Jun Cai

103 papers receiving 3.7k citations

Jun Cai's Hit Papers

Modeling transmission of SARS-CoV-2 Omicron in China 2022 · 175 citations
1750+2+4Years since publication100200300400

Peers

Jun Cai
Comparison fields: 5 of 177
  • Health, Toxicology and Mutagenesis 924
  • Modeling and Simulation 279
  • Biological Psychiatry 135
  • Environmental Engineering 649
  • Atmospheric Science 704
Replace Valentina Bollati with:
Valentina Bollati Italy
E. Andrés Houseman United States
Min Xu China
Robyn Lucas Australia
Paola Palestini Italy
Myles Cockburn United States
Pei Li China
Marc Chadeau‐Hyam United Kingdom
Yongjie Wei China
Wenhao Zhou China
Jun Cai relative to Valentina Bollati Italy Valentina Bollati's profile →
Citations per field
00.5×7.3×
Valentina Bollati · 1×
Citations per year

Countries citing papers authored by Jun Cai

Since Specialization
Citations

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

Fields of papers citing papers by Jun Cai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 114 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism
Hit paper breakdown →
2020481
2 2006289
3 2018198
4
Hypofrontality and negative symptoms in major depressive disorder.
1998198
5
Modeling transmission of SARS-CoV-2 Omicron in China
Hit paper breakdown →
2022175
6 2017143
7 2007113
8 201498
9 200697
10 200983
11 200982
12 201680
13 201768
14 202165
15 201859
16 202158
17 202058
18 202057
19 201354
20 201754

About Jun Cai

Jun Cai is a scholar working on Molecular Biology, Modeling and Simulation, Infectious Diseases, Epidemiology and Genetics, having authored 114 papers that have together received 3.8k indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (17 papers), Atmospheric chemistry and aerosols (11 papers), Air Quality and Health Impacts (9 papers), Influenza Virus Research Studies (8 papers), SARS-CoV-2 and COVID-19 Research (8 papers), Genetics and Neurodevelopmental Disorders (6 papers), Schizophrenia research and treatment (6 papers) and Air Quality Monitoring and Forecasting (6 papers). The work is most often cited by research in Health, Toxicology and Mutagenesis (924 citations), Modeling and Simulation (279 citations), Biological Psychiatry (135 citations), Environmental Engineering (649 citations) and Atmospheric Science (704 citations). Jun Cai has collaborated with scholars based in China, United States and Belgium. Frequent co-authors include Bing Xu, Dwight A. Towler, Ziyue Chen, Jian-Su Shao, Bingbo Gao, Bin He, Danlu Chen, Xiaoming Xie, Yan Zhuang and Бин Чэн. Their work appears in journals such as Scientific Reports, Psychopharmacology, Nature Communications, Enzyme and Microbial Technology and International Journal of Environmental Research and Public Health.

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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