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Network effects in influenza spread: The impact of mobility and socio-economic factors

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  • Burris, Courtney
  • Nikolaev, Alexander
  • Zhong, Shiran
  • Bian, Ling

Abstract

This paper introduces new methods of modeling and analyzing social networks that emerge in the context of disease spread. Four methods of constructing informative networks are presented, two of which use. static data and two use temporal data, namely individual citizen mobility observations taken over an extensive period of time. We show how the built networks can be analyzed, and how the numerical results can be interpreted, using network permutation-based surprise analysis. In doing so, we explain the relationship of surprise analysis with conventional network hypothesis testing and Quadratic Assignment Procedure regression. Surprise analysis is more comprehensive, and can be without limitation performed with any form(s) of network subgraphs, including those with multiple nodal attributes, weighted links, and temporal features. To illustrate our methodological work in application, we put them to use for interpreting networks constructed from the data collected over one year in an observational study in Buffalo and Erie counties in New York state during the 2016–2017 influenza season. Even with the limitations in the data size, our methods are able to reveal the global (city- and season-wide) patterns in the spread of influenza, taking into account population mobility and socio-economic factors.

Suggested Citation

  • Burris, Courtney & Nikolaev, Alexander & Zhong, Shiran & Bian, Ling, 2021. "Network effects in influenza spread: The impact of mobility and socio-economic factors," Socio-Economic Planning Sciences, Elsevier, vol. 78(C).
    • Handle: RePEc:eee:soceps:v:78:y:2021:i:c:s0038012121000732
    DOI: 10.1016/j.seps.2021.101081
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    References listed on IDEAS

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    3. Shiran Zhong & Ling Bian, 2016. "A Location-Centric Network Approach to Analyzing Epidemic Dynamics," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 106(2), pages 480-488, March.
    4. Paolo Bajardi & Chiara Poletto & Jose J Ramasco & Michele Tizzoni & Vittoria Colizza & Alessandro Vespignani, 2011. "Human Mobility Networks, Travel Restrictions, and the Global Spread of 2009 H1N1 Pandemic," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-8, January.
    5. François Bavaud, 2016. "Testing Spatial Autocorrelation in Weighted Networks: The Modes Permutation Test," Advances in Spatial Science, in: Roberto Patuelli & Giuseppe Arbia (ed.), Spatial Econometric Interaction Modelling, chapter 0, pages 67-83, Springer.
    6. Stephen Eubank & Hasan Guclu & V. S. Anil Kumar & Madhav V. Marathe & Aravind Srinivasan & Zoltán Toroczkai & Nan Wang, 2004. "Modelling disease outbreaks in realistic urban social networks," Nature, Nature, vol. 429(6988), pages 180-184, May.
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    Cited by:

    1. Kazancoglu, Yigit & Ekinci, Esra & Mangla, Sachin Kumar & Sezer, Muruvvet Deniz & Ozbiltekin-Pala, Melisa, 2023. "Impact of epidemic outbreaks (COVID-19) on global supply chains: A case of trade between Turkey and China," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).

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