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Epihiper-A high performance computational modeling framework to support epidemic science.

Jiangzhuo Chen1, Stefan Hoops1, Henning S Mortveit1,2

  • 1Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA.

PNAS Nexus
|December 25, 2024
PubMed
Summary
This summary is machine-generated.

Epihiper is a high-performance computational framework for epidemic science. It enables detailed simulation of disease spread on dynamic networks and supports user-defined interventions for public health policy.

Keywords:
agent-based modelscomputational epidemiologyhigh performance computingprogrammable pharmaceutical and nonpharmaceutical interventionssocial-contact networks

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Area of Science:

  • Epidemiology
  • Computational Science
  • Public Health

Background:

  • Effective epidemic response requires sophisticated computational tools.
  • Simulating disease dynamics on large, dynamic social networks presents significant challenges.

Purpose of the Study:

  • To introduce Epihiper, a high-performance computational modeling framework for epidemic science.
  • To provide a flexible platform for custom disease models and user-programmable interventions.

Main Methods:

  • Developed a framework supporting custom disease models and large-scale, dynamic network simulations.
  • Incorporated fine-grained control over network attributes and intervention targeting.
  • Enabled intervention execution based on trigger conditions and customizable sets.

Main Results:

  • Epihiper supports detailed simulation of epidemic evolution with user-defined interventions.
  • The framework allows for dynamic network updates in response to nonpharmaceutical interventions.
  • Demonstrated high-performance computing responsiveness for realistic, large-scale scenarios.

Conclusions:

  • Epihiper is a versatile and extensible framework for advanced epidemic modeling.
  • The framework supports public health policymakers in epidemic planning and response.
  • Epihiper was utilized in the CDC Scenario Modeling Hub for COVID-19 response.