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 () and Alessandro Vespignani ()
Additional contact information
Dina Mistry: Institute for Disease Modeling, Global Health Division, Bill and Melinda Gates Foundation
Maria Litvinova: Northeastern University
Ana Pastore y Piontti: Northeastern University
Matteo Chinazzi: Northeastern University
Laura Fumanelli: Bruno Kessler Foundation
Marcelo F. C. Gomes: Fiocruz, Scientific Computing Program, Grupo de Métodos Analíticos em Vigilância Epidemiológica
Syed A. Haque: Northeastern University
Quan-Hui Liu: Sichuan University
Kunpeng Mu: Northeastern University
Xinyue Xiong: Northeastern University
M. Elizabeth Halloran: Fred Hutchinson Cancer Research Center
Ira M. Longini: University of Florida
Stefano Merler: Bruno Kessler Foundation
Marco Ajelli: Northeastern University
Alessandro Vespignani: Northeastern University

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: Abstract Mathematical and computational modeling approaches are increasingly used as quantitative tools in the analysis and forecasting of infectious disease epidemics. The growing need for realism in addressing complex public health questions is, however, calling for accurate models of the human contact patterns that govern the disease transmission processes. Here we present a data-driven approach to generate effective population-level contact matrices by using highly detailed macro (census) and micro (survey) data on key socio-demographic features. We produce age-stratified contact matrices for 35 countries, including 277 sub-national administratvie regions of 8 of those countries, covering approximately 3.5 billion people and reflecting the high degree of cultural and societal diversity of the focus countries. We use the derived contact matrices to model the spread of airborne infectious diseases and show that sub-national heterogeneities in human mixing patterns have a marked impact on epidemic indicators such as the reproduction number and overall attack rate of epidemics of the same etiology. The contact patterns derived here are made publicly available as a modeling tool to study the impact of socio-economic differences and demographic heterogeneities across populations on the epidemiology of infectious diseases.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-20544-y

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DOI: 10.1038/s41467-020-20544-y

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