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A smoothing and bootstrap-based framework for early outbreak detection.

Lengyang Wang1, Yingcun Xia2, Ee Hui Goh1

  • 1Advanced Methods and Analytics, Communicable Diseases Agency, Singapore, Singapore.

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Summary
This summary is machine-generated.

This study introduces a new framework for early infectious disease outbreak detection using smoothed effective reproduction number (Rt) estimates. The method improves real-time surveillance by reducing calendar effects and enhancing detection accuracy.

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Area of Science:

  • Epidemiology
  • Public Health Surveillance
  • Mathematical Modeling

Background:

  • Timely detection of infectious disease outbreaks is crucial for public health.
  • The effective reproduction number (Rt) is a key metric for transmission dynamics, but susceptible to distortions from daily reporting variations.
  • Existing outbreak detection methods can be unreliable due to calendar effects and random noise in case data.

Purpose of the Study:

  • To develop and evaluate an Rt-based outbreak detection framework integrating calendar-aware smoothing and bootstrap inference.
  • To improve the stability and interpretability of Rt estimates for real-time surveillance.
  • To assess the performance of the proposed method against established outbreak detection algorithms.

Main Methods:

  • Developed an Rt-based framework incorporating calendar-aware smoothing (e.g., working-day moving average adjusting for holidays) and bootstrap inference.
  • Utilized daily COVID-19 case data from Singapore for evaluation.
  • Compared the proposed method with Early Aberration Reporting System (EARS), EpiEstim, and logistic regression approaches.

Main Results:

  • Calendar-aware smoothing, particularly the working-day moving average (MAH), significantly improved the stability of Rt estimates.
  • The proposed framework demonstrated superior timeliness in detecting both observed and simulated outbreaks.
  • The method maintained desired false positive rates and showed robustness across varying data conditions.

Conclusions:

  • The proposed Rt-based framework offers a simple, interpretable, and theoretically grounded approach for early outbreak detection.
  • Calendar-aware smoothing is a necessary step for producing stable and interpretable Rt inputs for outbreak detection.
  • The method's consistent performance suggests broad applicability to other infectious diseases.