Abstract

Social network analyses were used to investigate contact patterns in a free-living possum Trichosurus vulpecula population and to estimate the influence of contact on R(0) for bovine tuberculosis (TB). Using data collected during a five-year capture-mark-recapture study of a free-living possum population, observed estimates of R(0) were computed and compared with R(0) computed from random networks of similar size that approximated a random mixing process. All networks displayed a heterogeneous pattern of contact with the average number of contacts per possum ranging from 20 to 26 per year. The networks consistently showed small-world and single-scale features. The mean estimates of R(0) for TB using the observed contact networks were 1.78, 1.53, 1.53, 1.51, and 1.52 times greater than the corresponding random networks (P <0.05). We estimate that TB would spread if an average of between 1.94 and 1.97 infective contacts occurred per year per infected possum, which is approximately half of that expected from a random network. These results have implications for the management of TB in New Zealand where the possum is the principal wildlife reservoir host of Mycobacterium bovis, the causal agent of bovine TB. This study argues the relevance of refining epidemiological models used to inform disease management policy to account for contact heterogeneity.

Highlights

  • Evaluating the transmission dynamics of an infectious disease process and its ability to establish and persist in a population is essential for devising effective control strategies

  • This is supported by a high coefficient of variation which ranged from 74% to 87%

  • Defining contact networks in animal populations is difficult because it requires the interactions between members of a population to be monitored and recorded for extended periods of time

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Summary

Introduction

Evaluating the transmission dynamics of an infectious disease process and its ability to establish and persist in a population is essential for devising effective control strategies. Calculation of R0 is challenging as it depends on knowledge of the contact structure in the population of interest, which is often unknown [3] In their simplest form, susceptible-infected-recovered (SIR) models assume that individuals in a population are likely to contact and infect each other [3]. Non-uniformity means that the likelihood of a disease being transmitted from one individual to another will vary with the probability of an infected individual making contact with other members of the population [43].

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