Noa Dagan

6.7k total citations · 5 hit papers
37 papers, 4.0k citations indexed

About

Noa Dagan is a scholar working on Infectious Diseases, Cardiology and Cardiovascular Medicine and Health. According to data from OpenAlex, Noa Dagan has authored 37 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Infectious Diseases, 7 papers in Cardiology and Cardiovascular Medicine and 6 papers in Health. Recurrent topics in Noa Dagan's work include SARS-CoV-2 and COVID-19 Research (17 papers), COVID-19 Clinical Research Studies (11 papers) and Vaccine Coverage and Hesitancy (6 papers). Noa Dagan is often cited by papers focused on SARS-CoV-2 and COVID-19 Research (17 papers), COVID-19 Clinical Research Studies (11 papers) and Vaccine Coverage and Hesitancy (6 papers). Noa Dagan collaborates with scholars based in Israel, United States and United Kingdom. Noa Dagan's co-authors include Ran D. Balicer, Noam Barda, Ben Y. Reis, Marc Lipsitch, Miguel A. Hernán, Eldad Kepten, Oren Miron, Mark A. Katz, Isaac S. Kohane and Doron Netzer and has published in prestigious journals such as Science, New England Journal of Medicine and Proceedings of the National Academy of Sciences.

In The Last Decade

Noa Dagan

33 papers receiving 3.9k citations

Hit Papers

BNT162b2 mRNA Covid-19 Vaccine in a Nationwide Mass Vacci... 2021 2026 2022 2024 2021 2021 2021 2022 2021 500 1000 1.5k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Noa Dagan Israel 18 2.7k 1.1k 550 447 369 37 4.0k
Noam Barda Israel 17 2.6k 1.0× 1.1k 1.0× 563 1.0× 347 0.8× 322 0.9× 45 3.7k
Julia Stowe United Kingdom 31 3.4k 1.2× 1.6k 1.4× 912 1.7× 264 0.6× 192 0.5× 82 5.2k
Sara E. Oliver United States 20 1.6k 0.6× 1.0k 0.9× 384 0.7× 378 0.8× 281 0.8× 54 2.9k
Tom T. Shimabukuro United States 37 2.5k 0.9× 1.6k 1.4× 209 0.4× 873 2.0× 595 1.6× 92 4.8k
Bradley K. Ackerson United States 26 2.1k 0.8× 929 0.8× 396 0.7× 222 0.5× 123 0.3× 100 3.4k
Julianne Gee United States 32 1.9k 0.7× 1.9k 1.6× 216 0.4× 865 1.9× 433 1.2× 74 4.6k
John M. McLaughlin United States 29 2.3k 0.9× 1.3k 1.1× 726 1.3× 173 0.4× 88 0.2× 105 4.3k
Ruth Simmons United Kingdom 18 2.5k 0.9× 944 0.8× 749 1.4× 115 0.3× 45 0.1× 69 3.4k
Elise Tessier United Kingdom 15 2.4k 0.9× 1.2k 1.1× 829 1.5× 79 0.2× 53 0.1× 32 3.3k
Simon Thelwall United Kingdom 9 1.9k 0.7× 608 0.5× 581 1.1× 126 0.3× 66 0.2× 19 2.4k

Countries citing papers authored by Noa Dagan

Since Specialization
Citations

This map shows the geographic impact of Noa Dagan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Noa Dagan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Noa Dagan more than expected).

Fields of papers citing papers by Noa Dagan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Noa Dagan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Noa Dagan. The network helps show where Noa Dagan may publish in the future.

Co-authorship network of co-authors of Noa Dagan

This figure shows the co-authorship network connecting the top 25 collaborators of Noa Dagan. A scholar is included among the top collaborators of Noa Dagan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Noa Dagan. Noa Dagan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Shapira, Bracha, et al.. (2024). Trends of common laboratory biomarkers after SARS-CoV-2 infection. Journal of Infection. 89(6). 106318–106318.
3.
Dagan, Noa, Adir Sommer, Reut Ohana, et al.. (2024). Evaluation of AI Solutions in Health Care Organizations — The OPTICA Tool. NEJM AI. 1(9). 11 indexed citations
4.
Isakov, Ofer, Dan Riesel, Ben Y. Reis, et al.. (2024). Development and Validation of a Colorectal Cancer Prediction Model: A Nationwide Cohort-Based Study. Digestive Diseases and Sciences. 69(7). 2611–2620.
5.
Koller, Daphne, Andrew L. Beam, Arjun K. Manrai, et al.. (2023). Why We Support and Encourage the Use of Large Language Models in NEJM AI Submissions. NEJM AI. 1(1). 33 indexed citations
7.
Hayek, Samah, Yatir Ben‐Shlomo, Eldad Kepten, et al.. (2022). Indirect protection of children from SARS-CoV-2 infection through parental vaccination. Science. 375(6585). 1155–1159. 34 indexed citations
8.
Cohen-Stavi, Chandra J., Noam Barda, Shlomit Yaron, et al.. (2022). BNT162b2 Vaccine Effectiveness against Omicron in Children 5 to 11 Years of Age. New England Journal of Medicine. 387(3). 227–236. 53 indexed citations
9.
Hayek, Samah, Yatir Ben‐Shlomo, Noa Dagan, et al.. (2022). Effectiveness of REGEN-COV antibody combination in preventing severe COVID-19 outcomes. Nature Communications. 13(1). 4480–4480. 5 indexed citations
10.
Bielopolski, Dana, Noam Barda, Noa Dagan, et al.. (2022). BNT162b2 vaccine effectiveness in chronic kidney disease patients—an observational study. Clinical Kidney Journal. 15(10). 1838–1846. 6 indexed citations
11.
Waxman, Jacob, Ben Y. Reis, Doron Netzer, et al.. (2022). Comparing COVID-19-related hospitalization rates among individuals with infection-induced and vaccine-induced immunity in Israel. Nature Communications. 13(1). 2202–2202. 16 indexed citations
12.
Mittelman, Moshe, Noam Barda, Noa Dagan, et al.. (2021). Effectiveness of the BNT162b2mRNA COVID-19 vaccine in patients with hematological neoplasms in a nationwide mass vaccination setting. Blood. 139(10). 1439–1451. 43 indexed citations
13.
Leader, Avi, Noa Dagan, Noam Barda, et al.. (2021). Previously undiagnosed cancer in patients with arterial thrombotic events – A population‐based cohort study. Journal of Thrombosis and Haemostasis. 20(3). 635–647. 7 indexed citations
14.
Dagan, Noa, Noam Barda, Eldad Kepten, et al.. (2021). BNT162b2 mRNA Covid-19 Vaccine in a Nationwide Mass Vaccination Setting. New England Journal of Medicine. 384(15). 1412–1423. 1619 indexed citations breakdown →
15.
Shepshelovich, Daniel, Noam Barda, Hadar Goldvaser, et al.. (2021). Incidence of lung cancer following pneumonia in smokers: a population-based study. QJM. 115(5). 287–291. 4 indexed citations
16.
Barda, Noam, Noa Dagan, Cyrille J. Cohen, et al.. (2021). Effectiveness of a third dose of the BNT162b2 mRNA COVID-19 vaccine for preventing severe outcomes in Israel: an observational study. The Lancet. 398(10316). 2093–2100. 558 indexed citations breakdown →
17.
Dagan, Noa, Noam Barda, Tal Biron‐Shental, et al.. (2021). Effectiveness of the BNT162b2 mRNA COVID-19 vaccine in pregnancy. Nature Medicine. 27(10). 1693–1695. 196 indexed citations breakdown →
18.
Barda, Noam, Gal Yona, Guy N. Rothblum, et al.. (2020). Addressing bias in prediction models by improving subpopulation calibration. Journal of the American Medical Informatics Association. 28(3). 549–558. 40 indexed citations
19.
Dagan, Noa, Eldad Elnekave, Noam Barda, et al.. (2020). Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization. Nature Medicine. 26(1). 77–82. 74 indexed citations
20.
Barda, Noam, Dan Riesel, Joseph Levy, et al.. (2020). Developing a COVID-19 mortality risk prediction model when individual-level data are not available. Nature Communications. 11(1). 4439–4439. 77 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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