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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

<p>Forecasting the seasonality and trend of pulmonary tuberculosis in Jiangsu Province of China using advanced statistical time-series analyses</p>
Qiao Liu, Zhongqi Li, Ye Ji, et al.
Infection and Drug Resistance (2019) Vol. Volume 12, pp. 2311-2322
Open Access | Times Cited: 76

Showing 1-25 of 76 citing articles:

Estimation of COVID-19 prevalence in Italy, Spain, and France
Zeynep Ceylan
The Science of The Total Environment (2020) Vol. 729, pp. 138817-138817
Open Access | Times Cited: 572
Prediction of COVID-19 corona virus pandemic based on time series data using support vector machine
Vijander Singh, Ramesh Chandra Poonia, Sandeep Kumar, et al.
Journal of Discrete Mathematical Sciences and Cryptography (2020) Vol. 23, Iss. 8, pp. 1583-1597
Closed Access | Times Cited: 193
Modeling and forecasting of COVID-19 using a hybrid dynamic model based on SEIRD with ARIMA corrections
Maher Alaraj, Munir Majdalawieh, Nishara Nizamuddin
Infectious Disease Modelling (2020) Vol. 6, pp. 98-111
Open Access | Times Cited: 74
Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study
Mirxat Alim, Guohua Ye, Peng Guan, et al.
BMJ Open (2020) Vol. 10, Iss. 12, pp. e039676-e039676
Open Access | Times Cited: 74
Forecasting the Spreading of COVID-19 across Nine Countries from Europe, Asia, and the American Continents Using the ARIMA Models
Ovidiu-Dumitru Ilie, Roxana-Oana Cojocariu, Alin Ciobîcă, et al.
Microorganisms (2020) Vol. 8, Iss. 8, pp. 1158-1158
Open Access | Times Cited: 49
Prediction of COVID-19 pandemic measuring criteria using support vector machine, prophet and linear regression models in Indian scenario
Amit Kumar Gupta‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Vijander Singh, Priya Mathur, et al.
Journal of Interdisciplinary Mathematics (2020) Vol. 24, Iss. 1, pp. 89-108
Open Access | Times Cited: 46
Meteorological factors contribute to the risk of pulmonary tuberculosis: A multicenter study in eastern China
Zhongqi Li, Qiao Liu, Mengyao Zhan, et al.
The Science of The Total Environment (2021) Vol. 793, pp. 148621-148621
Open Access | Times Cited: 38
ARIMA Model Estimation Based on Genetic Algorithm for COVID-19 Mortality Rates
Mohanad A. Deif, Ahmed Solyman, Rania E. Hammam
International Journal of Information Technology & Decision Making (2021) Vol. 20, Iss. 06, pp. 1775-1798
Closed Access | Times Cited: 36
Time series prediction for the epidemic trends of monkeypox using the ARIMA, exponential smoothing, GM (1, 1) and LSTM deep learning methods
Wudi Wei, Gang Wang, Xing Tao, et al.
Journal of General Virology (2023) Vol. 104, Iss. 4
Closed Access | Times Cited: 16
Green hydrogen production ensemble forecasting based on hybrid dynamic optimization algorithm
Amel Ali Alhussan, El-Sayed M. El-kenawy, Mohammed A. Saeed, et al.
Frontiers in Energy Research (2023) Vol. 11
Open Access | Times Cited: 16
Machine learning-based diffusion model for prediction of coronavirus-19 outbreak
Supriya Raheja, Shreya Kasturia, Xiaochun Cheng, et al.
Neural Computing and Applications (2021) Vol. 35, Iss. 19, pp. 13755-13774
Open Access | Times Cited: 31
Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China
Zhongqi Li, Hongqiu Pan, Qiao Liu, et al.
Infectious Diseases of Poverty (2020) Vol. 9, Iss. 1
Open Access | Times Cited: 32
<p>An Advanced Data-Driven Hybrid Model of SARIMA-NNNAR for Tuberculosis Incidence Time Series Forecasting in Qinghai Province, China</p>
Yongbin Wang, Chunjie Xu, Yuchun Li, et al.
Infection and Drug Resistance (2020) Vol. Volume 13, pp. 867-880
Open Access | Times Cited: 29
Identification and Prediction of Tuberculosis in Eastern China: Analyses from 10-year Population-based Notification Data in Zhejiang Province, China
Kui Liu, Tao Li, Avina Vongpradith, et al.
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 29
Forecasting COVID-19 pandemic in Alberta, Canada using modified ARIMA models
Jian Sun
Computer Methods and Programs in Biomedicine Update (2021) Vol. 1, pp. 100029-100029
Open Access | Times Cited: 26
Estimating the Effects of the COVID-19 Outbreak on the Reductions in Tuberculosis Cases and the Epidemiological Trends in China: A Causal Impact Analysis
Wenhao Ding, Yanyan Li, Yichun Bai, et al.
Infection and Drug Resistance (2021) Vol. Volume 14, pp. 4641-4655
Open Access | Times Cited: 25
The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China
Daren Zhao, Huiwu Zhang, Qing Cao, et al.
PLoS ONE (2022) Vol. 17, Iss. 2, pp. e0262734-e0262734
Open Access | Times Cited: 18
Development and comparison of predictive models for sexually transmitted diseases—AIDS, gonorrhea, and syphilis in China, 2011–2021
Zhixin Zhu, Xiaoxia Zhu, Yancen Zhan, et al.
Frontiers in Public Health (2022) Vol. 10
Open Access | Times Cited: 18
Predicting spatio-temporal dynamics of dengue using INLA (integrated nested laplace approximation) in Yogyakarta, Indonesia
Marko Ferdian Salim, Tri Baskoro Tunggul Satoto, Danardono Danardono
BMC Public Health (2025) Vol. 25, Iss. 1
Open Access
Forecasting Global Tuberculosis Incidence Rates Using a Multi‐Variate Input, Multi‐Region, Multi‐Step Output Integrated Model: SP‐CTLA
Jian Hao, Yuxin Liang, H. Y. Zhao, et al.
Transactions in GIS (2025) Vol. 29, Iss. 3
Closed Access
Screening for pulmonary tuberculosis in high-risk groups of diabetic patients
Ye Ji, Hengfu Cao, Qiao Liu, et al.
International Journal of Infectious Diseases (2020) Vol. 93, pp. 84-89
Open Access | Times Cited: 25
Time Series Analysis and Forecasting of the Hand-Foot-Mouth Disease Morbidity in China Using An Advanced Exponential Smoothing State Space TBATS Model
Chongchong Yu, Chunjie Xu, Yuhong Li, et al.
Infection and Drug Resistance (2021) Vol. Volume 14, pp. 2809-2821
Open Access | Times Cited: 23
Evaluating the impact of COVID-19 outbreak on hepatitis B and forecasting the epidemiological trend in mainland China: a causal analysis
Chao-Qun He, Bai-Hong Sun, Wang-Tao Yu, et al.
BMC Public Health (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 3
<p>Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework</p>
Yongbin Wang, Chunjie Xu, Jingchao Ren, et al.
Infection and Drug Resistance (2020) Vol. Volume 13, pp. 733-747
Open Access | Times Cited: 21
Time series analysis of temporal trends in hemorrhagic fever with renal syndrome morbidity rate in China from 2005 to 2019
Yongbin Wang, Chunjie Xu, Weidong Wu, et al.
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 21
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