Nature Communications (Jan 2021)

Inferring high-resolution human mixing patterns for disease modeling

  • Dina Mistry,
  • Maria Litvinova,
  • Ana Pastore y Piontti,
  • Matteo Chinazzi,
  • Laura Fumanelli,
  • Marcelo F. C. Gomes,
  • Syed A. Haque,
  • Quan-Hui Liu,
  • Kunpeng Mu,
  • Xinyue Xiong,
  • M. Elizabeth Halloran,
  • Ira M. Longini,
  • Stefano Merler,
  • Marco Ajelli,
  • Alessandro Vespignani

DOI
https://doi.org/10.1038/s41467-020-20544-y
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 12

Abstract

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The growing need for realism in addressing complex public health questions calls for accurate models of the human contact patterns that govern disease transmission. Here, the authors generate effective population-level contact matrices by using highly detailed macro (census) and micro (survey) data on key socio-demographic features.