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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:

Time series analysis of COVID-19 infection curve: A change-point perspective
Feiyu Jiang, Zifeng Zhao, Xiaofeng Shao
Journal of Econometrics (2020) Vol. 232, Iss. 1, pp. 1-17
Open Access | Times Cited: 77

Showing 1-25 of 77 citing articles:

Time series prediction of COVID-19 transmission in America using LSTM and XGBoost algorithms
Junling Luo, Zhong-Liang Zhang, Yao Fu, et al.
Results in Physics (2021) Vol. 27, pp. 104462-104462
Open Access | Times Cited: 109
Limiting mobility during COVID-19, when and to what level? An international comparative study using change point analysis
Suliman Gargoum, Ali S. Gargoum
Journal of Transport & Health (2021) Vol. 20, pp. 101019-101019
Open Access | Times Cited: 40
Dynamic-spread assessment and kink detection in the early mpox outbreak
Junyang Cai, Chenwei Song, Jian Zhou, et al.
Applied Mathematical Modelling (2025), pp. 115996-115996
Closed Access
Spatiotemporal Analysis of Heat-Related Emergency Department Visits and Hospitalizations in Florida (2005–2021): A Bayesian Change Point Detection Approach
Ehsan Foroutan, Saeid Niazmardi, Tao Hu
Sustainable Cities and Society (2025), pp. 106288-106288
Closed Access
Local mortality estimates during the COVID-19 pandemic in Italy
Augusto Cerqua, R. Di Stefano, Marco Letta, et al.
Journal of Population Economics (2021) Vol. 34, Iss. 4, pp. 1189-1217
Open Access | Times Cited: 34
A hybrid time series forecasting method based on neutrosophic logic with applications in financial issues
S. A. Edalatpanah, Farnaz Sheikh Hassani, Florentín Smarandache, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 129, pp. 107531-107531
Closed Access | Times Cited: 12
Enhancing federated learning robustness in adversarial environment through clustering Non-IID features
Yanli Li, Dong Yuan, Abubakar Sadiq Sani, et al.
Computers & Security (2023) Vol. 132, pp. 103319-103319
Open Access | Times Cited: 10
Kolmogorov–Smirnov type testing for structural breaks: A new adjusted-range based self-normalization approach
Yongmiao Hong, Oliver Linton, Brendan McCabe, et al.
Journal of Econometrics (2023) Vol. 238, Iss. 2, pp. 105603-105603
Open Access | Times Cited: 8
Time series clustering based on polynomial fitting and multi-order trend features
Yun-Suk Kang, Chongyan Wu, Bin Yu
Information Sciences (2024) Vol. 678, pp. 120939-120939
Closed Access | Times Cited: 2
Forecasting the future of library and information science and its sub-fields
Zehra Taşkın
Scientometrics (2020) Vol. 126, Iss. 2, pp. 1527-1551
Open Access | Times Cited: 21
Mining the relationship between COVID-19 sentiment and market performance
Ziyuan Ziyuan, Jeffrey Chen, Anchen Sun
PLoS ONE (2024) Vol. 19, Iss. 7, pp. e0306520-e0306520
Open Access | Times Cited: 2
Modelling the COVID-19 Infection Trajectory: A Piecewise Linear Quantile Trend Model
Feiyu Jiang, Zifeng Zhao, Xiaofeng Shao
Journal of the Royal Statistical Society Series B (Statistical Methodology) (2021) Vol. 84, Iss. 5, pp. 1589-1607
Closed Access | Times Cited: 17
COVID-19 infection spread and human mobility
Masahiko Shibamoto, Shoka Hayaki, Yoshitaka Ogisu
Journal of the Japanese and International Economies (2022) Vol. 64, pp. 101195-101195
Open Access | Times Cited: 11
Multiple change point estimation of trends in Covid-19 infections and deaths in India as compared with WHO regions
Pavan Kumar, Biswajit Lahiri, Rafael Alvarado
Spatial Statistics (2021) Vol. 49, pp. 100538-100538
Open Access | Times Cited: 15
Functional data analysis: An application to COVID‐19 data in the United States in 2020
Chen Tang, Tiandong Wang, Panpan Zhang
Quantitative Biology (2022) Vol. 10, Iss. 2, pp. 172-187
Open Access | Times Cited: 10
Lag time between state-level policy interventions and change points in COVID-19 outcomes in the United States
Tanujit Dey, Jaechoul Lee, Sounak Chakraborty, et al.
Patterns (2021) Vol. 2, Iss. 8, pp. 100306-100306
Open Access | Times Cited: 13
Forecasting COVID-19 Disease Cases Using the SARIMA-NNAR Hybrid Model
İbrahim Demir, Murat Ki̇ri̇şçi̇
Universal Journal of Mathematics and Applications (2022) Vol. 5, Iss. 1, pp. 15-23
Open Access | Times Cited: 9
Mgformer: Multi-group transformer for multivariate time series classification
Jianfeng Wen, Nan Zhang, Xuzhe Lu, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108633-108633
Closed Access | Times Cited: 1
Spatiotemporal Variations of COVID-19 Variants in 100 Countries: Exploring Spatial Patterns and Time-Lag Effects
Dan Zou, Suhong Zhou, Yitong Liao, et al.
Annals of the American Association of Geographers (2024) Vol. 114, Iss. 6, pp. 1156-1176
Open Access | Times Cited: 1
Comparing the Change in R0 for the COVID-19 Pandemic in Eight Countries Using an SIR Model for Specific Periods
Tak-ching Leung
COVID (2024) Vol. 4, Iss. 7, pp. 930-951
Open Access | Times Cited: 1
Open government data, uncertainty and coronavirus: An infodemiological case study
Nikolaos Yiannakoulias, Catherine E. Slavik, Shelby L. Sturrock, et al.
Social Science & Medicine (2020) Vol. 265, pp. 113549-113549
Closed Access | Times Cited: 13
Short-term Covid-19 forecast for latecomers
Marcelo C. Medeiros, Alexandre Street, Davi Valladão, et al.
International Journal of Forecasting (2021) Vol. 38, Iss. 2, pp. 467-488
Open Access | Times Cited: 12
Testing the predictive accuracy of COVID-19 forecasts
Laura Coroneo, Fabrizio Iacone, Alessia Paccagnini, et al.
International Journal of Forecasting (2022) Vol. 39, Iss. 2, pp. 606-622
Open Access | Times Cited: 8
The risk of future waves of COVID-19: modeling and data analysis
Sha He, Jie Yang, Mengqi He, et al.
Mathematical Biosciences & Engineering (2021) Vol. 18, Iss. 5, pp. 5409-5426
Open Access | Times Cited: 11
Modeling and forecasting the COVID-19 pandemic with heterogeneous autoregression approaches: South Korea
Eunju Hwang, SeongMin Yu
Results in Physics (2021) Vol. 29, pp. 104631-104631
Open Access | Times Cited: 10
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