Natalie M. Linton

7.4k citations
28 papers · 2.1k indexed · 2 hit papers · h-index 11
Topics
COVID-19 epidemiological studies (17 papers)COVID-19 Pandemic Impacts (7 papers)SARS-CoV-2 and COVID-19 Research (6 papers)
Partner nations
JapanTaiwanUnited States

In The Last Decade

Natalie M. Linton

25 papers receiving 2.1k citations

Hit Papers

Incubation Period and Other Epidemiological Characteristi...202020262022202420202020250500750

Peers

Natalie M. Linton
Comparison fields: 5 of 141
  • Modeling and Simulation 1.4k
  • Infectious Diseases 892
  • Economics and Econometrics 563
  • Epidemiology 323
  • Clinical Psychology 206
Replace Andrei R. Akhmetzhanov with:
Andrei R. Akhmetzhanov Japan
Giulia Pullano France
Alberto Alexander Gayle Japan
Xingjie Hao China
Qianying Lin Hong Kong
Christine Tedijanto United States
Alexander E. Zarebski Australia
Katsuma Hayashi Japan
Affan Shoukat Canada
Natalie M. Linton relative to Andrei R. Akhmetzhanov Japan Andrei R. Akhmetzhanov's profile →
Citations per field
00.5×1.5×
Andrei R. Akhmetzhanov · 1×
Citations per year

Countries citing papers authored by Natalie M. Linton

Since Specialization
Citations

This map shows the geographic impact of Natalie M. Linton'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 Natalie M. Linton with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Natalie M. Linton more than expected).

Fields of papers citing papers by Natalie M. Linton

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Natalie M. Linton. 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 Natalie M. Linton. The network helps show where Natalie M. Linton may publish in the future.

Co-authorship network of co-authors of Natalie M. Linton

This figure shows the co-authorship network connecting the top 25 collaborators of Natalie M. Linton. A scholar is included among the top collaborators of Natalie M. Linton 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 Natalie M. Linton. Natalie M. Linton 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 0
2 0
3 9
4 7
5 5
6 3
7 9
8 8
9 4
10 10
11 4
12 3
13
Serial interval of novel coronavirus (COVID-19) infectionsbreakdown →
660
14 12
15 6
16 20
17 85
18 24
19 4
20 10

About Natalie M. Linton

Natalie M. Linton is a scholar working on Modeling and Simulation, Infectious Diseases and Economics and Econometrics, having authored 28 papers that have together received 2.1k indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (17 papers), COVID-19 Pandemic Impacts (7 papers) and SARS-CoV-2 and COVID-19 Research (6 papers). The work is most often cited by research in Modeling and Simulation (1.4k citations), Infectious Diseases (892 citations) and Economics and Econometrics (563 citations). Natalie M. Linton has collaborated with scholars based in Japan, Taiwan and United States. Frequent co-authors include Hiroshi Nishiura, Andrei R. Akhmetzhanov, Sung-mok Jung, Yichi Yang, Ryo Kinoshita, T. Kobayashi, Katsuma Hayashi, Baoyin Yuan, Tuhin Biswas and Md. Saimul Islam. Their work appears in journals such as PLoS ONE, American Journal of Public Health 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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