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
-
- COVID-19 epidemiological studies 17
- Epidemiology 14
- Influenza Virus Research Studies 10
- Data-Driven Disease Surveillance 7
- Co-authors
- Daniela De Angelis (28 shared papers)Anne M. Presanis (7 shared papers)Richard Pebody (9 shared papers)André Charlett (8 shared papers)Valérie Delpech (4 shared papers)Thomas House (4 shared papers)Xu‐Sheng Zhang (6 shared papers)Alison Brown (3 shared papers)
- Journals
- Journal of the Royal Statistical Society Series A (Statistics in Society) (2 papers)BMC Public Health (2 papers)Journal of Theoretical Biology (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 703 citations
Peers
Comparison fields: 5 of 89
- Modeling and Simulation 292
- Infectious Diseases 259
- Virology 48
- Epidemiology 289
- Health 21
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 31 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2011 | 97 | |
| 2 | 2013 | 91 | |
| 3 | 2021 | 68 | |
| 4 | 2011 | 62 | |
| 5 | 2021 | 57 | |
| 6 | 2013 | 53 | |
| 7 | 2014 | 53 | |
| 8 | 2014 | 36 | |
| 9 | 2022 | 27 | |
| 10 | 2017 | 23 | |
| 11 | 2014 | 19 | |
| 12 | 2022 | 19 | |
| 13 | 2022 | 19 | |
| 14 | 2021 | 15 | |
| 15 | 2012 | 10 | |
| 16 | 2017 | 8 | |
| 17 | 2020 | 8 | |
| 18 | 2017 | 8 | |
| 19 | 2014 | 8 | |
| 20 | 2020 | 7 |
About Paul Birrell
Paul Birrell is a scholar working on Modeling and Simulation, Epidemiology, Infectious Diseases, Artificial Intelligence and Public Health, Environmental and Occupational Health, having authored 31 papers that have together received 717 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (17 papers), Influenza Virus Research Studies (10 papers), Data-Driven Disease Surveillance (7 papers), HIV/AIDS Research and Interventions (3 papers), Bayesian Methods and Mixture Models (3 papers), Census and Population Estimation (2 papers), Zoonotic diseases and public health (2 papers) and COVID-19 and healthcare impacts (2 papers). The work is most often cited by research in Modeling and Simulation (292 citations), Infectious Diseases (259 citations), Virology (48 citations), Epidemiology (289 citations) and Health (21 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, Thomas House, Xu‐Sheng Zhang, Alison Brown, Tim Chadborn and Edwin van Leeuwen. Their work appears in journals such as Journal of the Royal Statistical Society Series A (Statistics in Society), BMC Public Health, Journal of Theoretical Biology, 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.