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Animal Social Networks$
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Jens Krause, Richard James, Daniel W. Franks, and Darren P. Croft

Print publication date: 2015

Print ISBN-13: 9780199679041

Published to Oxford Scholarship Online: January 2015

DOI: 10.1093/acprof:oso/9780199679041.001.0001

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Disease transmission in animal social networks

Disease transmission in animal social networks

Chapter:
(p.95) Chapter 10 Disease transmission in animal social networks
Source:
Animal Social Networks
Author(s):

Julian A. Drewe

Sarah E. Perkins

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199679041.003.0010

Social network analysis is inherently suited to the study of disease transmission because it allows the exploration of epidemiological relationships between susceptible and infected individuals within individual populations, which are often highly heterogeneous. Coupling the study of social networks with the infection status of individuals or groups has provided insight into how host behaviour affects network structure, and vice versa. This chapter provides an overview of the parasite transmission processes in animal social networks and illustrates how using the network approach has contributed to an understanding of the central biological issues in disease transmission. The chapter then addresses some important considerations such as how to adequately capture disease transmission on a social network, suitable time intervals for constructing parasite transmission networks, and which methods are appropriate for data collection, data analysis, and bias reduction. Next, the chapter reviews ecological methods that are of specific importance for host–parasite interactions, and explores how host–parasite interactions, animal behaviour, and disease transmission networks are inter-related. The chapter concludes with a discussion of how the application of network analysis to our understanding of infectious disease transmission might develop in the future.

Keywords:   infectious disease, parasite transmission, heterogeneity, host–parasite interactions, network analysis

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