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The Dynamic Epidemiological Networks (DEN) is a multiscale and extensible data representation framework for describing the spread of a rapidly evolving infectious diseases like COVID-19. A DEN can represent information at various granularities starting from the molecular to epidemiological. It can also represent non-conservative real-world environments characterized by impersistent as well as novel data. One prototypical application of the DEN framework has been to represent molecular data collected from the Covid-19 pandemic. The rapid evolution of the SARS-CoV-2 virus leads to a continually changing molecular landscape of the etiological agent. In such settings, assignment of samples to variants and even variant definitions themselves can change as existing variants evolve, novel variants appear and older variants die-out. In this paper we present preliminary results extending the DEN framework using the transportation formulation to determine correspondences between viral lineages as they evolve. An advantage of our approach lies in the fact that correspondences between related sample clusters across time can be determined even when these clusters do not share common elements. The applicability of our approach is demonstrated by constructing and analyzing temporal molecular networks of SARS-CoV-2 genomes sequenced as part of COVID-19 tracking efforts.

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Determining Temporal Linkages in Dynamic Epidemiological Networks Using the Earth Mover’s Distance

  • 会议论文

  • 发表日期：

    2025年

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  • Rahul Singh,
  • Jiadong Yu

摘要

The Dynamic Epidemiological Networks (DEN) is a multiscale and extensible data representation framework for describing the spread of a rapidly evolving infectious diseases like COVID-19. A DEN can represent information at various granularities starting from the molecular to epidemiological. It can also represent non-conservative real-world environments characterized by impersistent as well as novel data. One prototypical application of the DEN framework has been to represent molecular data collected from the Covid-19 pandemic. The rapid evolution of the SARS-CoV-2 virus leads to a continually changing molecular landscape of the etiological agent. In such settings, assignment of samples to variants and even variant definitions themselves can change as existing variants evolve, novel variants appear and older variants die-out. In this paper we present preliminary results extending the DEN framework using the transportation formulation to determine correspondences between viral lineages as they evolve. An advantage of our approach lies in the fact that correspondences between related sample clusters across time can be determined even when these clusters do not share common elements. The applicability of our approach is demonstrated by constructing and analyzing temporal molecular networks of SARS-CoV-2 genomes sequenced as part of COVID-19 tracking efforts.