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Related Experiment Video

Updated: Jul 19, 2025

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A two-phase fluid model for epidemic flow.

Ziqiang Cheng1, Jin Wang2

  • 1School of Mathematics, Hefei University of Technology, Hefei, Anhui, 230009, China.

Infectious Disease Modelling
|August 7, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel fluid dynamics model to track infectious disease spread. The framework simulates epidemics like COVID-19, offering insights into spatiotemporal transmission dynamics.

Keywords:
COVID-19 simulationEpidemic spreadFluid dynamics

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Computational Fluid Dynamics

Background:

  • Understanding the spatial spread of infectious diseases is crucial for effective public health interventions.
  • Traditional epidemiological models often simplify the complex dynamics of disease transmission.

Purpose of the Study:

  • To develop and apply a novel mathematical and computational modeling framework for studying the spatial spread of infectious diseases.
  • To investigate the spatiotemporal dynamics of COVID-19 outbreaks using a fluid dynamics approach.

Main Methods:

  • Modeling susceptible and infected populations as two interacting inviscid fluids.
  • Employing high-order numerical methods from computational fluid dynamics for implementation.
  • Simulating COVID-19 outbreaks in Wuhan, China, and Tennessee, USA.

Main Results:

  • The fluid dynamics model successfully captures the macroscopic progression and spread of epidemics.
  • Detailed spatiotemporal dynamics of COVID-19 transmission were investigated through simulations.
  • The framework provides a new tool for analyzing epidemic spread.

Conclusions:

  • The proposed fluid dynamics modeling framework offers a powerful approach to studying infectious disease dynamics.
  • This method allows for a detailed examination of the complex interactions driving epidemic spread.
  • The simulations provide valuable insights for understanding and managing infectious disease outbreaks.