Motoi Suzuki
- Modeling and Simulation top 0.5%
- COVID-19 epidemiological studies 33
- Infectious Diseases top 2%
- SARS-CoV-2 and COVID-19 Research 26
- Viral Infections and Vectors 19
- COVID-19 Clinical Research Studies 15
- Parasitology top 2%
- Epidemiology top 2%
- Respiratory viral infections research 36
- Pneumonia and Respiratory Infections 33
- Influenza Virus Research Studies 19
- Microbiology top 2%
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- Vaccine Coverage and Hesitancy 13
- Co-authors
- Koya AriyoshiKonosuke MorimotoLay‐Myint YoshidaVũ Đình ThiểmHideki YanaiWolf‐Peter SchmidtĐặng Đức AnhLe Huu Tho
- Journals
- Proceedings of the National Academy of Sciences (1 paper)SHILAP Revista de lepidopterología (1 paper)PLoS ONE (8 papers)
- Partner nations
- JapanVietnamUnited Kingdom
In The Last Decade
Motoi Suzuki
139 papers receiving 2.9k citations
Peers
Comparison fields: 5 of 147
- Modeling and Simulation 481
- Infectious Diseases 913
- Parasitology 255
- Epidemiology 1.3k
- Microbiology 185
Countries citing papers authored by Motoi Suzuki
This map shows the geographic impact of Motoi Suzuki'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 Motoi Suzuki with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Motoi Suzuki more than expected).
Fields of papers citing papers by Motoi Suzuki
This network shows the impact of papers produced by Motoi Suzuki. 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 Motoi Suzuki. The network helps show where Motoi Suzuki may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Motoi Suzuki, 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 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 1 | |
| 5 | 2025 | 0 | |
| 6 | 2024 | 0 | |
| 7 | 2024 | 3 | |
| 8 | 2024 | 0 | |
| 9 | 2023 | 2 | |
| 10 | 2023 | 9 | |
| 11 | 2023 | 14 | |
| 12 | 2022 | 4 | |
| 13 | 2022 | 0 | |
| 14 | 2022 | 12 | |
| 15 | 2021 | 10 | |
| 16 | 2021 | 2 | |
| 17 | 2020 | 46 | |
| 18 | QUBO solver for combinatorial optimization problems with constraints | 2019 | 6 |
| 19 | Supporting Fair Trade : Cultural Anthropological Study and Critique | 2019 | 0 |
| 20 | Labor Saving Technology for Planting Chinese Yams Using a Seed Tuber Planter (Part 2) | 2012 | 1 |
About Motoi Suzuki
Motoi Suzuki is a scholar working on Modeling and Simulation, Infectious Diseases and Health, having authored 157 papers that have together received 3.0k indexed citations. Recurring topics across this work include Respiratory viral infections research (36 papers), COVID-19 epidemiological studies (33 papers), Pneumonia and Respiratory Infections (33 papers), SARS-CoV-2 and COVID-19 Research (26 papers), Influenza Virus Research Studies (19 papers), Viral Infections and Vectors (19 papers), COVID-19 Clinical Research Studies (15 papers) and Vaccine Coverage and Hesitancy (13 papers). The work is most often cited by research in Modeling and Simulation (481 citations), Infectious Diseases (913 citations) and Parasitology (255 citations). Motoi Suzuki has collaborated with scholars based in Japan, Vietnam and United Kingdom. Frequent co-authors include Koya Ariyoshi, Konosuke Morimoto, Lay‐Myint Yoshida, Vũ Đình Thiểm, Hideki Yanai, Wolf‐Peter Schmidt, Đặng Đức Anh, Le Huu Tho, Makito Yaegashi and Masahiro Aoshima. Their work appears in journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and PLoS ONE.
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.