Paul Birrell
Impact in
- Modeling and Simulation top 1%
- COVID-19 epidemiological studies
- Infectious Diseases top 5%
- HIV/AIDS Research and Interventions
- SARS-CoV-2 and COVID-19 Research
- SARS-CoV-2 detection and testing
Papers in
- Epidemiology 22
- Influenza Virus Research Studies 14
- Data-Driven Disease Surveillance 10
- HIV, Drug Use, Sexual Risk 3
-
- COVID-19 epidemiological studies 19
- Co-authors
- Daniela De Angelis (27 shared papers)Anne M. Presanis (7 shared papers)Richard Pebody (9 shared papers)André Charlett (8 shared papers)Valérie Delpech (4 shared papers)Xu‐Sheng Zhang (6 shared papers)Thomas House (4 shared papers)Alison Brown (3 shared papers)
- Journals
- Journal of the Royal Statistical Society Series A (Statistics in Society) (2 papers)Journal of Theoretical Biology (2 papers)BMC Public Health (2 papers)Epidemics (2 papers)Proceedings of the National Academy of Sciences (2 papers)
- Partner nations
- United KingdomUnited StatesNetherlands
In The Last Decade
Paul Birrell
28 papers receiving 673 citations
Peers
Comparison fields: 5 of 93
- Modeling and Simulation 327
- Infectious Diseases 323
- Virology 71
- Epidemiology 384
- Health 28
Countries citing papers authored by Paul Birrell
This map shows the geographic impact of Paul Birrell'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 Paul Birrell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paul Birrell more than expected).
Fields of papers citing papers by Paul Birrell
This network shows the impact of papers produced by Paul Birrell. 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 Paul Birrell. The network helps show where Paul Birrell may publish in the future.
Co-authors
The 25 scholars most cited alongside Paul Birrell, 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 30 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2011 | 94 | |
| 2 | 2013 | 90 | |
| 3 | 2021 | 65 | |
| 4 | 2011 | 60 | |
| 5 | 2021 | 57 | |
| 6 | 2013 | 51 | |
| 7 | 2014 | 49 | |
| 8 | 2014 | 36 | |
| 9 | 2017 | 23 | |
| 10 | 2022 | 21 | |
| 11 | 2022 | 18 | |
| 12 | 2014 | 17 | |
| 13 | 2022 | 16 | |
| 14 | 2021 | 13 | |
| 15 | 2012 | 10 | |
| 16 | 2017 | 8 | |
| 17 | 2020 | 8 | |
| 18 | 2017 | 8 | |
| 19 | 2014 | 8 | |
| 20 | 2016 | 7 |
About Paul Birrell
Paul Birrell is a scholar working on Epidemiology, Modeling and Simulation, Infectious Diseases, Artificial Intelligence and Public Health, Environmental and Occupational Health, having authored 30 papers that have together received 686 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (19 papers), Influenza Virus Research Studies (14 papers), Data-Driven Disease Surveillance (10 papers), HIV/AIDS Research and Interventions (3 papers), HIV, Drug Use, Sexual Risk (3 papers), SARS-CoV-2 and COVID-19 Research (3 papers), Bayesian Methods and Mixture Models (3 papers) and HIV Research and Treatment (2 papers). The work is most often cited by research in Modeling and Simulation (327 citations), Infectious Diseases (323 citations), Virology (71 citations), Epidemiology (384 citations) and Health (28 citations). Paul Birrell has collaborated with scholars based in United Kingdom, United States and Netherlands. Frequent co-authors include Daniela De Angelis, Anne M. Presanis, Richard Pebody, André Charlett, Valérie Delpech, Xu‐Sheng Zhang, Thomas House, Alison Brown, Tim Chadborn and Nick Gent. Their work appears in journals such as Journal of the Royal Statistical Society Series A (Statistics in Society), Journal of Theoretical Biology, BMC Public Health, Epidemics and Proceedings of the National Academy of Sciences.
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.