G. David Williamson
- Epidemiology top 5%
- Public Health, Environmental and Occupational Health top 5%
- Surgery top 10%
- Cardiology and Cardiovascular Medicine top 5%
- Endocrinology, Diabetes and Metabolism top 5%
- Co-authors
- Donna F. StroupPaul GardAntonie W. VoorsSathanur R. SrinivasanJames L. CresantaDavid S. FreedmanLarry S. WebberGerald S. Berenson
- Topics
- Data-Driven Disease Surveillance (7 papers)Influenza Virus Research Studies (5 papers)Statistical Methods and Bayesian Inference (2 papers)
- Partner nations
- United StatesBrazilUnited Kingdom
In The Last Decade
G. David Williamson
16 papers receiving 2.2k citations
Hit Papers
Peers
Comparison fields: 5 of 159
- Epidemiology 758
- Public Health, Environmental and Occupational Health 517
- Surgery 358
- Cardiology and Cardiovascular Medicine 341
- Endocrinology, Diabetes and Metabolism 334
Countries citing papers authored by G. David Williamson
This map shows the geographic impact of G. David Williamson'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 G. David Williamson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites G. David Williamson more than expected).
Fields of papers citing papers by G. David Williamson
This network shows the impact of papers produced by G. David Williamson. 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 G. David Williamson. The network helps show where G. David Williamson may publish in the future.
Co-authorship network of co-authors of G. David Williamson
This figure shows the co-authorship network connecting the top 25 collaborators of G. David Williamson. A scholar is included among the top collaborators of G. David Williamson 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 G. David Williamson. G. David Williamson is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 106 | |
| 3 | 6 | |
| 4 | Meta-analysis of Observational Studies in Epidemiology | 397 |
| 5 | 22 | |
| 6 | 2 | |
| 7 | 56 | |
| 8 | 4 | |
| 9 | 480 | |
| 10 | 69 | |
| 11 | 113 | |
| 12 | 11 | |
| 13 | 15 | |
| 14 | 28 | |
| 15 | 37 | |
| 16 | 108 | |
| 17 | Relation of Serum Lipoprotein Levels and Systolic Blood Pressure to Early Atherosclerosisbreakdown → | 836 |
About G. David Williamson
G. David Williamson is a scholar working on Statistics, Probability and Uncertainty, Statistics and Probability and Modeling and Simulation, having authored 17 papers that have together received 2.3k indexed citations. Recurring topics across this work include Data-Driven Disease Surveillance (7 papers), Influenza Virus Research Studies (5 papers) and Statistical Methods and Bayesian Inference (2 papers). The work is most often cited by research in Modeling and Simulation (229 citations), Epidemiology (758 citations) and Endocrinology, Diabetes and Metabolism (334 citations). G. David Williamson has collaborated with scholars based in United States, Brazil and United Kingdom. Frequent co-authors include Donna F. Stroup, Paul Gard, Antonie W. Voors, Sathanur R. Srinivasan, James L. Cresanta, David S. Freedman, Larry S. Webber, Gerald S. Berenson, William P. Newman and Nancy H. Arden. Their work appears in journals such as New England Journal of Medicine, Cancer and American Journal of Epidemiology.
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.