ecologic Studies of Seasonal Exposures and Outcomes: Examples From Estimates of Mortality Due to InfluenzaAnnals of Epidemiology 2009; 19(10): 681 doi: 10.1016/j.annepidem.2009.06.009
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Amir Talaei-Khoei, James M Wilson, Seyed-Farzan Kazemi. Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics ExperimentJMIR Public Health and Surveillance 2019; 5(1): e11357 doi: 10.2196/11357
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Lei Duan, Changjie Tang, Xiaosong Li, Guozhu Dong, Xianming Wang, Jie Zuo, Min Jiang, Zhongqi Li, Yongqing Zhang. Mining effective multi-segment sliding window for pathogen incidence rate predictionData & Knowledge Engineering 2013; 87: 425 doi: 10.1016/j.datak.2013.05.006
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Yan-Ling Zheng, Li-Ping Zhang, Xue-Liang Zhang, Kai Wang, Yu-Jian Zheng, Zhefeng Meng. Forecast Model Analysis for the Morbidity of Tuberculosis in Xinjiang, ChinaPLOS ONE 2015; 10(3): e0116832 doi: 10.1371/journal.pone.0116832
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A. SUMI, T. LUO, D. ZHOU, B. YU, D. KONG, N. KOBAYASHI. Time-series analysis of hepatitis A, B, C and E infections in a large Chinese city: application to prediction analysisEpidemiology and Infection 2013; 141(5): 905 doi: 10.1017/S095026881200146X
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Lingling Zhou, Lijing Yu, Ying Wang, Zhouqin Lu, Lihong Tian, Li Tan, Yun Shi, Shaofa Nie, Li Liu, David Joseph Diemert. A Hybrid Model for Predicting the Prevalence of Schistosomiasis in Humans of Qianjiang City, ChinaPLoS ONE 2014; 9(8): e104875 doi: 10.1371/journal.pone.0104875
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Jainn-Shiun Chiu, Yuh-Feng Wang, Yu-Cheih Su, Ling-Huei Wei, Jian-Guo Liao, Yu-Chuan Li. Artificial Neural Network to Predict Skeletal Metastasis in Patients with Prostate CancerJournal of Medical Systems 2009; 33(2): 91 doi: 10.1007/s10916-008-9168-2
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Pratima Kumari, Durga Toshniwal. Real-time estimation of COVID-19 cases using machine learning and mathematical models - The case of India2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) 2020; : 369 doi: 10.1109/ICIIS51140.2020.9342735
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Adewale F. Lukman, Rauf I. Rauf, Oluwakemi Abiodun, Olajumoke Oludoun, Kayode Ayinde, Roseline O. Ogundokun. COVID-19 prevalence estimation: Four most affected African countriesInfectious Disease Modelling 2020; 5: 827 doi: 10.1016/j.idm.2020.10.002
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Zhaohui Xia, Lei Qin, Zhen Ning, Xingyu Zhang, Jie Zhang. Deep learning time series prediction models in surveillance data of hepatitis incidence in ChinaPLOS ONE 2022; 17(4): e0265660 doi: 10.1371/journal.pone.0265660
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Lei Duan, Changjie Tang, Chi Gou, Min Jiang, Jie Zuo. Advanced Data Mining and ApplicationsLecture Notes in Computer Science 2011; 7121: 152 doi: 10.1007/978-3-642-25856-5_12
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Xingyu Zhang, Yuanyuan Liu, Min Yang, Tao Zhang, Alistair A. Young, Xiaosong Li, Alessandro Vespignani. Comparative Study of Four Time Series Methods in Forecasting Typhoid Fever Incidence in ChinaPLoS ONE 2013; 8(5): e63116 doi: 10.1371/journal.pone.0063116
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Mevlut Ture, Imran Kurt. Comparison of four different time series methods to forecast hepatitis A virus infectionExpert Systems with Applications 2006; 31(1): 41 doi: 10.1016/j.eswa.2005.09.002
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Ruijing Gan, Ni Chen, Daizheng Huang. Comparisons of forecasting for hepatitis in Guangxi Province, China by using three neural networks modelsPeerJ 2016; 4: e2684 doi: 10.7717/peerj.2684
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Zeming Li, Yanning Li. A comparative study on the prediction of the BP artificial neural network model and the ARIMA model in the incidence of AIDSBMC Medical Informatics and Decision Making 2020; 20(1) doi: 10.1186/s12911-020-01157-3
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Zhong-Qi Li, Hong-Qiu Pan, Qiao Liu, Huan Song, Jian-Ming Wang. Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern ChinaInfectious Diseases of Poverty 2020; 9(1) doi: 10.1186/s40249-020-00771-7
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Eldon Y. Li, Chen-Yuan Tung, Shu-Hsun Chang. The wisdom of crowds in action: Forecasting epidemic diseases with a web-based prediction market systemInternational Journal of Medical Informatics 2016; 92: 35 doi: 10.1016/j.ijmedinf.2016.04.014
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Philipe Riskalla Leal, Ricardo José de Paula Souza e Guimarães, Milton Kampel. Associations Between Environmental and Sociodemographic Data and Hepatitis‐A Transmission in Pará State (Brazil)GeoHealth 2021; 5(5) doi: 10.1029/2020GH000327
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P. D. Sreekanth, P. D. Sreedevi, Shakeel Ahmed, N. Geethanjali. Comparison of FFNN and ANFIS models for estimating groundwater levelEnvironmental Earth Sciences 2011; 62(6): 1301 doi: 10.1007/s12665-010-0617-0
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Hadi Bagheri, Leili Tapak, Manoochehr Karami, Zahra Hosseinkhani, Hamidreza Najari, Safdar Karimi, Zahra Cheraghi, Esteban Tlelo-Cuautle. Forecasting the monthly incidence rate of brucellosis in west of Iran using time series and data mining from 2010 to 2019PLOS ONE 2020; 15(5): e0232910 doi: 10.1371/journal.pone.0232910