Comparing transmission potential networks based on social network surveys, close contacts and environmental overlap in rural Madagascar.

Journal Article (Journal Article)

Social and spatial network analysis is an important approach for investigating infectious disease transmission, especially for pathogens transmitted directly between individuals or via environmental reservoirs. Given the diversity of ways to construct networks, however, it remains unclear how well networks constructed from different data types effectively capture transmission potential. We used empirical networks from a population in rural Madagascar to compare social network survey and spatial data-based networks of the same individuals. Close contact and environmental pathogen transmission pathways were modelled with the spatial data. We found that naming social partners during the surveys predicted higher close-contact rates and the proportion of environmental overlap on the spatial data-based networks. The spatial networks captured many strong and weak connections that were missed using social network surveys alone. Across networks, we found weak correlations among centrality measures (a proxy for superspreading potential). We conclude that social network surveys provide important scaffolding for understanding disease transmission pathways but miss contact-specific heterogeneities revealed by spatial data. Our analyses also highlight that the superspreading potential of individuals may vary across transmission modes. We provide detailed methods to construct networks for close-contact transmission pathogens when not all individuals simultaneously wear GPS trackers.

Full Text

Duke Authors

Cited Authors

  • Kauffman, K; Werner, CS; Titcomb, G; Pender, M; Rabezara, JY; Herrera, JP; Shapiro, JT; Solis, A; Soarimalala, V; Tortosa, P; Kramer, R; Moody, J; Mucha, PJ; Nunn, C

Published Date

  • January 12, 2022

Published In

Volume / Issue

  • 19 / 186

Start / End Page

  • 20210690 -

PubMed ID

  • 35016555

Pubmed Central ID

  • PMC8753172

Electronic International Standard Serial Number (EISSN)

  • 1742-5662

International Standard Serial Number (ISSN)

  • 1742-5689

Digital Object Identifier (DOI)

  • 10.1098/rsif.2021.0690

Language

  • eng