Randall Dahn
Impact in
- Agronomy and Crop Science top 10%
- Animal Disease Management and Epidemiology
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- Viral gastroenteritis research and epidemiology
- Viral Infections and Vectors
- SARS-CoV-2 and COVID-19 Research
Papers in
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- Viral gastroenteritis research and epidemiology 2
- SARS-CoV-2 and COVID-19 Research 1
- COVID-19 Clinical Research Studies 1
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- Influenza Virus Research Studies 2
- Respiratory viral infections research 1
- Co-authors
- Yoshihiro Kawaoka (4 shared papers)Tadashi Maemura (3 shared papers)Chunyang Gu (3 shared papers)Gabriele Neumann (3 shared papers)Lavanya Babujee (4 shared papers)Yasuo Suzuki (2 shared papers)Sanja Trifkovic (2 shared papers)Peter Halfmann (3 shared papers)
- Partner nations
- JapanUnited StatesUnited Kingdom
In The Last Decade
Randall Dahn
4 papers receiving 164 citations
Randall Dahn's Hit Papers
Peers
Comparison fields: 5 of 30
- Agronomy and Crop Science 70
- Infectious Diseases 96
- Epidemiology 147
- Animal Science and Zoology 22
- Modeling and Simulation 6
Countries citing papers authored by Randall Dahn
This map shows the geographic impact of Randall Dahn'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 Randall Dahn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Randall Dahn more than expected).
Fields of papers citing papers by Randall Dahn
This network shows the impact of papers produced by Randall Dahn. 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 Randall Dahn. The network helps show where Randall Dahn may publish in the future.
Co-authors
The 25 scholars most cited alongside Randall Dahn, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Pathogenicity and transmissibility of bovine H5N1 influenza virus Hit paper breakdown → | 2024 | 115 |
| 2 | A human isolate of bovine H5N1 is transmissible and lethal in animal models Hit paper breakdown → | 2024 | 52 |
| 3 | 2024 | 4 | |
| 4 | 2025 | 2 |
About Randall Dahn
Randall Dahn is a scholar working on Infectious Diseases, Epidemiology, Surgery, Ophthalmology and Agronomy and Crop Science, having authored 4 papers that have together received 173 indexed citations. Recurring topics across this work include Influenza Virus Research Studies (2 papers), Viral gastroenteritis research and epidemiology (2 papers), Animal Disease Management and Epidemiology (1 paper), Retinal and Optic Conditions (1 paper), SARS-CoV-2 and COVID-19 Research (1 paper), Respiratory viral infections research (1 paper), COVID-19 Clinical Research Studies (1 paper) and Organ Transplantation Techniques and Outcomes (1 paper). The work is most often cited by research in Agronomy and Crop Science (70 citations), Infectious Diseases (96 citations), Epidemiology (147 citations), Animal Science and Zoology (22 citations) and Modeling and Simulation (6 citations). Randall Dahn has collaborated with scholars based in Japan, United States and United Kingdom. Frequent co-authors include Yoshihiro Kawaoka, Tadashi Maemura, Chunyang Gu, Gabriele Neumann, Lavanya Babujee, Yasuo Suzuki, Sanja Trifkovic, Peter Halfmann, Amie J. Eisfeld and Tong Wang. Their work appears in journals such as Nature, EBioMedicine and Nature Communications.
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.