### Abstract

Comparisons between mass-action or ‘‘random’’ network models and empirical networks have produced
mixed results. Here we seek to discover whether a simulated disease spread through randomly constructed
networks can be coerced to model the spread in empirical networks by altering a single disease
parameter — the probability of infection. A stochastic model for disease spread through herds of cattle is
utilised to model the passage of an SEIR (susceptible–latent–infected–resistant) through five networks.
The first network is an empirical network of recorded contacts, from four datasets available, and the other
four networks are constructed from randomly distributed contacts based on increasing amounts of information
from the recorded network. A numerical study on adjusting the value of the probability of infection
was conducted for the four random network models. We found that relative percentage reductions in the
probability of infection, between 5.6% and 39.4% in the random network models, produced results that
most closely mirrored the results from the empirical contact networks. In all cases tested, to reduce the
differences between the two models, required a reduction in the probability of infection in the random
network.
© 2014 Elsevier Inc. All rights reserved.

Original language | English |
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Pages (from-to) | 11 - 18 |

Number of pages | 8 |

Journal | Theoretical Population Biology |

Volume | 98 |

DOIs | |

Publication status | Print publication - 2014 |

### Bibliographical note

1023397### Keywords

- Disease
- Mass-action
- Network
- Recorded contacts
- SEIR simulation

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## Profiles

## George Gunn

- Animal Health And Welfare, Animal Health - Team Leader Head of Vet. Epidemiology

Person: Academic contract that is research only

## Roger Humphry

- Animal Health And Welfare, Animal Health - Applied Statistician

Person: Academic contract that is research only