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Adaptive social contact rates induce complex dynamics during epidemics.

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Governments must balance public health and economic costs during epidemics. Adaptive social behavior can lead to complex dynamics, including oscillations and uncertainty in disease projections.

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Public Health Policy

Background:

  • Epidemics present a critical dilemma, balancing public health risks against economic consequences.
  • Adaptive social contact significantly influences epidemic outcomes, yet these coupled human-natural systems are not fully understood.

Purpose of the Study:

  • To develop a theoretical model for adaptive, optimal control of social contact rates during epidemics.
  • To analyze the trade-offs between public health costs and economic impacts of social distancing.

Main Methods:

  • Utilized traditional epidemic modeling tools combined with a utility function incorporating delayed information.
  • Developed a discrete-time deterministic strategic model to analyze adaptive behavior in epidemic control.

Main Results:

  • Identified an endemic equilibrium and oscillatory dynamics under specific parametric conditions.
  • Revealed complex dynamic regimes that are sensitive to small parameter changes, indicating system complexity.
  • Demonstrated that adaptive behavior can lead to fluctuations around a quasi-equilibrium, with potential for multiple peaks and surges.

Conclusions:

  • Infectious disease dynamics with adaptive behavior exhibit complex adaptive system characteristics.
  • Expect fluctuations and uncertainty in epidemic projections due to the interplay of public health and economic factors.
  • Adaptive social distancing strategies require careful consideration of their dynamic and potentially unpredictable outcomes.