A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2 T Britton, F Ball, P Trapman Science 369 (6505), 846-849, 2020 | 265 | 2020 |

The abundance threshold for plague as a critical percolation phenomenon S Davis, P Trapman, H Leirs, M Begon, JAP Heesterbeek Nature 454 (7204), 634-637, 2008 | 184 | 2008 |

Analysis of a stochastic SIR epidemic on a random network incorporating household structure F Ball, D Sirl, P Trapman Mathematical Biosciences 224 (2), 53-73, 2010 | 151 | 2010 |

Eight challenges for network epidemic models L Pellis, F Ball, S Bansal, K Eames, T House, V Isham, P Trapman Epidemics 10, 58-62, 2015 | 139 | 2015 |

Five challenges for spatial epidemic models S Riley, K Eames, V Isham, D Mollison, P Trapman Epidemics 10, 68-71, 2015 | 112 | 2015 |

On analytical approaches to epidemics on networks P Trapman Theoretical population biology 71 (2), 160-173, 2007 | 110 | 2007 |

The nosocomial transmission rate of animal-associated ST398 meticillin-resistant *Staphylococcus aureus*MCJ Bootsma, MWM Wassenberg, P Trapman, MJM Bonten Journal of the Royal Society Interface 8 (57), 578-584, 2011 | 102 | 2011 |

Threshold behaviour and final outcome of an epidemic on a random network with household structure F Ball, D Sirl, P Trapman Advances in Applied Probability 41 (3), 765-796, 2009 | 88 | 2009 |

Reproduction numbers for epidemic models with households and other social structures. I. Definition and calculation of R0 L Pellis, F Ball, P Trapman Mathematical biosciences 235 (1), 85-97, 2012 | 69 | 2012 |

Epidemics on random intersection graphs FG Ball, DJ Sirl, P Trapman Annals of Applied Probability 24 (3), 1081-1128, 2014 | 50 | 2014 |

Five challenges for stochastic epidemic models involving global transmission T Britton, T House, AL Lloyd, D Mollison, S Riley, P Trapman Epidemics 10, 54-57, 2015 | 49 | 2015 |

Key questions for modelling COVID-19 exit strategies RN Thompson, TD Hollingsworth, V Isham, D Arribas-Bel, B Ashby, ... Proceedings of the Royal Society B 287 (1932), 20201405, 2020 | 39 | 2020 |

The growth of the infinite long-range percolation cluster P Trapman Annals of probability 38 (4), 1583-1608, 2010 | 37 | 2010 |

The disease-induced herd immunity level for Covid-19 is substantially lower than the classical herd immunity level T Britton, F Ball, P Trapman arXiv preprint arXiv:2005.03085, 2020 | 30 | 2020 |

A useful relationship between epidemiology and queueing theory: the distribution of the number of infectives at the moment of the first detection P Trapman, MCJ Bootsma Mathematical biosciences 219 (1), 15-22, 2009 | 30 | 2009 |

Reproduction numbers for epidemic models with households and other social structures II: comparisons and implications for vaccination F Ball, L Pellis, P Trapman Mathematical biosciences 274, 108-139, 2016 | 27 | 2016 |

A branching model for the spread of infectious animal diseases in varying environments P Trapman, R Meester, H Heesterbeek Journal of mathematical biology 49 (6), 553-576, 2004 | 26 | 2004 |

Inferring *R*_{0} in emerging epidemics—the effect of common population structure is smallP Trapman, F Ball, JS Dhersin, VC Tran, J Wallinga, T Britton Journal of the Royal Society Interface 13 (121), 20160288, 2016 | 24 | 2016 |

Reproduction numbers for epidemics on networks using pair approximation P Trapman Mathematical biosciences 210 (2), 464-489, 2007 | 24 | 2007 |

Who is the infector? Epidemic models with symptomatic and asymptomatic cases KY Leung, P Trapman, T Britton Mathematical biosciences 301, 190-198, 2018 | 20 | 2018 |