Rob Deardon

2.1k total citations
79 papers, 1.1k citations indexed

About

Rob Deardon is a scholar working on Modeling and Simulation, Agronomy and Crop Science and Epidemiology. According to data from OpenAlex, Rob Deardon has authored 79 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Modeling and Simulation, 33 papers in Agronomy and Crop Science and 20 papers in Epidemiology. Recurrent topics in Rob Deardon's work include COVID-19 epidemiological studies (36 papers), Animal Disease Management and Epidemiology (29 papers) and Statistical Methods and Bayesian Inference (9 papers). Rob Deardon is often cited by papers focused on COVID-19 epidemiological studies (36 papers), Animal Disease Management and Epidemiology (29 papers) and Statistical Methods and Bayesian Inference (9 papers). Rob Deardon collaborates with scholars based in Canada, United States and United Kingdom. Rob Deardon's co-authors include Bryan T. Grenfell, Stephen P. Brooks, Nicholas J. Savill, Michael J. Tildesley, Matt J. Keeling, Mark Woolhouse, Darren J. Shaw, Tal Avgar, John M. Fryxell and Zvonimir Poljak and has published in prestigious journals such as Nature, Gastroenterology and PLoS ONE.

In The Last Decade

Rob Deardon

76 papers receiving 1.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Rob Deardon Canada 17 427 347 195 166 151 79 1.1k
Eve Miguel France 15 232 0.5× 291 0.8× 207 1.1× 138 0.8× 330 2.2× 29 914
Gareth Davies United Kingdom 15 419 1.0× 105 0.3× 284 1.5× 113 0.7× 109 0.7× 28 1.0k
Pauline Ezanno France 23 513 1.2× 128 0.4× 353 1.8× 264 1.6× 442 2.9× 84 1.5k
Richard Howey United Kingdom 14 303 0.7× 63 0.2× 149 0.8× 95 0.6× 263 1.7× 28 980
Lara Savini Italy 14 472 1.1× 111 0.3× 283 1.5× 95 0.6× 201 1.3× 41 826
Thibaud Porphyre United Kingdom 17 455 1.1× 52 0.1× 295 1.5× 123 0.7× 347 2.3× 56 887
Baoxu Huang China 16 405 0.9× 84 0.2× 87 0.4× 394 2.4× 307 2.0× 49 816
M.G. Garner Australia 23 977 2.3× 107 0.3× 633 3.2× 210 1.3× 259 1.7× 55 1.4k
Moh A. Alkhamis Kuwait 17 406 1.0× 60 0.2× 233 1.2× 170 1.0× 258 1.7× 49 774
Luke O’Grady Ireland 23 786 1.8× 227 0.7× 167 0.9× 314 1.9× 337 2.2× 84 1.8k

Countries citing papers authored by Rob Deardon

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of Rob Deardon

This figure shows the co-authorship network connecting the top 25 collaborators of Rob Deardon. A scholar is included among the top collaborators of Rob Deardon based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Rob Deardon. Rob Deardon is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Pardo, Jordi Pardo, Samuel Whittle, Rachelle Buchbinder, et al.. (2023). Crowd-sourcing and automation facilitated the identification and classification of randomized controlled trials in a living review. Journal of Clinical Epidemiology. 164. 1–8. 2 indexed citations
2.
Torabi, Mahmoud, et al.. (2023). Analyzing COVID-19 data in the Canadian province of Manitoba: A new approach. Spatial Statistics. 55. 100729–100729. 1 indexed citations
3.
Ward, Caitlin, Rob Deardon, & Alexandra M. Schmidt. (2023). Bayesian modeling of dynamic behavioral change during an epidemic. Infectious Disease Modelling. 8(4). 947–963. 6 indexed citations
4.
Deardon, Rob, et al.. (2021). Contact network uncertainty in individual level models of infectious disease transmission. PubMed. 13(1). 20190012–20190012. 2 indexed citations
5.
Singh, Balbir, Michael P. Ward, Mark Lowerison, et al.. (2021). Meta-analysis and adjusted estimation of COVID-19 case fatality risk in India and its association with the underlying comorbidities. One Health. 13. 100283–100283. 8 indexed citations
6.
Kwong, Grace P. S., Rob Deardon, Sylvia Checkley, et al.. (2021). Qiviut cortisol is associated with metrics of health and other intrinsic and extrinsic factors in wild muskoxen (Ovibos moschatus). Conservation Physiology. 10(1). coab103–coab103. 4 indexed citations
7.
Singh, Balbir, Mark Lowerison, Ryan T. Lewinson, et al.. (2020). Public health interventions slowed but did not halt the spread of COVID‐19 in India. Transboundary and Emerging Diseases. 68(4). 2171–2187. 14 indexed citations
8.
Deardon, Rob, et al.. (2020). Continuous Time Distance-Based and Network-Based Individual Level Models for Epidemics [R package EpiILMCT version 1.1.6]. 1 indexed citations
9.
Deardon, Rob, et al.. (2020). Geographically dependent individual-level models for infectious diseases transmission. Biostatistics. 23(1). 1–17. 11 indexed citations
10.
Deardon, Rob, et al.. (2019). Incorporating Contact Network Uncertainty in Individual Level Models of Infectious Disease using Approximate Bayesian Computation. The International Journal of Biostatistics. 16(1). 9 indexed citations
11.
12.
Deardon, Rob, Cheryl Barnabé, Vivian P. Bykerk, et al.. (2019). Joint Estimation of Remission and Response for Methotrexate‐Based DMARD Options in Rheumatoid Arthritis: A Bivariate Network Meta‐Analysis. ACR Open Rheumatology. 1(8). 471–479. 4 indexed citations
13.
Taylor, Graham W., et al.. (2019). Dynamic contact networks of swine movement in Manitoba, Canada: Characterization and implications for infectious disease spread. Transboundary and Emerging Diseases. 66(5). 1910–1919. 6 indexed citations
14.
15.
Deardon, Rob, et al.. (2018). Deep learning for supervised classification of spatial epidemics. Spatial and Spatio-temporal Epidemiology. 29. 187–198. 16 indexed citations
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17.
Deardon, Rob, et al.. (2013). Latent Conditional Individual-Level Models for Infectious Disease Modeling. The International Journal of Biostatistics. 9(1). 75–93. 5 indexed citations
18.
Poljak, Zvonimir, et al.. (2013). An assessment of external biosecurity on Southern Ontario swine farms and its application to surveillance on a geographic level.. PubMed. 77(4). 241–53. 4 indexed citations
19.
Deardon, Rob, et al.. (2011). Spatial measurement error in infectious disease models. Journal of Applied Statistics. 39(5). 1139–1150. 4 indexed citations
20.
Tildesley, Michael J., Rob Deardon, Nicholas J. Savill, et al.. (2008). Accuracy of models for the 2001 foot-and-mouth epidemic. Proceedings of the Royal Society B Biological Sciences. 275(1641). 1459–1468. 72 indexed citations

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.

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