Kaifa Wang

1.8k citations
82 papers · 1.3k indexed · h-index 16

Impact in

Papers in

Kaifa Wang

77 papers receiving 1.3k citations

Peers

Kaifa Wang
Comparison fields: 5 of 129
  • Modeling and Simulation 617
  • Public Health, Environmental and Occupational Health 986
  • Genetics 611
  • Virology 72
  • Hepatology 116
Replace Cruz Vargas‐De‐León with:
Cruz Vargas‐De‐León Mexico
Hisashi Inaba Japan
Xia Wang China
Bruno Buonomo Italy
Abdessamad Tridane United Arab Emirates
Shingo Iwami Japan
Xiao-Qiang Zhao China
H. W. Hethcote United States
Naveen K. Vaidya United States
Yu Yang China
Kaifa Wang relative to Cruz Vargas‐De‐León Mexico Cruz Vargas‐De‐León's profile →
Citations per field
00.5×5.5×
Cruz Vargas‐De‐León · 1×
Citations per year

Countries citing papers authored by Kaifa Wang

Since Specialization
Citations

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

Fields of papers citing papers by Kaifa Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Kaifa Wang, 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 Kaifa Wang Line = papers co-authored together Kaifa Wang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 82 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2007169
2 2007140
3 2007115
4 2009106
5 200677
6 201570
7 201269
8 201667
9 200562
10 201146
11 201733
12 202031
13 201323
14 200817
15 202016
16 201416
17 200915
18 201414
19 200314
20 201112

About Kaifa Wang

Kaifa Wang is a scholar working on Public Health, Environmental and Occupational Health, Modeling and Simulation, Genetics, Epidemiology and Hepatology, having authored 82 papers that have together received 1.3k indexed citations. Recurring topics across this work include Mathematical and Theoretical Epidemiology and Ecology Models (39 papers), Evolution and Genetic Dynamics (27 papers), COVID-19 epidemiological studies (22 papers), Hepatitis B Virus Studies (13 papers), Hepatitis C virus research (9 papers), Mathematical Biology Tumor Growth (7 papers), Geology and Paleoclimatology Research (6 papers) and Evolutionary Game Theory and Cooperation (5 papers). The work is most often cited by research in Modeling and Simulation (617 citations), Public Health, Environmental and Occupational Health (986 citations), Genetics (611 citations), Virology (72 citations) and Hepatology (116 citations). Kaifa Wang has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Wendi Wang, Xianning Liu, Ángela Torres, Shiping Song, Weiming Wang, Yongli Cai, Yali Yang, Jianquan Li, Zhenqing Li and Mingjiang Wu. Their work appears in journals such as Mathematical Biosciences & Engineering, Journal of Theoretical Biology, PLoS ONE, Nonlinear Analysis Real World Applications and Mathematical Biosciences.

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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