Fred Lu
Impact in
- Modeling and Simulation top 2%
- COVID-19 epidemiological studies
- Health Informatics top 10%
Papers in
-
- Data-Driven Disease Surveillance 8
- Influenza Virus Research Studies 4
-
- COVID-19 epidemiological studies 6
- Co-authors
- Mauricio Santillana (9 shared papers)John S. Brownstein (2 shared papers)Matthew Biggerstaff (1 shared paper)Mohammad W. Hattab (1 shared paper)S. C. Kou (1 shared paper)Nicholas Brooke (1 shared paper)Shihao Yang (1 shared paper)Leonardo Clemente (4 shared papers)
- Journals
- PLoS Computational Biology (3 papers)JMIR Public Health and Surveillance (2 papers)Nature Communications (2 papers)PLoS neglected tropical diseases (1 paper)Journal of Luminescence (1 paper)
- Partner nations
- United StatesMexicoBelgium
In The Last Decade
Fred Lu
20 papers receiving 358 citations
Peers
Comparison fields: 5 of 87
- Modeling and Simulation 125
- Health Informatics 14
- Epidemiology 169
- Health Information Management 10
- Public Health, Environmental and Occupational Health 52
Countries citing papers authored by Fred Lu
This map shows the geographic impact of Fred Lu'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 Fred Lu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fred Lu more than expected).
Fields of papers citing papers by Fred Lu
This network shows the impact of papers produced by Fred Lu. 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 Fred Lu. The network helps show where Fred Lu may publish in the future.
Co-authors
The 25 scholars most cited alongside Fred Lu, 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 21 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 75 | |
| 2 | 2019 | 75 | |
| 3 | 2017 | 69 | |
| 4 | 2021 | 32 | |
| 5 | 2019 | 23 | |
| 6 | 2021 | 21 | |
| 7 | 2022 | 16 | |
| 8 | 2022 | 11 | |
| 9 | 2022 | 10 | |
| 10 | 2022 | 10 | |
| 11 | 1991 | 7 | |
| 12 | 2023 | 4 | |
| 13 | 2022 | 4 | |
| 14 | 2025 | 3 | |
| 15 | 2023 | 1 | |
| 16 | 2025 | 1 | |
| 17 | 2022 | 1 | |
| 18 | 1989 | 1 | |
| 19 | 2024 | 1 | |
| 20 | 2023 | 1 |
About Fred Lu
Fred Lu is a scholar working on Epidemiology, Modeling and Simulation, Artificial Intelligence, Molecular Biology and Public Health, Environmental and Occupational Health, having authored 21 papers that have together received 367 indexed citations. Recurring topics across this work include Data-Driven Disease Surveillance (8 papers), COVID-19 epidemiological studies (6 papers), Influenza Virus Research Studies (4 papers), Mosquito-borne diseases and control (3 papers), Gene expression and cancer classification (2 papers), Adversarial Robustness in Machine Learning (2 papers), Face and Expression Recognition (2 papers) and Anomaly Detection Techniques and Applications (2 papers). The work is most often cited by research in Modeling and Simulation (125 citations), Health Informatics (14 citations), Epidemiology (169 citations), Health Information Management (10 citations) and Public Health, Environmental and Occupational Health (52 citations). Fred Lu has collaborated with scholars based in United States, Mexico and Belgium. Frequent co-authors include Mauricio Santillana, John S. Brownstein, Matthew Biggerstaff, Mohammad W. Hattab, S. C. Kou, Nicholas Brooke, Shihao Yang, Leonardo Clemente, Manan Shah and Josh Gray. Their work appears in journals such as PLoS Computational Biology, JMIR Public Health and Surveillance, Nature Communications, PLoS neglected tropical diseases and Journal of Luminescence.
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.