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Veterinary Record 170:157-158 doi:10.1136/vr.e588
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Disease modelling and the human factor

  1. Dirk Pfeiffer3
  1. Research fellow, Veterinary Epidemiology and Public Health Group, Department of Veterinary Clinical Sciences, Royal Veterinary College, Hawkshead Lane, North Mymms, Hatfield AL9 7TA
  2. Senior Lecturer in Animal Health Economics, Veterinary Epidemiology and Public Health Group, Department of Veterinary Clinical Sciences, Royal Veterinary College, Hawkshead Lane, North Mymms, Hatfield AL9 7TA
  3. Professor of Veterinary Epidemiology, Veterinary Epidemiology and Public Health Group, Department of Veterinary Clinical Sciences, Royal Veterinary College, Hawkshead Lane, North Mymms, Hatfield AL9 7TA
  1. E-mail: gfournie{at}rvc.ac.uk E-mail: jrushton{at}rvc.ac.uk E-mail: pfeiffer{at}rvc.ac.uk

Human behaviour, a significant factor in the management of animal disease outbreaks, can both affect and be affected by the way an outbreak develops. Guillaume Fournié, Jonathan Rushton and Dirk Pfeiffer argue that explicitly accounting for this in mathematical modelling would improve the value of such models in policy development

ASSESSING the likelihood of an infectious disease outbreak and predicting its scale and the most effective mitigation strategies are complex tasks that cannot be achieved by mental calculation or using intuition alone. Instead, it should involve quantitative approaches. Mathematical modelling of disease transmission is one such approach, and it is now widely considered to be an essential tool in the disease control and prevention policy development process (Royal Society 2002).

In recent years, there has been an increasing emphasis in mathematical modelling research on the role of contacts between individuals within human or animal populations, with these contacts serving as paths through which an infectious agent could spread within a population. Due to greater availability of large and detailed datasets of the nature and distribution of potentially infectious contacts and the development of more sophisticated analysis techniques, simple models where all individuals are assumed to mix randomly have been replaced by more complex models, where heterogeneity of contact patterns is explicitly acknowledged. The structural characteristics, or topology, of networks shaped by these contact patterns have been shown to influence the speed of spread of an infectious agent in a host population, the scale of the epidemic, and the effectiveness of control strategies (Fig 1).

Fig 1

Illustration of the impact of contact network topology on disease spread and efficiency of control strategies. Networks A and B incorporate the same number of farms (n=50, …

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