Joseph T. Wu
- Modeling and Simulation top 0.01%
- Infectious Diseases top 0.1%
- Epidemiology top 0.5%
- Economics and Econometrics top 0.5%
- Agronomy and Crop Science top 0.5%
- Co-authors
- GM LeungKathy LeungBenjamin J. CowlingPeng WuEric H. Y. LauDi LiuSteven RileyVicky J. Fang
- Topics
- COVID-19 epidemiological studies (59 papers)Influenza Virus Research Studies (48 papers)SARS-CoV-2 and COVID-19 Research (29 papers)
- Partner nations
- Hong KongChinaUnited States
In The Last Decade
Joseph T. Wu
148 papers receiving 10.8k citations
Hit Papers
Peers
Comparison fields: 5 of 202
- Modeling and Simulation 4.9k
- Infectious Diseases 4.9k
- Epidemiology 3.0k
- Economics and Econometrics 1.6k
- Agronomy and Crop Science 1.0k
Countries citing papers authored by Joseph T. Wu
This map shows the geographic impact of Joseph T. Wu'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 Joseph T. Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joseph T. Wu more than expected).
Fields of papers citing papers by Joseph T. Wu
This network shows the impact of papers produced by Joseph T. Wu. 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 Joseph T. Wu. The network helps show where Joseph T. Wu may publish in the future.
Co-authorship network of co-authors of Joseph T. Wu
This figure shows the co-authorship network connecting the top 25 collaborators of Joseph T. Wu. A scholar is included among the top collaborators of Joseph T. Wu 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 Joseph T. Wu. Joseph T. Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 3 | |
| 3 | 21 | |
| 4 | 2 | |
| 5 | Real-world COVID-19 vaccine effectiveness against the Omicron BA.2 variant in a SARS-CoV-2 infection-naive populationbreakdown → | 116 |
| 6 | 16 | |
| 7 | Estimating the transmission dynamics of SARS-CoV-2 Omicron BF.7 in Beijing after adjustment of the zero-COVID policy in November–December 2022breakdown → | 107 |
| 8 | 2 | |
| 9 | 53 | |
| 10 | 30 | |
| 11 | 11 | |
| 12 | 7 | |
| 13 | 33 | |
| 14 | 2 | |
| 15 | 13 | |
| 16 | 5 | |
| 17 | 102 | |
| 18 | 1 | |
| 19 | 89 | |
| 20 | 27 |
About Joseph T. Wu
Joseph T. Wu is a scholar working on Modeling and Simulation, Infectious Diseases and Agronomy and Crop Science, having authored 153 papers that have together received 11.1k indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (59 papers), Influenza Virus Research Studies (48 papers) and SARS-CoV-2 and COVID-19 Research (29 papers). The work is most often cited by research in Modeling and Simulation (4.9k citations), Infectious Diseases (4.9k citations) and Agronomy and Crop Science (1.0k citations). Joseph T. Wu has collaborated with scholars based in Hong Kong, China and United States. Frequent co-authors include GM Leung, Kathy Leung, Benjamin J. Cowling, Peng Wu, Eric H. Y. Lau, Di Liu, Steven Riley, Vicky J. Fang, Hongjie Yu and Tommy Tsan‐Yuk Lam. Their work appears in journals such as New England Journal of Medicine, Proceedings of the National Academy of Sciences and The Lancet.
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.