Rob Deardon
Impact in
- Modeling and Simulation top 0.5%
- COVID-19 epidemiological studies
- Agronomy and Crop Science top 1%
- Animal Disease Management and Epidemiology
Papers in
-
- COVID-19 epidemiological studies 38
-
- Animal Disease Management and Epidemiology 28
- Co-authors
- Nicholas J. Savill (6 shared papers)Bryan T. Grenfell (7 shared papers)Michael J. Tildesley (6 shared papers)Matt J. Keeling (7 shared papers)Stephen P. Brooks (6 shared papers)Mark Woolhouse (7 shared papers)Darren J. Shaw (6 shared papers)Zvonimir Poljak (6 shared papers)
- Journals
- Spatial and Spatio-temporal Epidemiology (8 papers)PLoS ONE (3 papers)Infectious Disease Modelling (3 papers)Transboundary and Emerging Diseases (3 papers)Canadian Journal of Statistics (3 papers)
- Partner nations
- CanadaUnited StatesUnited Kingdom
In The Last Decade
Rob Deardon
78 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 124
- Modeling and Simulation 366
- Agronomy and Crop Science 425
- Small Animals 104
- Microbiology 62
- Ecology, Evolution, Behavior and Systematics 188
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-authors
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
Showing the 20 most-cited of 81 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2006 | 199 | |
| 2 | INFERENCE FOR INDIVIDUAL-LEVEL MODELS OF INFECTIOUS DISEASES IN LARGE POPULATIONS. | 2010 | 85 |
| 3 | 2008 | 72 | |
| 4 | 2013 | 70 | |
| 5 | 2013 | 49 | |
| 6 | 2019 | 49 | |
| 7 | 2018 | 46 | |
| 8 | 2006 | 39 | |
| 9 | 2021 | 32 | |
| 10 | 2008 | 32 | |
| 11 | 2020 | 28 | |
| 12 | 2012 | 25 | |
| 13 | 2021 | 24 | |
| 14 | 2017 | 21 | |
| 15 | 2013 | 20 | |
| 16 | 2020 | 20 | |
| 17 | 2006 | 19 | |
| 18 | 2016 | 18 | |
| 19 | 2018 | 17 | |
| 20 | 2012 | 15 |
About Rob Deardon
Rob Deardon is a scholar working on Modeling and Simulation, Agronomy and Crop Science, Epidemiology, Genetics and Economics and Econometrics, having authored 81 papers that have together received 1.2k indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (38 papers), Animal Disease Management and Epidemiology (28 papers), Data-Driven Disease Surveillance (9 papers), Statistical Methods and Bayesian Inference (9 papers), Vector-Borne Animal Diseases (7 papers), Spatial and Panel Data Analysis (7 papers), Influenza Virus Research Studies (7 papers) and Animal Behavior and Welfare Studies (5 papers). The work is most often cited by research in Modeling and Simulation (366 citations), Agronomy and Crop Science (425 citations), Small Animals (104 citations), Microbiology (62 citations) and Ecology, Evolution, Behavior and Systematics (188 citations). Rob Deardon has collaborated with scholars based in Canada, United States and United Kingdom. Frequent co-authors include Nicholas J. Savill, Bryan T. Grenfell, Michael J. Tildesley, Matt J. Keeling, Stephen P. Brooks, Mark Woolhouse, Darren J. Shaw, Zvonimir Poljak, Tal Avgar and John M. Fryxell. Their work appears in journals such as Spatial and Spatio-temporal Epidemiology, PLoS ONE, Infectious Disease Modelling, Transboundary and Emerging Diseases and Canadian Journal of Statistics.
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.