Rob Deardon
- Modeling and Simulation top 0.5%
- COVID-19 epidemiological studies 36
- Agronomy and Crop Science top 1%
- Animal Disease Management and Epidemiology 29
- Small Animals top 5%
- Animal Behavior and Welfare Studies 7
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- Vector-Borne Animal Diseases 8
- Microbiology top 10%
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- Statistical Methods and Bayesian Inference 9
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- Influenza Virus Research Studies 8
- Data-Driven Disease Surveillance 8
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- Spatial and Panel Data Analysis 5
- Co-authors
- Stephen P. BrooksMark WoolhouseMichael J. TildesleyNicholas J. SavillBryan T. GrenfellMatt J. KeelingDarren J. ShawTal Avgar
- Journals
- Spatial and Spatio-temporal Epidemiology (8 papers)Transboundary and Emerging Diseases (3 papers)Canadian Journal of Statistics (3 papers)
- Partner nations
- CanadaUnited StatesUnited Kingdom
In The Last Decade
Rob Deardon
76 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 130
- Modeling and Simulation 347
- Agronomy and Crop Science 427
- Small Animals 114
- Ecology, Evolution, Behavior and Systematics 195
- Microbiology 61
Countries citing papers authored by Rob Deardon
This map shows the geographic impact of Rob Deardon'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 Rob Deardon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rob Deardon more than expected).
Fields of papers citing papers by Rob Deardon
This network shows the impact of papers produced by Rob Deardon. 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 Rob Deardon. The network helps show where Rob Deardon may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Rob Deardon, 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 | 2024 | 0 | |
| 2 | 2023 | 2 | |
| 3 | 2023 | 3 | |
| 4 | 2023 | 1 | |
| 5 | 2023 | 6 | |
| 6 | 2022 | 1 | |
| 7 | 2021 | 2 | |
| 8 | 2020 | 14 | |
| 9 | 2020 | 19 | |
| 10 | 2019 | 4 | |
| 11 | 2019 | 9 | |
| 12 | 2019 | 1 | |
| 13 | 2019 | 6 | |
| 14 | 2018 | 0 | |
| 15 | 2018 | 16 | |
| 16 | 2018 | 42 | |
| 17 | 2013 | 5 | |
| 18 | 2011 | 4 | |
| 19 | 2008 | 32 | |
| 20 | 2004 | 2 |
About Rob Deardon
Rob Deardon is a scholar working on Modeling and Simulation, Agronomy and Crop Science and Statistics and Probability, having authored 79 papers that have together received 1.1k indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (36 papers), Animal Disease Management and Epidemiology (29 papers), Statistical Methods and Bayesian Inference (9 papers), Influenza Virus Research Studies (8 papers), Data-Driven Disease Surveillance (8 papers), Vector-Borne Animal Diseases (8 papers), Animal Behavior and Welfare Studies (7 papers) and Spatial and Panel Data Analysis (5 papers). The work is most often cited by research in Modeling and Simulation (347 citations), Agronomy and Crop Science (427 citations) and Small Animals (114 citations). Rob Deardon has collaborated with scholars based in Canada, United States and United Kingdom. Frequent co-authors include Stephen P. Brooks, Mark Woolhouse, Michael J. Tildesley, Nicholas J. Savill, Bryan T. Grenfell, Matt J. Keeling, Darren J. Shaw, Tal Avgar, John M. Fryxell and Zvonimir Poljak. Their work appears in journals such as Spatial and Spatio-temporal Epidemiology, Transboundary and Emerging Diseases, Canadian Journal of Statistics, PLoS ONE and Computers and Electronics in Agriculture.
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.