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Discrete time forecasting of epidemics.

Daniel A M Villela1

  • 1Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.

Infectious Disease Modelling
|January 30, 2020
PubMed
Summary

This study introduces a new model for infectious disease forecasting that accounts for data collected over discrete time intervals. The model accurately predicts epidemic dynamics and case numbers, despite data noise.

Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health Surveillance

Background:

  • Infectious disease forecasting is crucial for epidemic management.
  • Accurate prediction requires understanding epidemic dynamics like growth and turning points.
  • Existing models often overlook the impact of aggregated, discrete time-series data from health surveillance.

Purpose of the Study:

  • To develop a phenomenological model for infectious disease forecasting that incorporates discrete time-series data.
  • To enable estimation of key epidemic dynamics parameters and improve forecasting accuracy.
  • To address the practical challenge of batch data aggregation in health surveillance.

Main Methods:

  • Development of a phenomenological model to handle data aggregated over discrete time intervals.
Keywords:
ForecastingInfluenzaMathematical modelSARI

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  • Derivation of equations for estimating epidemic dynamics parameters (e.g., starting point, growth factor).
  • Application and validation of the model using severe acute respiratory illness (SARI) data and synthetic datasets.

    • Main Results:

    • The model successfully estimates key epidemic dynamics parameters.
    • Forecasting demonstrates strong adherence to observed epidemic dynamics over time.
    • The model shows resilience to statistical noise in the data.
    • A delay effect in forecasting was observed due to the discrete nature of the data aggregation.

    Conclusions:

    • The developed model provides a robust framework for infectious disease forecasting with discrete time-series data.
    • The model's ability to estimate dynamics and forecast cases is validated by real and synthetic data.
    • Acknowledging and modeling the discrete time aspect is essential for accurate epidemic surveillance and prediction.