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Inferring high-resolution human mixing patterns for disease modeling

Author

Listed:
  • Dina Mistry

    (Institute for Disease Modeling, Global Health Division, Bill and Melinda Gates Foundation
    Northeastern University)

  • Maria Litvinova

    (Northeastern University
    ISI Foundation
    Indiana University School of Public Health)

  • 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
    University of Washington)

  • Ira M. Longini

    (University of Florida)

  • Stefano Merler

    (Bruno Kessler Foundation)

  • Marco Ajelli

    (Northeastern University
    Indiana University School of Public Health)

  • Alessandro Vespignani

    (Northeastern University
    ISI Foundation)

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.

Suggested Citation

  • 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, 2021. "Inferring high-resolution human mixing patterns for disease modeling," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-20544-y
    DOI: 10.1038/s41467-020-20544-y
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    Cited by:

    1. Benjamin Faucher & Rania Assab & Jonathan Roux & Daniel Levy-Bruhl & Cécile Tran Kiem & Simon Cauchemez & Laura Zanetti & Vittoria Colizza & Pierre-Yves Boëlle & Chiara Poletto, 2022. "Agent-based modelling of reactive vaccination of workplaces and schools against COVID-19," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Li, Wenjie & Li, Jiachen & Nie, Yanyi & Lin, Tao & Chen, Yu & Liu, Xiaoyang & Su, Sheng & Wang, Wei, 2024. "Infectious disease spreading modeling and containing strategy in heterogeneous population," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    3. Michele Tizzoni & Elaine O. Nsoesie & Laetitia Gauvin & Márton Karsai & Nicola Perra & Shweta Bansal, 2022. "Addressing the socioeconomic divide in computational modeling for infectious diseases," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    4. Thomas Ash & Antonio M. Bento & Daniel Kaffine & Akhil Rao & Ana I. Bento, 2022. "Disease-economy trade-offs under alternative epidemic control strategies," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    5. Stefano Guarino & Enrico Mastrostefano & Massimo Bernaschi & Alessandro Celestini & Marco Cianfriglia & Davide Torre & Lena Rebecca Zastrow, 2021. "Inferring Urban Social Networks from Publicly Available Data," Future Internet, MDPI, vol. 13(5), pages 1-45, April.
    6. Atienza-Diez, Iker & Seoane, Luís F., 2023. "Long- and short-term effects of cross-immunity in epidemic dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    7. Adriana Manna & Júlia Koltai & Márton Karsai, 2024. "Importance of social inequalities to contact patterns, vaccine uptake, and epidemic dynamics," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    8. Mark P. Khurana & Jacob Curran-Sebastian & Neil Scheidwasser & Christian Morgenstern & Morten Rasmussen & Jannik Fonager & Marc Stegger & Man-Hung Eric Tang & Jonas L. Juul & Leandro Andrés Escobar-He, 2024. "High-resolution epidemiological landscape from ~290,000 SARS-CoV-2 genomes from Denmark," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    9. Nicolò Gozzi & Matteo Chinazzi & Natalie E. Dean & Ira M. Longini Jr & M. Elizabeth Halloran & Nicola Perra & Alessandro Vespignani, 2023. "Estimating the impact of COVID-19 vaccine inequities: a modeling study," Nature Communications, Nature, vol. 14(1), pages 1-10, December.

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