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Epidemic dynamics in census-calibrated modular contact network.

Kirti Jain1, Vasudha Bhatnagar1, Sharanjit Kaur2

  • 1Department of Computer Science, University of Delhi, Delhi, 110007 India.

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|January 23, 2023
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Summary

This study introduces a novel framework for creating realistic social contact networks by integrating demographic data. This demography-laced network model accurately predicts epidemic dynamics, improving public health planning.

Keywords:
DemographyModular networkNetwork topologySmall-world networkSocial contact networkUrban geography

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Area of Science:

  • Epidemiology
  • Network Science
  • Computational Social Science

Background:

  • Understanding epidemic spread requires modeling complex human interactions.
  • Existing models often oversimplify social connectivity, limiting predictive accuracy.

Purpose of the Study:

  • To develop a novel framework for constructing demography-informed social contact networks.
  • To assess the impact of demographic factors on epidemic dynamics using these networks.
  • To validate the framework with a real-world COVID-19 case study.

Main Methods:

  • A modular network framework with small-world topology was developed, incorporating demographic data.
  • Synthetic networks were used to study the influence of zoning, density, and mobility on epidemic variables via a SEIR model.
  • Real-world census data was utilized to create surrogate social contact networks for three Indian states.

Main Results:

  • Demographic factors significantly influence social contact patterns and epidemic dynamics.
  • The demography-laced modular contact network demonstrated reduced errors in epidemic variable estimation.
  • The framework effectively emulates human interactions in family, social, and work settings.

Conclusions:

  • The proposed network framework provides a potent tool for urban planners, demographers, and social scientists.
  • Integrating demographic data into network models enhances the accuracy of epidemic spread predictions.
  • This approach offers a more realistic representation of population connectivity for public health interventions.