Prerna Arora

2.9k total citations · 1 hit paper
18 papers, 961 citations indexed

About

Prerna Arora is a scholar working on Infectious Diseases, Animal Science and Zoology and Molecular Biology. According to data from OpenAlex, Prerna Arora has authored 18 papers receiving a total of 961 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Infectious Diseases, 4 papers in Animal Science and Zoology and 3 papers in Molecular Biology. Recurrent topics in Prerna Arora's work include SARS-CoV-2 and COVID-19 Research (15 papers), COVID-19 Clinical Research Studies (10 papers) and SARS-CoV-2 detection and testing (4 papers). Prerna Arora is often cited by papers focused on SARS-CoV-2 and COVID-19 Research (15 papers), COVID-19 Clinical Research Studies (10 papers) and SARS-CoV-2 detection and testing (4 papers). Prerna Arora collaborates with scholars based in Germany and United States. Prerna Arora's co-authors include Stefan Pöhlmann, Markus Hoffmann, Amy Kempf, Luise Graichen, Sebastian Schulz, Hans‐Martin Jäck, Martin Sebastian Winkler, Nadine Krüger, Heike Hofmann-Winkler and Bojan F. Hörnich and has published in prestigious journals such as Cell, Nature Communications and PLoS ONE.

In The Last Decade

Prerna Arora

18 papers receiving 956 citations

Hit Papers

SARS-CoV-2 variants B.1.3... 2021 2026 2022 2024 2021 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Prerna Arora Germany 10 879 208 127 92 88 18 961
Prudence Kgagudi South Africa 7 685 0.8× 185 0.9× 108 0.9× 69 0.8× 73 0.8× 10 797
Veronica Ueckermann South Africa 8 706 0.8× 226 1.1× 108 0.9× 72 0.8× 63 0.7× 26 824
Anna-Sophie Moldenhauer Germany 12 873 1.0× 221 1.1× 112 0.9× 140 1.5× 65 0.7× 14 1.0k
Michael T. Boswell South Africa 8 679 0.8× 184 0.9× 108 0.9× 71 0.8× 60 0.7× 11 751
Frances Ayres South Africa 5 660 0.8× 181 0.9× 110 0.9× 79 0.9× 74 0.8× 8 729
Cheila Rocha Germany 13 725 0.8× 221 1.1× 77 0.6× 114 1.2× 62 0.7× 27 943
Alexander S. Hahn Germany 8 619 0.7× 181 0.9× 91 0.7× 100 1.1× 55 0.6× 18 762
Claire Carlin United States 11 701 0.8× 210 1.0× 130 1.0× 59 0.6× 60 0.7× 14 743
Bojan F. Hörnich Germany 5 606 0.7× 170 0.8× 90 0.7× 75 0.8× 55 0.6× 6 701
Alina Seidel Germany 8 679 0.8× 146 0.7× 109 0.9× 78 0.8× 57 0.6× 12 736

Countries citing papers authored by Prerna Arora

Since Specialization
Citations

This map shows the geographic impact of Prerna Arora'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 Prerna Arora with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Prerna Arora more than expected).

Fields of papers citing papers by Prerna Arora

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Prerna Arora. 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 Prerna Arora. The network helps show where Prerna Arora may publish in the future.

Co-authorship network of co-authors of Prerna Arora

This figure shows the co-authorship network connecting the top 25 collaborators of Prerna Arora. A scholar is included among the top collaborators of Prerna Arora 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 Prerna Arora. Prerna Arora is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Arora, Prerna, Amy Kempf, Inga Nehlmeier, et al.. (2025). Entry Efficiency, Protease Dependence, and Antibody-Mediated Neutralization of SARS-CoV-2 Sublineages KP.3.1.1 and XEC. Vaccines. 13(4). 385–385. 1 indexed citations
2.
Arora, Prerna, Lu Zhang, Inga Nehlmeier, et al.. (2025). Host cell lectins ASGR1 and DC-SIGN jointly with TMEM106B confer ACE2 independence and imdevimab resistance to SARS-CoV-2 pseudovirus with spike mutation E484D. Journal of Virology. 99(2). e0123024–e0123024. 1 indexed citations
3.
Schulz, Sebastian, Amy Kempf, Inga Nehlmeier, et al.. (2024). Comparative Analysis of Host Cell Entry Efficiency and Neutralization Sensitivity of Emerging SARS-CoV-2 Lineages KP.2, KP.2.3, KP.3, and LB.1. Vaccines. 12(11). 1236–1236. 2 indexed citations
4.
Hoffmann, Markus, Lok-Yin Roy Wong, Prerna Arora, et al.. (2023). Omicron subvariant BA.5 efficiently infects lung cells. Nature Communications. 14(1). 3500–3500. 21 indexed citations
5.
Arora, Prerna, Lu Zhang, Cheila Rocha, et al.. (2022). The SARS-CoV-2 Delta-Omicron Recombinant Lineage (XD) Exhibits Immune-Escape Properties Similar to the Omicron (BA.1) Variant. International Journal of Molecular Sciences. 23(22). 14057–14057. 4 indexed citations
6.
Arora, Prerna, Anzhalika Sidarovich, Luise Graichen, et al.. (2022). Functional analysis of polymorphisms at the S1/S2 site of SARS-CoV-2 spike protein. PLoS ONE. 17(3). e0265453–e0265453. 9 indexed citations
7.
Arora, Prerna, Amy Kempf, Inga Nehlmeier, et al.. (2022). SARS-CoV-2 variants C.1.2 and B.1.621 (Mu) partially evade neutralization by antibodies elicited upon infection or vaccination. Cell Reports. 39(5). 110754–110754. 4 indexed citations
8.
Hoffmann, Markus, Prerna Arora, & Stefan Pöhlmann. (2022). Understanding Omicron: Transmissibility, immune evasion and antiviral intervention. Clinical and Translational Medicine. 12(5). e839–e839. 2 indexed citations
9.
Arora, Prerna, Lu Zhang, Nadine Krüger, et al.. (2022). SARS-CoV-2 Omicron sublineages show comparable cell entry but differential neutralization by therapeutic antibodies. Cell Host & Microbe. 30(8). 1103–1111.e6. 29 indexed citations
10.
Hoffmann, Markus, Anzhalika Sidarovich, Prerna Arora, et al.. (2022). Evidence for an ACE2-Independent Entry Pathway That Can Protect from Neutralization by an Antibody Used for COVID-19 Therapy. mBio. 13(3). e0036422–e0036422. 22 indexed citations
11.
Arora, Prerna, Amy Kempf, Inga Nehlmeier, et al.. (2022). No evidence for increased cell entry or antibody evasion by Delta sublineage AY.4.2. Cellular and Molecular Immunology. 19(3). 449–452. 5 indexed citations
12.
Hoffmann, Markus, Heike Hofmann-Winkler, Nadine Krüger, et al.. (2021). SARS-CoV-2 variant B.1.617 is resistant to bamlanivimab and evades antibodies induced by infection and vaccination. Cell Reports. 36(3). 109415–109415. 162 indexed citations
13.
Arora, Prerna, Anzhalika Sidarovich, Nadine Krüger, et al.. (2021). B.1.617.2 enters and fuses lung cells with increased efficiency and evades antibodies induced by infection and vaccination. Cell Reports. 37(2). 109825–109825. 56 indexed citations
14.
Arora, Prerna, Cheila Rocha, Amy Kempf, et al.. (2021). The spike protein of SARS-CoV-2 variant A.30 is heavily mutated and evades vaccine-induced antibodies with high efficiency. Cellular and Molecular Immunology. 18(12). 2673–2675. 29 indexed citations
15.
Arora, Prerna, Pavel Marichal‐Gallardo, Michael Winkler, et al.. (2021). Cell culture-based production and in vivo characterization of purely clonal defective interfering influenza virus particles. BMC Biology. 19(1). 91–91. 19 indexed citations
17.
Hoffmann, Markus, Prerna Arora, Rüdiger Groß, et al.. (2021). SARS-CoV-2 variants B.1.351 and P.1 escape from neutralizing antibodies. Cell. 184(9). 2384–2393.e12. 563 indexed citations breakdown →
18.
Arora, Prerna, Sabine Gärtner, Markus Hoffmann, et al.. (2019). A system for production of defective interfering particles in the absence of infectious influenza A virus. PLoS ONE. 14(3). e0212757–e0212757. 24 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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