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Related Experiment Videos

Highly optimized tolerance in epidemic models incorporating local optimization and regrowth.

C Robert1, J M Carlson, J Doyle

  • 1Department of Physics, University of California, Santa Barbara, California 93106, USA. crobert@physics.ucsb.edu

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|June 21, 2001
PubMed
Summary

Highly optimized tolerance (HOT) states emerge from both local and global optimization in population dynamics models with epidemics. Time-dependent regrowth alters these states to prevent extinction risks.

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Area of Science:

  • Ecology
  • Complex Systems
  • Epidemiology

Background:

  • Highly optimized tolerance (HOT) describes complexity arising in systems optimized for robust performance in harsh environments.
  • Population dynamics models are crucial for understanding species survival and epidemic spread.
  • Coupled map models offer a framework for studying complex system behaviors.

Purpose of the Study:

  • To investigate the consequences of global versus local optimization criteria within a coupled map model.
  • To examine the impact of time-dependent regrowth on highly optimized tolerance states.
  • To analyze how these features influence system resilience and dynamics in the presence of epidemics.

Main Methods:

  • Utilized a coupled map model incorporating population dynamics and fatal epidemic spread.

Related Experiment Videos

  • Contrasted global and local optimization criteria for system robustness.
  • Introduced and analyzed the effects of time-dependent regrowth parameters.
  • Characterized the resulting highly optimized tolerance states and their dynamical regimes.

    • Main Results:

    • Both local and global optimization criteria result in highly optimized tolerance (HOT) states.
    • These HOT states, while potentially different in configuration, share qualitative similarities.
    • Time-dependent regrowth causes HOT states to deviate from static model optima, enhancing resilience.
    • Deviations protect against slow or impossible regrowth after significant population losses.

    Conclusions:

    • Highly optimized tolerance states are achievable through diverse optimization strategies in dynamic systems.
    • Adaptive mechanisms, like time-dependent regrowth, are essential for system survival against severe disturbances.
    • Despite potential for chaotic dynamics, HOT states tend towards simpler, more stable regimes.