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Modeling epidemic flow with fluid dynamics.

Ziqiang Cheng1, Jin Wang2

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

Mathematical Biosciences and Engineering : MBE
|July 8, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel mathematical model using fluid dynamics to simulate infectious disease spread. The model accurately predicts COVID-19 spatial dynamics in Wuhan, offering new insights.

Keywords:
COVID-19computational fluid dynamicsepidemic modeling

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Computational Fluid Dynamics

Background:

  • Understanding the spatial dynamics of infectious diseases is crucial for effective public health interventions.
  • Existing models often simplify the complex interactions driving disease transmission.
  • A novel approach integrating fluid dynamics could provide a more realistic representation of epidemic spread.

Purpose of the Study:

  • To propose a new mathematical model for studying the spatial spread of infectious diseases.
  • To apply fluid dynamics theory to model epidemic spread as fluid motion.
  • To investigate the model's efficacy using computational fluid dynamics methods.

Main Methods:

  • Development of a mathematical model based on partial differential equations.
  • Incorporation of fluid dynamics theory, specifically the Euler equation for inviscid flow.
  • Application of computational fluid dynamics (CFD) techniques, including a fifth-order weighted essentially non-oscillatory (WENO) scheme for spatial discretization.

Main Results:

  • The proposed model successfully simulates the spatial spread of infectious diseases.
  • Numerical simulations for the COVID-19 outbreak in Wuhan, China, showed high accuracy in matching reported cumulative cases.
  • The framework provided new insights into the complex spatial dynamics of the COVID-19 pandemic.

Conclusions:

  • The fluid dynamics-based mathematical model offers a powerful tool for understanding and predicting infectious disease spread.
  • The computational framework demonstrates significant potential for epidemiological research and public health planning.
  • Accurate simulation of COVID-19 spatial dynamics highlights the model's applicability to real-world outbreaks.