Takeshi Arashiro
- Infectious Diseases top 10%
- Epidemiology
- Public Health, Environmental and Occupational Health
- Modeling and Simulation top 5%
- Ecology, Evolution, Behavior and Systematics
- Co-authors
- Keiichi FurukawaAkira NakamuraYuzo ArimaTadaki SuzukiHarutaka KatanoShuetsu FukushiKenta TakahashiMasayuki Saijo
- Topics
- COVID-19 epidemiological studies (9 papers)SARS-CoV-2 and COVID-19 Research (8 papers)COVID-19 Clinical Research Studies (8 papers)
- Journals
- Journal of Clinical InvestigationEmerging infectious diseasesPLoS neglected tropical diseases
- Partner nations
- JapanUnited StatesFrance
In The Last Decade
Takeshi Arashiro
26 papers receiving 320 citations
Peers
Comparison fields: 5 of 59
- Infectious Diseases 216
- Epidemiology 80
- Public Health, Environmental and Occupational Health 63
- Modeling and Simulation 56
- Ecology, Evolution, Behavior and Systematics 32
Countries citing papers authored by Takeshi Arashiro
This map shows the geographic impact of Takeshi Arashiro'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 Takeshi Arashiro with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Takeshi Arashiro more than expected).
Fields of papers citing papers by Takeshi Arashiro
This network shows the impact of papers produced by Takeshi Arashiro. 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 Takeshi Arashiro. The network helps show where Takeshi Arashiro may publish in the future.
Co-authorship network of co-authors of Takeshi Arashiro
This figure shows the co-authorship network connecting the top 25 collaborators of Takeshi Arashiro. A scholar is included among the top collaborators of Takeshi Arashiro 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 Takeshi Arashiro. Takeshi Arashiro is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 3 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 2 | |
| 6 | 2 | |
| 7 | 1 | |
| 8 | 1 | |
| 9 | 6 | |
| 10 | 3 | |
| 11 | 14 | |
| 12 | 8 | |
| 13 | 1 | |
| 14 | 4 | |
| 15 | 5 | |
| 16 | 0 | |
| 17 | 1 | |
| 18 | 79 | |
| 19 | 8 | |
| 20 | 26 |
About Takeshi Arashiro
Takeshi Arashiro is a scholar working on Modeling and Simulation, Infectious Diseases and Epidemiology, having authored 28 papers that have together received 324 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (9 papers), SARS-CoV-2 and COVID-19 Research (8 papers) and COVID-19 Clinical Research Studies (8 papers). The work is most often cited by research in Modeling and Simulation (56 citations), Infectious Diseases (216 citations) and Endocrinology (19 citations). Takeshi Arashiro has collaborated with scholars based in Japan, United States and France. Frequent co-authors include Keiichi Furukawa, Akira Nakamura, Yuzo Arima, Tadaki Suzuki, Harutaka Katano, Shuetsu Fukushi, Kenta Takahashi, Masayuki Saijo, Yuji Wada and Kaori Sano. Their work appears in journals such as Journal of Clinical Investigation, Emerging infectious diseases and PLoS neglected tropical diseases.
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.