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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:

Extended compartmental model for modeling COVID-19 epidemic in Slovenia
Miha Fošnarič, Tina Kamenšek, Jerneja Žganec Gros, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 9

Showing 9 citing articles:

Hybrid PDE–ODE models for efficient simulation of infection spread in epidemiology
Kristina Maier, Martin R. Weiser, Tim Conrad
Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences (2025) Vol. 481, Iss. 2306
Open Access
Enhancing COVID-19 forecasting precision through the integration of compartmental models, machine learning and variants
Daniele Baccega, Paolo Castagno, Antonio Fernández Anta, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 2
Predictive model of COVID-19 epidemic process based on neural network
Serhii Krivtsov, Ievgen Meniailov, Kseniia Bazilevych, et al.
RADIOELECTRONIC AND COMPUTER SYSTEMS (2022), Iss. 4, pp. 7-18
Open Access | Times Cited: 4
Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models
Dmytro Chumachenko, Tetiana Dudkina, Sergiy Yakovlev, et al.
International Journal of Telemedicine and Applications (2023) Vol. 2023, pp. 1-24
Open Access | Times Cited: 2
Exploring the potential of learning methods and recurrent dynamic model with vaccination: A comparative case study of COVID-19 in Austria, Brazil, and China
Seyed Ali Rakhshan, Marzie Zaj, F.H. Ghane, et al.
Physical review. E (2024) Vol. 109, Iss. 1
Closed Access
Enhancing COVID-19 Forecasting Precision through the Integration of Compartmental Models, Machine Learning and Variants
Daniele Baccega, Paolo Castagno, Antonio Fernández Anta, et al.
medRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
A dynamic ensemble approach based on trend analysis to COVID-19 incidence forecast
Jair Paulino de Sales, Paulo S. G. de Mattos Neto, Paulo Renato Alves Firmino
Biomedical Signal Processing and Control (2024) Vol. 95, pp. 106435-106435
Closed Access
A deterministic compartmental model for the transition between variants in the spread of Covid-19 in Italy
Mario Saviano, Annalisa Fierro, Antonella Liccardo
PLoS ONE (2023) Vol. 18, Iss. 11, pp. e0293416-e0293416
Open Access
COVID-19 Pandemisinin Kompartman Modelleri: Sistematik Bir Literatür Taraması
Deniz YERİNDE, Merve Er
Bayburt Üniversitesi Fen Bilimleri Dergisi (2023) Vol. 6, Iss. 2, pp. 254-267
Open Access
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