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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:

OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany
Stefan T. Radev, Frederik Graw, Simiao Chen, et al.
PLoS Computational Biology (2021) Vol. 17, Iss. 10, pp. e1009472-e1009472
Open Access | Times Cited: 24

Showing 24 citing articles:

Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong
Ammon Thompson, Benjamin J. Liebeskind, Erik J. Scully, et al.
Systematic Biology (2024) Vol. 73, Iss. 1, pp. 183-206
Open Access | Times Cited: 13
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks
Marvin Schmitt, Paul‐Christian Bürkner, Ullrich Köthe, et al.
Lecture notes in computer science (2024), pp. 541-557
Closed Access | Times Cited: 6
Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges
Yang Ye, Abhishek Pandey, Carolyn E. Bawden, et al.
Nature Communications (2025) Vol. 16, Iss. 1
Open Access
COVID-19 diagnosis on CT images with Bayes optimization-based deep neural networks and machine learning algorithms
Murat Canayaz, Sanem Şehri̇banoğlu, Recep O. Ozdag, et al.
Neural Computing and Applications (2022) Vol. 34, Iss. 7, pp. 5349-5365
Open Access | Times Cited: 25
Incorporating Deep Learning Into System Dynamics: Amortized Bayesian Inference for Scalable Likelihood‐Free Parameter Estimation
Hazhir Rahmandad, Ali Akhavan, Mohammad S. Jalali
System Dynamics Review (2025) Vol. 41, Iss. 1
Open Access
Deep learning and likelihood approaches for viral phylogeography converge on the same answers whether the inference model is right or wrong
Ammon Thompson, Benjamin J. Liebeskind, Erik J. Scully, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 11
Amortized Inference with User Simulations
Hee-Seung Moon, Antti Oulasvirta, Byungjoo Lee
(2023), pp. 1-20
Open Access | Times Cited: 11
BayesFlow: Amortized Bayesian Workflows With Neural Networks
Stefan T. Radev, Marvin Schmitt, Lukas Schumacher, et al.
The Journal of Open Source Software (2023) Vol. 8, Iss. 89, pp. 5702-5702
Open Access | Times Cited: 9
Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy
Paul‐Christian Bürkner, Maximilian Scholz, Stefan T. Radev
Statistics Surveys (2023) Vol. 17, Iss. none
Open Access | Times Cited: 9
Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants in Germany
Martin J. Kühn, Daniel Abele, Sebastian Binder, et al.
BMC Infectious Diseases (2022) Vol. 22, Iss. 1
Open Access | Times Cited: 14
Estimation of age-stratified contact rates during the COVID-19 pandemic using a novel inference algorithm
C. M. Pooley, Andrea Doeschl‐Wilson, Glenn Marion
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (2022) Vol. 380, Iss. 2233
Open Access | Times Cited: 13
HetMM: A Michaelis-Menten model for non-homogeneous enzyme mixtures
Jordan Douglas, Charles W. Carter, Peter R. Wills
iScience (2024) Vol. 27, Iss. 2, pp. 108977-108977
Open Access | Times Cited: 2
e3SIM: epidemiological-ecological-evolutionary simulation framework for genomic epidemiology
Peiyu Xu, Shenni Liang, Andrew W. Hahn, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 2
Simultaneous Identification of Changepoints and Model Parameters in Switching Dynamical Systems
Xiaoming Fu, Kai Fan, Heinrich Zozmann, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 1
Towards Reliable Parameter Extraction in MEMS Final Module Testing Using Bayesian Inference
Monika E. Heringhaus, Yi Zhang, André Zimmermann, et al.
Sensors (2022) Vol. 22, Iss. 14, pp. 5408-5408
Open Access | Times Cited: 6
Final sizes and durations of new COVID-19 pandemic waves in Poland and Germany predicted by generalized SIR model
Igor Nesteruk
medRxiv (Cold Spring Harbor Laboratory) (2021)
Open Access | Times Cited: 6
Bayesian model-based damage detection in engineering systems using Invertible Neural Networks
Andrés Martínez, Christos Lathourakis
e-Journal of Nondestructive Testing (2024) Vol. 29, Iss. 7
Open Access
A Century of Mathematical Epidemiology: A Bibliometric Analysis and Visualization of Research Trends
Pratheesh Mathew, Dary John, Jais Kurian, et al.
Cureus (2024)
Open Access
Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants in Germany
Martin J. Kühn, Daniel Abele, Sebastian C. Binder, et al.
medRxiv (Cold Spring Harbor Laboratory) (2021)
Open Access | Times Cited: 4
HetMM: A Michaelis-Menten model for non-homogeneous enzyme mixtures
Jordan Douglas, Charles W. Carter, Peter R. Wills
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 1
HetMM: A Michaelis-Menten Model for Non-Homogeneous Enzyme Mixtures
Jordan Douglas, Charles W. Carter, Peter R. Wills
(2023)
Open Access | Times Cited: 1
Inference of astrophysical parameters with a conditional invertible neural network
Teresa Bister, M. Erdmann, Ullrich Köthe, et al.
Journal of Physics Conference Series (2023) Vol. 2438, Iss. 1, pp. 012094-012094
Open Access
Hybrid prediction of infections and deaths due to COVID-19 in two Colombian data series
Mónica Paola de la Cruz, Diana Milena Galvis, Gladys Elena Salcedo Echeverry
PLoS ONE (2023) Vol. 18, Iss. 6, pp. e0286643-e0286643
Open Access
Den Patienten wirklich verstehen lernen: Real-world-Evidenz aus der „patient journey“
Petya Zyumbileva, Maria Uebe, Stefanie Rudolph, et al.
Prävention und Gesundheitsförderung (2022)
Open Access
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