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Related Experiment Video

Updated: Jan 28, 2026

Fluorescence-based Neuraminidase Inhibition Assay to Assess the Susceptibility of Influenza Viruses to The Neuraminidase Inhibitor Class of Antivirals
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Predictability in process-based ensemble forecast of influenza.

Sen Pei1, Mark A Cane2, Jeffrey Shaman1

  • 1Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States of America.

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Summary

Model errors and fluctuations significantly impact infectious disease forecasts. This study optimizes influenza models by analyzing error growth, improving short-term incidence predictions by 1-4 weeks.

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Computational Science

Background:

  • Process-based models are crucial for simulating and forecasting nonlinear dynamical systems like influenza.
  • Understanding forecast inaccuracies in infectious disease models is essential for public health preparedness.

Purpose of the Study:

  • To evaluate the impact of initial condition errors and stochastic fluctuations on influenza transmission model accuracy.
  • To identify and analyze the dominant sources of forecast error growth in compartmental influenza models.

Main Methods:

  • Utilized a compartmental model for influenza transmission dynamics.
  • Applied singular vector analysis to compute the fastest-growing error directions.
  • Generated ensemble forecasts with optimized perturbations based on error analysis.

Main Results:

  • Initial condition errors and stochastic fluctuations exhibit similar growth patterns, indicating dynamic error growth as a key factor in forecast inaccuracy.
  • Optimized ensemble perturbations led to improved short-term incidence forecasts.
  • Retrospective forecasts for 95 US cities (2003-2014) demonstrated enhanced accuracy for the subsequent 1-4 weeks.

Conclusions:

  • Dynamic error growth, irrespective of its source, is a primary driver of forecast inaccuracy in influenza models.
  • Singular vector analysis provides a method to identify optimal error perturbations for ensemble forecasting.
  • The developed approach enhances the accuracy of short-term influenza incidence predictions.