David Sirl
Impact in
- Modeling and Simulation top 1%
- COVID-19 epidemiological studies
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- Complex Network Analysis Techniques
- Opinion Dynamics and Social Influence
Papers in
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- Complex Network Analysis Techniques 14
- Opinion Dynamics and Social Influence 9
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- COVID-19 epidemiological studies 14
- Co-authors
- Frank Ball (14 shared papers)Pieter Trapman (6 shared papers)Tom Britton (4 shared papers)Joshua V. Ross (2 shared papers)Thomas House (1 shared paper)Frank Ball (2 shared papers)P. K. Pollett (4 shared papers)Hanjun Zhang (3 shared papers)
- Journals
- Journal of Mathematical Biology (6 papers)Journal of Applied Probability (5 papers)Advances in Applied Probability (4 papers)Journal for Research in Mathematics Education (2 papers)Mathematical Biosciences (1 paper)
- Partner nations
- United KingdomAustraliaSweden
In The Last Decade
David Sirl
23 papers receiving 380 citations
Peers
Comparison fields: 5 of 62
- Modeling and Simulation 193
- Statistical and Nonlinear Physics 204
- Public Health, Environmental and Occupational Health 135
- Mathematical Physics 47
- Infectious Diseases 35
Countries citing papers authored by David Sirl
This map shows the geographic impact of David Sirl'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 David Sirl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Sirl more than expected).
Fields of papers citing papers by David Sirl
This network shows the impact of papers produced by David Sirl. 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 David Sirl. The network helps show where David Sirl may publish in the future.
Co-authors
The 18 scholars most cited alongside David Sirl, 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 27 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2009 | 99 | |
| 2 | 2009 | 49 | |
| 3 | 2012 | 45 | |
| 4 | 2012 | 27 | |
| 5 | 2014 | 23 | |
| 6 | 2019 | 20 | |
| 7 | 2018 | 18 | |
| 8 | 2012 | 17 | |
| 9 | 2008 | 13 | |
| 10 | 2017 | 12 | |
| 11 | 2007 | 10 | |
| 12 | 2009 | 10 | |
| 13 | 2019 | 9 | |
| 14 | 2013 | 9 | |
| 15 | 2012 | 9 | |
| 16 | 2017 | 5 | |
| 17 | 2010 | 5 | |
| 18 | 2013 | 3 | |
| 19 | 2023 | 2 | |
| 20 | 2007 | 2 |
About David Sirl
David Sirl is a scholar working on Statistical and Nonlinear Physics, Modeling and Simulation, Public Health, Environmental and Occupational Health, Management Science and Operations Research and Statistics and Probability, having authored 27 papers that have together received 393 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (14 papers), Complex Network Analysis Techniques (14 papers), Opinion Dynamics and Social Influence (9 papers), Mathematical and Theoretical Epidemiology and Ecology Models (5 papers), Advanced Queuing Theory Analysis (3 papers), Markov Chains and Monte Carlo Methods (3 papers), Probability and Risk Models (3 papers) and Online and Blended Learning (2 papers). The work is most often cited by research in Modeling and Simulation (193 citations), Statistical and Nonlinear Physics (204 citations), Public Health, Environmental and Occupational Health (135 citations), Mathematical Physics (47 citations) and Infectious Diseases (35 citations). David Sirl has collaborated with scholars based in United Kingdom, Australia and Sweden. Frequent co-authors include Frank Ball, Pieter Trapman, Tom Britton, Joshua V. Ross, Thomas House, Frank Ball, P. K. Pollett, Hanjun Zhang, Hugh P. Possingham and Ian Jones. Their work appears in journals such as Journal of Mathematical Biology, Journal of Applied Probability, Advances in Applied Probability, Journal for Research in Mathematics Education and Mathematical Biosciences.
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.