Keisuke Ejima
Impact in
- Modeling and Simulation top 1%
- COVID-19 epidemiological studies
- Aging top 5%
- Genetics, Aging, and Longevity in Model Organisms
Papers in
- Epidemiology 30
- Traumatic Brain Injury Research 11
- Influenza Virus Research Studies 6
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- SARS-CoV-2 and COVID-19 Research 13
- SARS-CoV-2 detection and testing 10
- Co-authors
- Hiroshi Nishiura (21 shared papers)Kazuyuki Aihara (14 shared papers)Shingo Iwami (18 shared papers)David B. Allison (19 shared papers)Kenji Mizumoto (5 shared papers)Keisuke Kawata (11 shared papers)Yasuhisa Fujita (8 shared papers)Shoya Iwanami (10 shared papers)
- Journals
- Theoretical Biology and Medical Modelling (4 papers)Journal of Theoretical Biology (4 papers)Obesity (3 papers)PLoS ONE (3 papers)Scientific Reports (2 papers)
- Partner nations
- United StatesJapanSingapore
In The Last Decade
Keisuke Ejima
76 papers receiving 1.0k citations
Peers
Comparison fields: 5 of 124
- Modeling and Simulation 272
- Aging 63
- Infectious Diseases 327
- Epidemiology 352
- Virology 41
Countries citing papers authored by Keisuke Ejima
This map shows the geographic impact of Keisuke Ejima'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 Keisuke Ejima with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Keisuke Ejima more than expected).
Fields of papers citing papers by Keisuke Ejima
This network shows the impact of papers produced by Keisuke Ejima. 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 Keisuke Ejima. The network helps show where Keisuke Ejima may publish in the future.
Co-authors
The 25 scholars most cited alongside Keisuke Ejima, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 81 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 119 | |
| 2 | 2021 | 88 | |
| 3 | 2021 | 39 | |
| 4 | 2021 | 32 | |
| 5 | 2020 | 32 | |
| 6 | 2020 | 31 | |
| 7 | 2020 | 31 | |
| 8 | 2021 | 30 | |
| 9 | 2019 | 30 | |
| 10 | 2020 | 30 | |
| 11 | 2013 | 28 | |
| 12 | 2018 | 28 | |
| 13 | 2014 | 27 | |
| 14 | 2013 | 24 | |
| 15 | 2012 | 23 | |
| 16 | 2012 | 22 | |
| 17 | 2012 | 21 | |
| 18 | 2016 | 21 | |
| 19 | 2018 | 21 | |
| 20 | 2022 | 19 |
About Keisuke Ejima
Keisuke Ejima is a scholar working on Epidemiology, Infectious Diseases, Modeling and Simulation, Public Health, Environmental and Occupational Health and Virology, having authored 81 papers that have together received 1.1k indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (25 papers), SARS-CoV-2 and COVID-19 Research (13 papers), Traumatic Brain Injury Research (11 papers), SARS-CoV-2 detection and testing (10 papers), Obesity, Physical Activity, Diet (10 papers), HIV Research and Treatment (7 papers), Nutritional Studies and Diet (7 papers) and Influenza Virus Research Studies (6 papers). The work is most often cited by research in Modeling and Simulation (272 citations), Aging (63 citations), Infectious Diseases (327 citations), Epidemiology (352 citations) and Virology (41 citations). Keisuke Ejima has collaborated with scholars based in United States, Japan and Singapore. Frequent co-authors include Hiroshi Nishiura, Kazuyuki Aihara, Shingo Iwami, David B. Allison, Kenji Mizumoto, Keisuke Kawata, Yasuhisa Fujita, Shoya Iwanami, Kwang Su Kim and Megan E. Huibregtse. Their work appears in journals such as Theoretical Biology and Medical Modelling, Journal of Theoretical Biology, Obesity, PLoS ONE and Scientific Reports.
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.