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Updated: Feb 24, 2026

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Bayesian nowcasting for delay adjustments using time-varying parametric functions of cumulative reporting

Erick A Chacón-Montalván1,2, Yang Xiao1, Paula Moraga1

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Summary

This study introduces a novel Bayesian model for nowcasting disease cases, improving real-time surveillance by accounting for reporting delays. The model accurately estimates true case counts, even with significant underreporting, aiding public health decisions.

Keywords:
Bayesian hierarchical modelStandisease surveillancenowcastingreal-time estimationstochastic processes

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Accurate disease case estimation is vital for public health surveillance.
  • Reporting delays obscure true case numbers, hindering real-time response.
  • Existing methods struggle with dynamic reporting environments.

Purpose of the Study:

  • To develop a novel Bayesian hierarchical model for nowcasting true disease case counts.
  • To address and adjust for reporting delays in epidemiological data.
  • To enhance the accuracy and adaptability of real-time disease surveillance.

Main Methods:

  • Employed a Bayesian hierarchical model with flexible parametric forms.
  • Incorporated time-varying parameters modeled as stochastic processes (e.g., random walks, Ornstein-Uhlenbeck processes).
  • Assessed model performance via simulation studies and real-world data analysis.

Main Results:

  • The proposed model significantly outperforms traditional nowcasting methods in simulations.
  • Real-world data confirmed reliable true case count estimation despite reporting delays.
  • Demonstrated substantial underreporting in actual disease cases.

Conclusions:

  • Combining flexible parametric modeling with time-varying adjustments improves nowcasting accuracy.
  • The model provides a robust and adaptable tool for real-time disease surveillance.
  • Facilitates more informed and timely public health decisions based on current case data.