Lan Yi

972 citations
6 papers · 23 · h-index 4

Impact in

Papers in

    • SARS-CoV-2 and COVID-19 Research 4
    • Viral Infections and Outbreaks Research 2
    • SARS-CoV-2 detection and testing 1
    • Viral Infections and Vectors 1
    • Viral gastroenteritis research and epidemiology 1
    • COVID-19 Clinical Research Studies 1
    • COVID-19 epidemiological studies 5

Lan Yi

4 papers receiving 22 citations

Peers

Lan Yi
Comparison fields: 5 of 12
  • Modeling and Simulation 18
  • Infectious Diseases 14
  • Health 4
  • Epidemiology 7
  • Clinical Psychology 2
Replace Constanze Ciavarella with:
Constanze Ciavarella United Kingdom
Cécé Kpamou France
Sarah Lapidus United States
Dirk Douwes‐Schultz Canada
Hamad Eid Al Romaihi Qatar
Michele Nicoletti Italy
Irasha Harding United Kingdom
Owen Janson United States
Addison J. Hu United States
C. Omuemu Nigeria
Lan Yi relative to Constanze Ciavarella United Kingdom Constanze Ciavarella's profile →
Citations per field
00.5×1.5×
Constanze Ciavarella · 1×
Citations per year

Countries citing papers authored by Lan Yi

Since Specialization
Citations

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

Fields of papers citing papers by Lan Yi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Lan Yi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Lan Yi Line = papers co-authored together Lan Yi links everyone, so they are left out of the graph.

All Works

6 of 6 papers shown
#Work
1 202211
2 20244
3 20244
4 20233
5 20231
6 20250

About Lan Yi

Lan Yi is a scholar working on Infectious Diseases, Modeling and Simulation, Epidemiology, Organic Chemistry and Surgery, having authored 6 papers that have together received 23 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (5 papers), SARS-CoV-2 and COVID-19 Research (4 papers), Viral Infections and Outbreaks Research (2 papers), SARS-CoV-2 detection and testing (1 paper), Respiratory viral infections research (1 paper), Viral Infections and Vectors (1 paper), Viral gastroenteritis research and epidemiology (1 paper) and COVID-19 Clinical Research Studies (1 paper). The work is most often cited by research in Modeling and Simulation (18 citations), Infectious Diseases (14 citations), Health (4 citations), Epidemiology (7 citations) and Clinical Psychology (2 citations). Lan Yi has collaborated with scholars based in China, United States and South Korea. Frequent co-authors include Marco Ajelli, Hongjie Yu, Yan Wang, Kaiyuan Sun, Hengcong Liu, Zhaomin Feng, Cécile Viboud, Quanyi Wang, Helen Y. Chu and Ziyan Liu. Their work appears in journals such as China CDC Weekly, Infectious Disease Modelling, Clinical Microbiology and Infection, Proceedings of the National Academy of Sciences and BMC Medicine.

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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