Chunna Ma

607 citations
39 papers · 463 indexed · h-index 14
Topics
Influenza Virus Research Studies (30 papers)Respiratory viral infections research (17 papers)COVID-19 epidemiological studies (10 papers)

In The Last Decade

Chunna Ma

35 papers receiving 455 citations

Peers

Chunna Ma
Comparison fields: 5 of 78
  • Epidemiology 330
  • Infectious Diseases 169
  • Modeling and Simulation 102
  • Agronomy and Crop Science 94
  • Health 92
Replace Alejandro Pérez with:
Alejandro Pérez United States
Lisa Menning Switzerland
Suizan Zhou China
Margot González-León Mexico
Pam Daily Kirley United States
Paul A. Biedrzycki United States
Clare Looker Australia
Marta Balinska France
T. D'Mello United States
Núria Soldevila Spain
Chunna Ma relative to Alejandro Pérez United States Alejandro Pérez's profile →
Citations per field
00.5×2.6×
Alejandro Pérez · 1×
Citations per year

Countries citing papers authored by Chunna Ma

Since Specialization
Citations

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

Fields of papers citing papers by Chunna Ma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chunna Ma

This figure shows the co-authorship network connecting the top 25 collaborators of Chunna Ma. A scholar is included among the top collaborators of Chunna Ma 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 Chunna Ma. Chunna Ma 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 4
3 1
4 3
5 4
6 0
7 17
8 2
9 1
10 15
11 24
12 20
13 11
14
[Technical guidelines for the application of seasonal influenza vaccine in China (2014-2015)].
9
15 13
16 11
17 33
18 35
19
[Influenza vaccination coverage rates and factors associated with vaccine use among the elderly in Beijing during 2008-2010].
4
20 29

About Chunna Ma

Chunna Ma is a scholar working on Modeling and Simulation, Epidemiology and Infectious Diseases, having authored 39 papers that have together received 463 indexed citations. Recurring topics across this work include Influenza Virus Research Studies (30 papers), Respiratory viral infections research (17 papers) and COVID-19 epidemiological studies (10 papers). The work is most often cited by research in Modeling and Simulation (102 citations), Health (92 citations) and Epidemiology (330 citations). Chunna Ma has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Quanyi Wang, Peng Yang, Shuangsheng Wu, Yi Zhang, Daitao Zhang, Li Zhang, Ying Sun, Haiyue Li, Yang Pan and Wei Duan. 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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