Daniel Coombs
Impact in
- Modeling and Simulation top 1%
- COVID-19 epidemiological studies
- Immunology top 2%
- T-cell and B-cell Immunology
Papers in
-
- COVID-19 epidemiological studies 9
- Immunology 18
- T-cell and B-cell Immunology 17
- Co-authors
- Michael A. Gilchrist (5 shared papers)Byron Goldstein (11 shared papers)Omer Dushek (9 shared papers)Raibatak Das (8 shared papers)Carla Wofsy (4 shared papers)Michael J. Ward (3 shared papers)Fairooz F. Kabbinavar (1 shared paper)Dennis J. Slamon (1 shared paper)
- Journals
- PLoS Computational Biology (7 papers)Biophysical Journal (6 papers)Bulletin of Mathematical Biology (6 papers)Epidemics (4 papers)PLoS ONE (3 papers)
- Partner nations
- CanadaUnited StatesUnited Kingdom
In The Last Decade
Daniel Coombs
82 papers receiving 3.7k citations
Peers
Comparison fields: 5 of 137
- Modeling and Simulation 281
- Immunology 724
- Virology 123
- Public Health, Environmental and Occupational Health 875
- Oncology 527
Countries citing papers authored by Daniel Coombs
This map shows the geographic impact of Daniel Coombs'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 Daniel Coombs with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Coombs more than expected).
Fields of papers citing papers by Daniel Coombs
This network shows the impact of papers produced by Daniel Coombs. 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 Daniel Coombs. The network helps show where Daniel Coombs may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniel Coombs, 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 83 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 1999 | 584 | |
| 2 | 2010 | 195 | |
| 3 | 2019 | 174 | |
| 4 | 2005 | 136 | |
| 5 | 2004 | 135 | |
| 6 | 2005 | 124 | |
| 7 | 2007 | 114 | |
| 8 | 2009 | 111 | |
| 9 | 2009 | 94 | |
| 10 | 1999 | 91 | |
| 11 | 2002 | 88 | |
| 12 | 2004 | 88 | |
| 13 | 2022 | 87 | |
| 14 | 2018 | 83 | |
| 15 | 2012 | 72 | |
| 16 | 2004 | 71 | |
| 17 | 2015 | 69 | |
| 18 | 2020 | 68 | |
| 19 | 2011 | 63 | |
| 20 | 2014 | 63 |
About Daniel Coombs
Daniel Coombs is a scholar working on Modeling and Simulation, Immunology, Immunology and Allergy, Public Health, Environmental and Occupational Health and Infectious Diseases, having authored 83 papers that have together received 3.8k indexed citations. Recurring topics across this work include T-cell and B-cell Immunology (17 papers), Mathematical and Theoretical Epidemiology and Ecology Models (15 papers), COVID-19 epidemiological studies (9 papers), Cell Adhesion Molecules Research (9 papers), Cellular Mechanics and Interactions (7 papers), HIV/AIDS Research and Interventions (6 papers), SARS-CoV-2 and COVID-19 Research (5 papers) and Opioid Use Disorder Treatment (5 papers). The work is most often cited by research in Modeling and Simulation (281 citations), Immunology (724 citations), Virology (123 citations), Public Health, Environmental and Occupational Health (875 citations) and Oncology (527 citations). Daniel Coombs has collaborated with scholars based in Canada, United States and United Kingdom. Frequent co-authors include Michael A. Gilchrist, Byron Goldstein, Omer Dushek, Raibatak Das, Carla Wofsy, Michael J. Ward, Fairooz F. Kabbinavar, Dennis J. Slamon, Malgorzata Beryt and Mark Daniel Pegram. Their work appears in journals such as PLoS Computational Biology, Biophysical Journal, Bulletin of Mathematical Biology, Epidemics and PLoS ONE.
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.