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Early warning signals can predict infectious disease transitions. Applying controls like vaccination during weakened states accelerates disease extinction, while mistimed interventions prolong it.

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Public Health

Background:

  • The COVID-19 pandemic highlighted the need for improved infectious disease monitoring and control strategies.
  • Early warning signals (EWS) theory aims to detect critical transitions in dynamic systems, crucial for timely interventions.
  • Predicting stochastic transitions in epidemic models using generic EWS indicators is challenging, but detected changes signal an impending shift.

Purpose of the Study:

  • To describe a method using metric-based indicators from EWS theory to monitor epidemic system states.
  • To investigate the effectiveness of applying control measures (e.g., vaccination, quarantine) based on system resilience.
  • To determine optimal timing for control interventions to achieve earlier disease extinction.

Main Methods:

  • Utilized a susceptible-infectious-susceptible epidemic model with a stable endemic equilibrium.
  • Employed stochastic simulations where noise can induce transitions from endemic to extinct states.
  • Measured time series data using autocorrelation, return rate, skewness, and variance to assess system state and resilience.

Main Results:

  • Metric-based EWS indicators can identify when an epidemic system is in a weakened state, signaling an increased probability of transition.
  • Applying control measures during these weakened states significantly accelerates disease extinction compared to no intervention.
  • Intervening when the system is in a highly resilient state can paradoxically increase extinction time.

Conclusions:

  • EWS indicators provide valuable insights into the resilience and state of epidemic systems.
  • Timely application of control measures, informed by EWS, is critical for effective disease management and eradication.
  • Assessing system resilience before implementing interventions is crucial to avoid counterproductive outcomes.