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Addressing reporting delays in infectious disease forecasting improves accuracy. This framework uses historical or external data to correct under-reported cases, enhancing public health predictions.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Infectious disease forecasting is crucial for public health and policy.
  • Reporting delays in case data can lead to underestimation of disease burden.
  • Accurate forecasting is essential for timely and effective public health interventions.

Purpose of the Study:

  • To develop a general framework for addressing reporting delays in infectious disease forecasting.
  • To improve the accuracy and reliability of disease forecasts.
  • To provide guidance on handling reporting delays in practical forecasting efforts.

Main Methods:

  • Proposed a framework leveraging historical case data or external internet data to estimate reporting errors.
  • Developed methods to adapt forecasting pipelines for under- or over-reporting.
  • Applied methods to dengue fever and influenza-like illness (ILI) data.
  • Conducted simulation studies to compare method performance and robustness.

Main Results:

  • Implementing correction methods for reporting delays consistently increased forecasting accuracy and prediction coverage.
  • Method performance varied, with some requiring specific data quality or external sources.
  • Alternative strategies like data exclusion and sensitivity analysis were evaluated.

Conclusions:

  • Accounting for reporting delays is vital for accurate infectious disease forecasting.
  • The proposed framework offers practical strategies and guidance for public health practitioners.
  • The study highlights the importance of addressing data limitations in epidemiological modeling.