Evaluating empirical contact networks as potential transmission pathways for infectious diseases

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Networks are often used to incorporate heterogeneity in contact patterns in mathematical models of pathogen spread. However, few tools exist to evaluate whether potential transmission pathways in a population are adequately represented by an observed contact network. Here, we describe a novel permutation-based approach, the network k-test, to determine whether the pattern of cases within the observed contact network are likely to have resulted from transmission processes in the network, indicating that the network represents potential transmission pathways between nodes. Using simulated data of pathogen spread, we compare the power of this approach to other commonly used analytical methods. We test the robustness of this technique across common sampling constraints, including undetected cases, unobserved individuals and missing interaction data. We also demonstrate the application of this technique in two case studies of livestock and wildlife networks. We show that the power of the k-test to correctly identify the epidemiologic relevance of contact networks is substantially greater than other methods, even when 50% of contact or case data are missing. We further demonstrate that the impact of missing data on network analysis depends on the structure of the network and the type of missing data.

Original languageEnglish (US)
Article number20160166
JournalJournal of the Royal Society Interface
Volume13
Issue number121
DOIs
StatePublished - Aug 2016

Bibliographical note

Funding Information:
This research was supported by USDA-NIFA AFRI Foundational Program grant no. 2013-01130, the National Science Foundation (DEB-1413925), the University of Minnesota's Institute on the Environment, the Office of the Vice President for Research and the Cooperative State Research Service, US Department of Agriculture, under project nos. MINV-62-044 and 62-051. We thank A. Cheeran, S. Wells, A. Perez, J. Alvarez, A. Mosser and N. Fountain-Jones for their contributions to the development and implementation of this procedure on the real-world case studies. Data for the Uruguay case study were provided by the Directory of Animal Identification System (SIRA in Spanish), Ministry of Livestock, Agriculture and Fisheries, Montevideo, Uruguay. Data for the African lion case study were provided by the Serengeti Lion Project, University of Minnesota, St Paul, MN, USA.

Keywords

  • Clustering
  • Livestock movement
  • Missing data
  • Pathogen transmission
  • Social network analysis
  • Wildlife epidemiology

PubMed: MeSH publication types

  • Journal Article
  • Evaluation Study

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