Han Fu
Impact in
- Infectious Diseases top 5%
- Tuberculosis Research and Epidemiology
- COVID-19 Clinical Research Studies
- Modeling and Simulation top 5%
- COVID-19 epidemiological studies
Papers in
- Epidemiology 10
- Virology and Viral Diseases 5
- Pneumocystis jirovecii pneumonia detection and treatment 3
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- Tuberculosis Research and Epidemiology 4
- Co-authors
- Hsien-Ho Lin (5 shared papers)Nimalan Arinaminpathy (5 shared papers)David W. Dowdy (2 shared papers)Carel Pretorius (2 shared papers)Lucia Cilloni (2 shared papers)Enos Masini (2 shared papers)Sreenivas Achuthan Nair (2 shared papers)Juan F Vesga (2 shared papers)
- Journals
- Vaccines (2 papers)Annals of Translational Medicine (2 papers)Vaccine (2 papers)BMC Medicine (1 paper)Mycoses (1 paper)
- Partner nations
- ChinaUnited KingdomUnited States
In The Last Decade
Han Fu
38 papers receiving 679 citations
Peers
Comparison fields: 5 of 101
- Infectious Diseases 317
- Modeling and Simulation 41
- Pharmaceutical Science 44
- Applied Microbiology and Biotechnology 10
- Epidemiology 136
Countries citing papers authored by Han Fu
This map shows the geographic impact of Han Fu'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 Han Fu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Han Fu more than expected).
Fields of papers citing papers by Han Fu
This network shows the impact of papers produced by Han Fu. 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 Han Fu. The network helps show where Han Fu may publish in the future.
Co-authors
The 25 scholars most cited alongside Han Fu, 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 40 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 207 | |
| 2 | 2016 | 75 | |
| 3 | 2017 | 63 | |
| 4 | 2014 | 55 | |
| 5 | 2021 | 32 | |
| 6 | 2022 | 30 | |
| 7 | 2018 | 29 | |
| 8 | 2024 | 27 | |
| 9 | 2019 | 18 | |
| 10 | 2019 | 16 | |
| 11 | 2020 | 15 | |
| 12 | 2023 | 12 | |
| 13 | 2021 | 12 | |
| 14 | 2023 | 11 | |
| 15 | 2020 | 9 | |
| 16 | 2017 | 9 | |
| 17 | 2020 | 9 | |
| 18 | 2020 | 7 | |
| 19 | 2021 | 7 | |
| 20 | 2021 | 7 |
About Han Fu
Han Fu is a scholar working on Epidemiology, Infectious Diseases, Molecular Biology, Nephrology and Health, having authored 40 papers that have together received 694 indexed citations. Recurring topics across this work include Virology and Viral Diseases (5 papers), Vaccine Coverage and Hesitancy (4 papers), Tuberculosis Research and Epidemiology (4 papers), Acute Kidney Injury Research (4 papers), COVID-19 epidemiological studies (3 papers), Advancements in Transdermal Drug Delivery (3 papers), Pneumocystis jirovecii pneumonia detection and treatment (3 papers) and Silk-based biomaterials and applications (2 papers). The work is most often cited by research in Infectious Diseases (317 citations), Modeling and Simulation (41 citations), Pharmaceutical Science (44 citations), Applied Microbiology and Biotechnology (10 citations) and Epidemiology (136 citations). Han Fu has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Hsien-Ho Lin, Nimalan Arinaminpathy, David W. Dowdy, Carel Pretorius, Lucia Cilloni, Enos Masini, Sreenivas Achuthan Nair, Juan F Vesga, Suvanand Sahu and Sevim Ahmedov. Their work appears in journals such as Vaccines, Annals of Translational Medicine, Vaccine, BMC Medicine and Mycoses.
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.