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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Public Health

Background:

  • Infectious diseases pose ongoing public health risks.
  • Critical slowing down (CSD) in low-dimensional models may precede epidemic transitions.
  • Early-warning signals (EWS) based on CSD could anticipate disease (re-)emergence.

Purpose of the Study:

  • To assess the generality of CSD as a model-independent feature in epidemiological dynamics.
  • To determine if CSD predictions from simple models apply to complex, high-dimensional systems.
  • To evaluate the effectiveness of various EWS for detecting disease emergence.

Main Methods:

  • A simulation study using a hierarchy of five measles-like disease models of increasing complexity and dimensionality.
  • Models included SEIR variants, a multiplex network model (Mplex), and an agent-based simulator (FRED).
  • Models were parameterized for a 90% herd immunity threshold, with vaccine uptake decreasing from 92% to 70% over 15 years.

Main Results:

  • Evidence of CSD preceding disease re-emergence was found in all five models.
  • The mean and variance were the best performing EWS, achieving an Area Under the ROC Curve (AUC) > 0.75 one year before re-emergence.
  • Autocorrelation and index of dispersion also showed promise as candidate EWS.

Conclusions:

  • CSD is a generic feature of epidemiological dynamics, applicable across models of varying complexity.
  • Mean, variance, autocorrelation, and index of dispersion are promising EWS for early detection of infectious disease emergence.
  • These findings support the potential for CSD-based EWS in public health surveillance.