David Sirl
Impact in
- Modeling and Simulation top 1%
- COVID-19 epidemiological studies
-
- Complex Network Analysis Techniques
- Opinion Dynamics and Social Influence
Papers in
-
- COVID-19 epidemiological studies 14
-
- Complex Network Analysis Techniques 13
- Opinion Dynamics and Social Influence 8
- Co-authors
- Frank BallPieter TrapmanTom BrittonJoshua V. RossThomas HouseP. K. PollettHanjun ZhangIan Jones
- Journals
- Journal of Mathematical Biology (5 papers)Journal of Applied Probability (5 papers)Advances in Applied Probability (4 papers)Journal for Research in Mathematics Education (2 papers)Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences (1 paper)
- Partner nations
- United KingdomAustraliaSweden
In The Last Decade
David Sirl
23 papers receiving 377 citations
Peers
Comparison fields: 5 of 63
- Modeling and Simulation 196
- Statistical and Nonlinear Physics 203
- Mathematical Physics 47
- Public Health, Environmental and Occupational Health 133
- General Energy 2
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-authorship network
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
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2023 | 2 | |
| 3 | 2023 | 1 | |
| 4 | 2019 | 20 | |
| 5 | 2017 | 11 | |
| 6 | 2017 | 5 | |
| 7 | 2014 | 22 | |
| 8 | 2013 | 9 | |
| 9 | 2012 | 17 | |
| 10 | 2012 | 9 | |
| 11 | 2012 | 27 | |
| 12 | 2012 | 45 | |
| 13 | 2010 | 5 | |
| 14 | 2009 | 100 | |
| 15 | 2009 | 48 | |
| 16 | 2009 | 10 | |
| 17 | 2008 | 13 | |
| 18 | 2007 | 2 | |
| 19 | 2007 | 10 | |
| 20 | 2007 | 2 |
About David Sirl
David Sirl is a scholar working on Modeling and Simulation, Statistical and Nonlinear Physics, Statistics and Probability, Management Science and Operations Research and Management Information Systems, having authored 26 papers that have together received 391 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (14 papers), Complex Network Analysis Techniques (13 papers), Opinion Dynamics and Social Influence (8 papers), Mathematical and Theoretical Epidemiology and Ecology Models (4 papers), Advanced Queuing Theory Analysis (3 papers), Probability and Risk Models (3 papers), Markov Chains and Monte Carlo Methods (3 papers) and Influenza Virus Research Studies (2 papers). The work is most often cited by research in Modeling and Simulation (196 citations), Statistical and Nonlinear Physics (203 citations), Mathematical Physics (47 citations), Public Health, Environmental and Occupational Health (133 citations) and General Energy (2 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, Ian Jones and Hugh P. Possingham. 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 Proceedings of the Royal Society A Mathematical Physical and Engineering 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.