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Accurate epidemic prediction is possible only after the peak infection rate. An exponential growth metric helps quantify epidemic predictability, even without knowing future spread. This aids in understanding reliable prediction timing.

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Area of Science:

  • Epidemiology
  • Network Science
  • Mathematical Biology

Background:

  • Accurate epidemic prediction is crucial for effective disease control and resource allocation.
  • Understanding the reliability of epidemic predictions is essential for public health planning.
  • Previous models often focused on short-term predictions or assumed constant conditions.

Purpose of the Study:

  • To determine when epidemic predictions become reliable during an outbreak.
  • To develop a metric for quantifying the predictability of epidemics.
  • To analyze epidemic predictability in susceptible-infected-susceptible (SIS) and susceptible-infected-removed (SIR) models.

Main Methods:

  • Analysis of SIS and SIR epidemic models on networks with time-invariant spreading parameters.
  • Identification of the critical time point (peak infection rate) for reliable long-term prediction.
  • Definition and application of an exponential growth metric to assess epidemic predictability.

Main Results:

  • Reliable long-term epidemic predictions are achievable only after the peak rate of new infections.
  • Short-term predictions are the most reliable before the epidemic peak.
  • The exponential growth metric effectively quantifies epidemic predictability at various time points.

Conclusions:

  • The timing of an epidemic's peak is a key factor in determining prediction reliability.
  • The proposed exponential growth metric offers a tool to assess and compare epidemic predictability.
  • This research provides insights into the conditions under which epidemic forecasting can be trusted.