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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health Decision-Making

Background:

  • Emerging infectious disease management requires timely interventions, complicated by surveillance data uncertainties.
  • Noise in epidemiological data, including case under-reporting and ascertainment latencies, poses significant challenges to effective decision-making.
  • Balancing the costs of false alarms against delayed actions is critical in public health responses.

Purpose of the Study:

  • To quantify the impact of surveillance noise on the timeliness and confidence of intervention decisions.
  • To analyze the asymmetry in decision-making support between initiating and relaxing interventions.
  • To evaluate the influence of noise on epidemiological parameters like reproduction numbers and growth rates.

Main Methods:

  • Modeled intervention decisions as binary choices informed by reported case data and transmissibility estimates.
  • Utilized threshold-based decision triggers (case numbers, confidence levels) set by cost-benefit analyses.
  • Assessed the effects of case under-reporting and ascertainment latencies on decision timeliness and parameter estimation for both growing and declining epidemics.

Main Results:

  • Surveillance noise sources (under-reporting, latencies) introduce additive delays in initiating interventions during epidemic growth.
  • These noise sources also cause multiplicative reductions in confidence for estimated reproduction numbers and growth rates during epidemic expansion.
  • During epidemic decline, noise sources have counteracting effects on case data, with limited cumulative impact on transmissibility estimates.

Conclusions:

  • Standard surveillance data offer weaker support for initiating interventions compared to relaxing them, creating an information bottleneck during epidemic growth.
  • This asymmetry persists even with advanced feedback control algorithms.
  • The findings may justify more proactive intervention strategies during the early, growing phases of an epidemic.