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The effects of social structure and sex-biased transmission on macroparasite infection

  • S. E. PERKINS (a1), M. F. FERRARI (a1) and P. J. HUDSON (a1)
Summary

Mathematical models of disease dynamics tend to assume that individuals within a population mix at random and so transmission is random, and yet, in reality social structure creates heterogeneous contact patterns. We investigated the effect of heterogeneity in host contact patterns on potential macroparasite transmission by first quantifying the level of assortativity in a socially structured wild rodent population (Apodemus flavicollis) with respect to the directly-transmitted macroparasitic helminth, Heligmosomoides polygyrus. We found the population to be disassortatively mixed (i.e. male mice mixing with female mice more often than same sex mixing) at a constant level over time. The macroparasite H. polygyrus has previously been shown to exhibit male-biased transmission so we used a Susceptible-Infected (SI) mathematical model to simulate the effect of increasing strengths of male-biased transmission on the prevalence of the macroparasite using empirically-derived transmission networks. When transmission was equal between the sexes the model predicted macroparasite prevalence to be 73% and infection was male biased (82% of infection in the male mice). With a male-bias in transmission ten times that of the females, the expected macroparasite prevalence was 50% and was equally prevalent in both sexes, results that both most closely resembled empirical dynamics. As such, disassortative mixing alone did not produce macroparasite dynamics analogous to those from empirical observations; a strong male-bias in transmission was also required. We discuss the relevance of our results in the context of network models for transmission dynamics and control.

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Corresponding author
*Corresponding Author: Dr. Sarah Perkins, Center for Infectious Disease Dynamics, 208 Mueller Laboratory, Penn State University, State College, PA 16803, USA. Tel: (001) 814-863-2099. Fax: (001) 814-865-9131. E-mail: sep18@psu.edu
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Parasitology
  • ISSN: 0031-1820
  • EISSN: 1469-8161
  • URL: /core/journals/parasitology
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