Chong You
Impact in
- Modeling and Simulation top 1%
- COVID-19 epidemiological studies
- Infectious Diseases top 5%
- SARS-CoV-2 and COVID-19 Research
- COVID-19 Clinical Research Studies
Papers in
-
- COVID-19 epidemiological studies 9
-
- Bayesian Methods and Mixture Models 3
- Co-authors
- Xiao‐Hua Zhou (12 shared papers)Qiushi Lin (4 shared papers)Cheng Heng Pang (5 shared papers)Yuan Zhang (5 shared papers)Wenjie Hu (3 shared papers)Shicheng Yu (1 shared paper)Jiarui Sun (3 shared papers)Jing Qin (1 shared paper)
- Journals
- China CDC Weekly (3 papers)Nature Communications (1 paper)Signal Transduction and Targeted Therapy (1 paper)Life (1 paper)International Journal of Infectious Diseases (1 paper)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Chong You
23 papers receiving 792 citations
Peers
Comparison fields: 5 of 114
- Modeling and Simulation 324
- Infectious Diseases 239
- General Dentistry 10
- Health 42
- Statistics and Probability 38
Countries citing papers authored by Chong You
This map shows the geographic impact of Chong You'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 Chong You with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chong You more than expected).
Fields of papers citing papers by Chong You
This network shows the impact of papers produced by Chong You. 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 Chong You. The network helps show where Chong You may publish in the future.
Co-authors
The 25 scholars most cited alongside Chong You, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 189 | |
| 2 | 2020 | 171 | |
| 3 | 2020 | 142 | |
| 4 | 2021 | 73 | |
| 5 | 2021 | 61 | |
| 6 | 2017 | 27 | |
| 7 | 2020 | 25 | |
| 8 | 2014 | 21 | |
| 9 | 2021 | 21 | |
| 10 | 2020 | 15 | |
| 11 | 2022 | 9 | |
| 12 | 2022 | 8 | |
| 13 | 2014 | 7 | |
| 14 | 2020 | 7 | |
| 15 | Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction | 2020 | 6 |
| 16 | 2021 | 6 | |
| 17 | 2025 | 5 | |
| 18 | 2020 | 5 | |
| 19 | 2020 | 4 | |
| 20 | 2022 | 2 |
About Chong You
Chong You is a scholar working on Modeling and Simulation, Artificial Intelligence, Infectious Diseases, Oncology and Statistics and Probability, having authored 28 papers that have together received 808 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (9 papers), Statistical Methods and Bayesian Inference (4 papers), SARS-CoV-2 and COVID-19 Research (4 papers), COVID-19 Pandemic Impacts (3 papers), Statistical Methods and Inference (3 papers), COVID-19 Clinical Research Studies (3 papers), Bayesian Methods and Mixture Models (3 papers) and Breast Cancer Treatment Studies (2 papers). The work is most often cited by research in Modeling and Simulation (324 citations), Infectious Diseases (239 citations), General Dentistry (10 citations), Health (42 citations) and Statistics and Probability (38 citations). Chong You has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Xiao‐Hua Zhou, Qiushi Lin, Cheng Heng Pang, Yuan Zhang, Wenjie Hu, Shicheng Yu, Jiarui Sun, Jing Qin, Tao Wu and Haitao Zhao. Their work appears in journals such as China CDC Weekly, Nature Communications, Signal Transduction and Targeted Therapy, Life and International Journal of 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.