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Related Experiment Videos

Spatial models of virus-immune dynamics.

Georg A Funk1, Vincent A A Jansen, Sebastian Bonhoeffer

  • 1School of Biological Sciences, Royal Holloway-University of London, Egham (Surrey) TW20 0EX, UK. georg.funk@rhul.ac.uk

Journal of Theoretical Biology
|December 28, 2004
PubMed
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Spatial models reveal crucial differences in infectious disease dynamics compared to non-spatial models. Incorporating local dispersal in virus-immune interactions improves accuracy, especially in solid tissues.

Area of Science:

  • Mathematical Biology
  • Virology
  • Immunology

Background:

  • Most theoretical models of infectious disease dynamics assume well-mixed populations.
  • Infections often occur in solid tissues, where spatial structure is significant.

Purpose of the Study:

  • To explore spatially structured models of virus and virus-immune dynamics.
  • To understand how local dispersal affects infection dynamics compared to non-spatial models.

Main Methods:

  • Developed spatial models incorporating local dispersal for virus and immune effector cells.
  • Compared dynamics of spatial models with non-spatial models under homogeneous and heterogeneous conditions.
  • Investigated source-sink dynamics and their impact on infection outcomes.

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Main Results:

  • Spatial models showed robust stability properties across various dispersal schemes.
  • Significant differences in infection peak dynamics were observed between spatial and non-spatial models in homogeneous space.
  • Spatial coupling in heterogeneous environments altered equilibrium properties and reduced dynamic elimination likelihood.
  • Long-lasting oscillations were rare, aligning with clinical observations.
  • Infection outcomes critically depended on spatial coupling, with collapse possible under high emigration.

Conclusions:

  • Non-spatial models may introduce systematic errors in estimating acute infection dynamics.
  • Spatially structured models provide a more accurate representation of infectious diseases in solid tissues.
  • Further research is needed to bridge the data gap in solid tissue infection dynamics.