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Model selection for seasonal influenza forecasting.

Alexander E Zarebski1, Peter Dawson2, James M McCaw1,3,4

  • 1School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia.

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Forecasting influenza epidemics requires optimal models. This study introduces a new method to select models based on predictive skill, finding that humidity improves forecasts while population mixing does not.

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Area of Science:

  • Epidemiology and Public Health
  • Mathematical Modeling
  • Biostatistics

Background:

  • Seasonal influenza epidemics pose a significant public health burden, particularly in temperate regions like Melbourne, Australia.
  • Mechanistic transmission models coupled with particle filters are increasingly used for forecasting influenza.
  • Optimal model selection and performance measurement for influenza forecasting remain challenging research areas.

Purpose of the Study:

  • To develop and apply a likelihood-based model selection method for identifying influenza transmission models with superior predictive skill.
  • To determine the optimal influenza transmission model for forecasting laboratory-confirmed cases in Melbourne (2010-2015).
  • To evaluate the impact of environmental factors (absolute humidity) and population dynamics (inhomogeneous mixing) on forecast accuracy.

Main Methods:

  • A novel likelihood-based approach, analogous to Bayes factors, was employed for model selection based on predictive skill.
  • Predictive skill was defined as the probability of future data given past data.
  • Susceptible-Exposed-Infectious-Recovered (SEIR) models were extended to include absolute humidity and inhomogeneous mixing, and compared using the developed selection method.

Main Results:

  • The model selection method successfully identified optimal models for forecasting influenza cases.
  • Inclusion of absolute humidity, both as direct measurements and sinusoidal approximations, significantly enhanced forecast predictive skill.
  • Allowing for inhomogeneous mixing in the population did not improve, and in fact reduced, the predictive skill of the models.

Conclusions:

  • The proposed likelihood-based method provides a robust framework for selecting influenza forecasting models based on predictive performance.
  • Absolute humidity is a key environmental factor that improves the accuracy of influenza transmission forecasts.
  • The findings offer a pathway for integrating improved model selection into operational influenza forecasting systems.