Katsuma Hayashi
- Modeling and Simulation top 0.2%
- COVID-19 epidemiological studies 12
- Infectious Diseases top 2%
- SARS-CoV-2 and COVID-19 Research 5
- COVID-19 Clinical Research Studies 4
- Viral Infections and Outbreaks Research 3
- Economics and Econometrics top 5%
- COVID-19 Pandemic Impacts 4
- Health top 10%
- Clinical Psychology top 10%
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- COVID-19 and healthcare impacts 4
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- Infection Control and Ventilation 3
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- Climate Change and Health Impacts 2
- Co-authors
- Hiroshi NishiuraSung-mok JungT. KobayashiAndrei R. AkhmetzhanovNatalie M. LintonYichi YangRyo KinoshitaBaoyin Yuan
- Journals
- Journal of Theoretical Biology (2 papers)American Journal of Tropical Medicine and Hygiene (1 paper)FEMS Microbiology Letters (1 paper)
- Partner nations
- JapanUnited Kingdom
In The Last Decade
Katsuma Hayashi
18 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 126
- Modeling and Simulation 879
- Infectious Diseases 584
- Economics and Econometrics 363
- Health 68
- Clinical Psychology 150
Countries citing papers authored by Katsuma Hayashi
This map shows the geographic impact of Katsuma Hayashi'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 Katsuma Hayashi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Katsuma Hayashi more than expected).
Fields of papers citing papers by Katsuma Hayashi
This network shows the impact of papers produced by Katsuma Hayashi. 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 Katsuma Hayashi. The network helps show where Katsuma Hayashi may publish in the future.
Co-authorship network
The 20 scholars most cited alongside Katsuma Hayashi, 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 | 2024 | 0 | |
| 2 | 2023 | 6 | |
| 3 | 2023 | 5 | |
| 4 | 2023 | 2 | |
| 5 | 2022 | 5 | |
| 6 | 2022 | 7 | |
| 7 | 2022 | 8 | |
| 8 | 2022 | 10 | |
| 9 | 2022 | 5 | |
| 10 | 2022 | 19 | |
| 11 | 2022 | 5 | |
| 12 | 2021 | 4 | |
| 13 | 2021 | 8 | |
| 14 | 2020 | 235 | |
| 15 | 2020 | 6 | |
| 16 | 2020 | 26 | |
| 17 | Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Databreakdown → | 2020 | 799 |
| 18 | 2020 | 135 | |
| 19 | 2008 | 15 |
About Katsuma Hayashi
Katsuma Hayashi is a scholar working on Modeling and Simulation, Infectious Diseases and Health, Toxicology and Mutagenesis, having authored 19 papers that have together received 1.3k indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (12 papers), SARS-CoV-2 and COVID-19 Research (5 papers), COVID-19 and healthcare impacts (4 papers), COVID-19 Pandemic Impacts (4 papers), COVID-19 Clinical Research Studies (4 papers), Viral Infections and Outbreaks Research (3 papers), Infection Control and Ventilation (3 papers) and Climate Change and Health Impacts (2 papers). The work is most often cited by research in Modeling and Simulation (879 citations), Infectious Diseases (584 citations) and Economics and Econometrics (363 citations). Katsuma Hayashi has collaborated with scholars based in Japan and United Kingdom. Frequent co-authors include Hiroshi Nishiura, Sung-mok Jung, T. Kobayashi, Andrei R. Akhmetzhanov, Natalie M. Linton, Yichi Yang, Ryo Kinoshita, Baoyin Yuan, Asami Anzai and Takeshi Miyama. Their work appears in journals such as Journal of Theoretical Biology, American Journal of Tropical Medicine and Hygiene and FEMS Microbiology Letters.
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.