Tom Britton

121 papers receiving 7.7k citations

Tom Britton's Hit Papers

Mathematical Tools for Understanding Infectious Disease Dynamics 2012 · 371 citations
3710+8+17Years since publication50010001.5k

Peers

Tom Britton
Comparison fields: 5 of 184
  • Modeling and Simulation 2.1k
  • Paleontology 668
  • Statistical and Nonlinear Physics 831
  • Physiology 1.3k
  • Epidemiology 1.6k
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Simon Tavaré United States
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Citations per year

Countries citing papers authored by Tom Britton

Since Specialization
Citations

This map shows the geographic impact of Tom Britton'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 Tom Britton with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tom Britton more than expected).

Fields of papers citing papers by Tom Britton

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tom Britton. 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 Tom Britton. The network helps show where Tom Britton may publish in the future.

Co-authors

The 25 scholars most cited alongside Tom Britton, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Tom Britton Line = papers co-authored together Tom Britton links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 127 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Dynamics of fat cell turnover in humans
Hit paper breakdown →
20081820
2
Stochastic Epidemic Models and Their Statistical Analysis
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2000615
3
Reliability of Bayesian Posterior Probabilities and Bootstrap Frequencies in Phylogenetics
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2003599
4
Diversification of Neoaves: integration of molecular sequence data and fossils
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2006590
5
Adipocyte Turnover: Relevance to Human Adipose Tissue Morphology
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2009518
6 2020414
7
Mathematical Tools for Understanding Infectious Disease Dynamics
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2012371
8 2010315
9 2007251
10 2012245
11 2006196
12 2008128
13 2011122
14 201998
15 199990
16 200279
17 201479
18 200868
19 200265
20 200061

About Tom Britton

Tom Britton is a scholar working on Modeling and Simulation, Public Health, Environmental and Occupational Health, Epidemiology, Genetics and Statistical and Nonlinear Physics, having authored 127 papers that have together received 8.0k indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (72 papers), Mathematical and Theoretical Epidemiology and Ecology Models (35 papers), Complex Network Analysis Techniques (19 papers), Evolution and Genetic Dynamics (17 papers), Opinion Dynamics and Social Influence (13 papers), Influenza Virus Research Studies (12 papers), Stochastic processes and statistical mechanics (10 papers) and Bayesian Methods and Mixture Models (9 papers). The work is most often cited by research in Modeling and Simulation (2.1k citations), Paleontology (668 citations), Statistical and Nonlinear Physics (831 citations), Physiology (1.3k citations) and Epidemiology (1.6k citations). Tom Britton has collaborated with scholars based in Sweden, United Kingdom and United States. Frequent co-authors include Håkan Andersson, Peter Arner, Odo Diekmann, Hans Heesterbeek, Mikael Rydén, Frank Ball, Samuel Bernard, Kirsty L. Spalding, Pål O. Westermark and Jonas Frisén. Their work appears in journals such as Journal of Applied Probability, Mathematical Biosciences, Epidemics, Systematic Biology and PLoS Computational Biology.

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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