T. Kobayashi

6.9k citations
39 papers · 2.0k indexed · 1 hit paper · h-index 15
Topics
COVID-19 epidemiological studies (13 papers)SARS-CoV-2 and COVID-19 Research (8 papers)COVID-19 Pandemic Impacts (6 papers)
Partner nations
JapanUnited StatesTaiwan

In The Last Decade

T. Kobayashi

39 papers receiving 1.9k citations

Hit Papers

Incubation Period and Other Epidemiological Characteristi...20202026202220242020250500750

Peers

T. Kobayashi
Comparison fields: 5 of 151
  • Modeling and Simulation 923
  • Infectious Diseases 814
  • Economics and Econometrics 366
  • Public Health, Environmental and Occupational Health 323
  • Atmospheric Science 285
Replace Michelle Kendall with:
Michelle Kendall United Kingdom
Chia C. Wang United States
Tauseef Ahmad China
Leonardo Soares Bastos Brazil
Taro Yamamoto Japan
Juliette Paireau France
Rachel Lowe United Kingdom
Michael Wilczek Germany
Nikolaos I. Stilianakis Germany
Samira Mubareka Canada
T. Kobayashi relative to Michelle Kendall United Kingdom Michelle Kendall's profile →
Citations per field
00.5×1.5×2.4×
Michelle Kendall · 1×
Citations per year

Countries citing papers authored by T. Kobayashi

Since Specialization
Citations

This map shows the geographic impact of T. Kobayashi'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 T. Kobayashi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. Kobayashi more than expected).

Fields of papers citing papers by T. Kobayashi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by T. Kobayashi. 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 T. Kobayashi. The network helps show where T. Kobayashi may publish in the future.

Co-authorship network of co-authors of T. Kobayashi

This figure shows the co-authorship network connecting the top 25 collaborators of T. Kobayashi. A scholar is included among the top collaborators of T. Kobayashi 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 T. Kobayashi. T. Kobayashi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 2
2 15
3 1
4 5
5 21
6 3
7 5
8 4
9 4
10 9
11 30
12 3
13 10
14 1
15 2
16 12
17 59
18 54
19 5
20 2

About T. Kobayashi

T. Kobayashi is a scholar working on Modeling and Simulation, Infectious Diseases and Atmospheric Science, having authored 39 papers that have together received 2.0k indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (13 papers), SARS-CoV-2 and COVID-19 Research (8 papers) and COVID-19 Pandemic Impacts (6 papers). The work is most often cited by research in Modeling and Simulation (923 citations), Infectious Diseases (814 citations) and Atmospheric Science (285 citations). T. Kobayashi has collaborated with scholars based in Japan, United States and Taiwan. Frequent co-authors include Hiroshi Nishiura, Andrei R. Akhmetzhanov, Natalie M. Linton, Sung-mok Jung, Katsuma Hayashi, Ryo Kinoshita, Yichi Yang, Baoyin Yuan, Yoshinori Furukawa and Yasuyuki Kato. Their work appears in journals such as PLoS ONE, Scientific Reports and Emerging infectious 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.

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