John J. Won
Impact in
- Infectious Diseases top 1%
- SARS-CoV-2 and COVID-19 Research
- COVID-19 Clinical Research Studies
- Viral gastroenteritis research and epidemiology
- SARS-CoV-2 detection and testing
- Neurology top 5%
- Long-Term Effects of COVID-19
Papers in
-
- SARS-CoV-2 and COVID-19 Research 2
- COVID-19 Clinical Research Studies 2
- SARS-CoV-2 detection and testing 1
-
- Animal Virus Infections Studies 2
- Co-authors
- Joy Y. Feng (2 shared papers)Mark R. Denison (2 shared papers)Amy María Sims (2 shared papers)Timothy P. Sheahan (2 shared papers)Tomáš Cihlář (2 shared papers)Ariane J. Brown (2 shared papers)Ralph Steven Baric (2 shared papers)Robert Anthony Jordan (1 shared paper)
- Journals
- Science Translational Medicine (1 paper)Antiviral Research (1 paper)Nature Communications (1 paper)Clinical Infectious Diseases (1 paper)Hepatology (1 paper)
- Partner nations
- United StatesDenmark
In The Last Decade
John J. Won
5 papers receiving 1.8k citations
John J. Won's Hit Papers
Peers
Comparison fields: 5 of 104
- Infectious Diseases 1.4k
- Neurology 326
- Modeling and Simulation 54
- Computational Theory and Mathematics 193
- Obstetrics and Gynecology 51
Countries citing papers authored by John J. Won
This map shows the geographic impact of John J. Won'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 John J. Won with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John J. Won more than expected).
Fields of papers citing papers by John J. Won
This network shows the impact of papers produced by John J. Won. 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 John J. Won. The network helps show where John J. Won may publish in the future.
Co-authors
The 19 scholars most cited alongside John J. Won, 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 | Comparative therapeutic efficacy of remdesivir and combination lopinavir, ritonavir, and interferon beta against MERS-CoV Hit paper breakdown → | 2020 | 1343 |
| 2 | Broad spectrum antiviral remdesivir inhibits human endemic and zoonotic deltacoronaviruses with a highly divergent RNA dependent RNA polymerase Hit paper breakdown → | 2019 | 404 |
| 3 | 2022 | 59 | |
| 4 | 2023 | 13 | |
| 5 | 2021 | 6 |
About John J. Won
John J. Won is a scholar working on Infectious Diseases, Animal Science and Zoology, Immunology, Genetics and Epidemiology, having authored 5 papers that have together received 1.8k indexed citations. Recurring topics across this work include Animal Virus Infections Studies (2 papers), SARS-CoV-2 and COVID-19 Research (2 papers), COVID-19 Clinical Research Studies (2 papers), Virus-based gene therapy research (1 paper), interferon and immune responses (1 paper), SARS-CoV-2 detection and testing (1 paper) and Respiratory viral infections research (1 paper). The work is most often cited by research in Infectious Diseases (1.4k citations), Neurology (326 citations), Modeling and Simulation (54 citations), Computational Theory and Mathematics (193 citations) and Obstetrics and Gynecology (51 citations). John J. Won has collaborated with scholars based in United States and Denmark. Frequent co-authors include Joy Y. Feng, Mark R. Denison, Amy María Sims, Timothy P. Sheahan, Tomáš Cihlář, Ariane J. Brown, Ralph Steven Baric, Robert Anthony Jordan, Stephanie A. Montgomery and Sarah R. Leist. Their work appears in journals such as Science Translational Medicine, Antiviral Research, Nature Communications, Clinical Infectious Diseases and Hepatology.
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.