Tom Britton

11.5k total citations · 6 hit papers
112 papers, 7.5k citations indexed

About

Tom Britton is a scholar working on Modeling and Simulation, Public Health, Environmental and Occupational Health and Epidemiology. According to data from OpenAlex, Tom Britton has authored 112 papers receiving a total of 7.5k indexed citations (citations by other indexed papers that have themselves been cited), including 62 papers in Modeling and Simulation, 37 papers in Public Health, Environmental and Occupational Health and 32 papers in Epidemiology. Recurrent topics in Tom Britton's work include COVID-19 epidemiological studies (62 papers), Mathematical and Theoretical Epidemiology and Ecology Models (34 papers) and Complex Network Analysis Techniques (19 papers). Tom Britton is often cited by papers focused on COVID-19 epidemiological studies (62 papers), Mathematical and Theoretical Epidemiology and Ecology Models (34 papers) and Complex Network Analysis Techniques (19 papers). Tom Britton collaborates with scholars based in Sweden, United Kingdom and United States. Tom Britton's co-authors include Håkan Andersson, Peter Arner, Mikael Rydén, Kirsty L. Spalding, Samuel Bernard, Pål O. Westermark, Jonas Frisén, Frank Ball, Odo Diekmann and Hans Heesterbeek and has published in prestigious journals such as Nature, Science and SHILAP Revista de lepidopterología.

In The Last Decade

Tom Britton

109 papers receiving 7.3k citations

Hit Papers

Dynamics of fat cell turnover in humans 2000 2026 2008 2017 2008 2003 2000 2006 2009 500 1000 1.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tom Britton Sweden 28 2.0k 1.7k 1.5k 1.4k 1.3k 112 7.5k
Jonathan Dushoff Canada 56 2.3k 1.1× 2.3k 1.3× 165 0.1× 1.5k 1.1× 2.3k 1.8× 160 10.9k
Simon Tavaré United States 68 361 0.2× 1.2k 0.7× 825 0.6× 10.7k 7.6× 437 0.3× 220 21.3k
Joshua S. Weitz United States 54 859 0.4× 464 0.3× 89 0.1× 2.2k 1.6× 634 0.5× 170 9.0k
David J. D. Earn Canada 40 3.4k 1.7× 2.0k 1.2× 52 0.0× 640 0.5× 2.2k 1.7× 104 7.7k
Lauren Ancel Meyers United States 48 3.8k 1.9× 1.8k 1.1× 92 0.1× 1.2k 0.9× 1.5k 1.2× 162 9.7k
David L. Robertson United Kingdom 64 530 0.3× 2.1k 1.2× 1.6k 1.0× 5.1k 3.6× 752 0.6× 218 19.4k
Caroline Colijn Canada 34 735 0.4× 1.4k 0.8× 111 0.1× 1.1k 0.8× 446 0.3× 127 4.3k
David J. Balding United Kingdom 58 272 0.1× 586 0.3× 531 0.4× 4.7k 3.3× 330 0.3× 169 15.0k
Carson C. Chow United States 47 131 0.1× 893 0.5× 778 0.5× 4.0k 2.8× 800 0.6× 151 14.9k
Simon D. W. Frost United States 54 260 0.1× 3.5k 2.0× 136 0.1× 4.2k 3.0× 1.5k 1.2× 152 15.3k

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-authorship network of co-authors of Tom Britton

This figure shows the co-authorship network connecting the top 25 collaborators of Tom Britton. A scholar is included among the top collaborators of Tom Britton 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 Tom Britton. Tom Britton 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.
Günther, Felix, et al.. (2025). A counterfactual analysis quantifying the COVID-19 vaccination impact in Sweden. Vaccine. 52. 126870–126870. 1 indexed citations
2.
Hoffmann, Jan, et al.. (2024). Management of cervical CSF-venous fistula causing acute cognitive impairment and coma. Acta Neurochirurgica. 166(1). 37–37. 2 indexed citations
3.
Britton, Tom. (2021). Quantifying the preventive effect of wearing face masks. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences. 477(2251). 20210151–20210151. 3 indexed citations
4.
Britton, Tom, Pieter Trapman, & Frank Ball. (2021). The risk for a new COVID-19 wave and how it depends on R 0 , the current immunity level and current restrictions. Royal Society Open Science. 8(7). 210386–210386. 2 indexed citations
6.
Britton, Tom & Gianpaolo Scalia Tomba. (2019). Estimation in emerging epidemics: biases and remedies.. Cineca Institutional Research Information System (Tor Vergata University). 97 indexed citations
7.
Giardina, Federica, Ethan Romero-Severson, Maria Axelsson, et al.. (2019). Getting more from heterogeneous HIV-1 surveillance data in a high immigration country: estimation of incidence and undiagnosed population size using multiple biomarkers. International Journal of Epidemiology. 48(6). 1795–1803. 13 indexed citations
8.
Ball, Frank, et al.. (2019). A stochastic SIR network epidemic model with preventive dropping of edges. Journal of Mathematical Biology. 78(6). 1875–1951. 20 indexed citations
9.
Masuda, Naoki, et al.. (2016). . SHILAP Revista de lepidopterología. 4 indexed citations
10.
Diekmann, Odo, Hans Heesterbeek, & Tom Britton. (2012). Mathematical Tools for Understanding Infectious Disease Dynamics. Princeton University Press eBooks. 217 indexed citations
11.
Britton, Tom, et al.. (2009). Epidemic modelling: Aspects where stochasticity matters. Mathematical Biosciences. 222(2). 109–116. 52 indexed citations
12.
Britton, Tom & Mathias Lindholm. (2009). The Early Stage Behaviour of a Stochastic SIR Epidemic with Term-Time Forcing. Journal of Applied Probability. 46(4). 975–992.
13.
Britton, Tom & Mathias Lindholm. (2009). The Early Stage Behaviour of a Stochastic SIR Epidemic with Term-Time Forcing. Journal of Applied Probability. 46(4). 975–992. 8 indexed citations
14.
Spalding, Kirsty L., Peter Arner, Pål O. Westermark, et al.. (2008). Dynamics of Fat Cell Turnover in Humans. Obstetrical & Gynecological Survey. 63(9). 577–578. 43 indexed citations
15.
Spalding, Kirsty L., Peter Arner, Pål O. Westermark, et al.. (2008). Dynamics of fat cell turnover in humans. Nature. 453(7196). 783–787. 1710 indexed citations breakdown →
16.
Svennblad, Bodil & Tom Britton. (2007). Improving Divergence Time Estimation in Phylogenetics: More Taxa vs. Longer Sequences. Statistical Applications in Genetics and Molecular Biology. 6(1). Article35–Article35. 2 indexed citations
17.
Ball, Frank & Tom Britton. (2007). An epidemic model with infector-dependent severity. Advances in Applied Probability. 39(4). 949–972. 1 indexed citations
18.
Britton, Tom, et al.. (2007). Modelling sexually transmitted infections: The effect of partnership activity and number of partners on. Theoretical Population Biology. 72(3). 389–399. 19 indexed citations
19.
Britton, Tom. (2001). Epidemics in heterogeneous communities: estimation of "R". RePEc: Research Papers in Economics. 63(4). 705–715. 2 indexed citations
20.
Andersson, Håkan & Tom Britton. (1998). Heterogeneity in epidemic models and its effect on the spread of infection. Journal of Applied Probability. 35(3). 651–661. 31 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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