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Evaluating predictive disease models requires more than just the grand mean null model. For West Nile virus (WNV), Negative Binomial, Historical, and Always Absent models offered stronger baselines, highlighting the need for multiple null models in infectious disease forecasting.

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Ecology

Background:

  • Predictive disease models are crucial for public health, but their evaluation often relies on insufficient null models.
  • The grand mean null model (R2) is commonly used but fails to adequately represent a model's predictive power.
  • West Nile virus (WNV) serves as a relevant case study for evaluating disease forecasting models.

Purpose of the Study:

  • To evaluate the performance of ten different null models for predicting human cases of West Nile virus (WNV).
  • To determine which null models provide a more robust baseline for assessing predictive disease models.
  • To investigate the impact of training data length on null model performance.

Main Methods:

  • Ten distinct null models were implemented and compared.
  • Model performance was evaluated using human case data for West Nile virus (WNV) in the United States.
  • The influence of training time series length on model performance was analyzed.

Main Results:

  • The Negative Binomial, Historical, and Always Absent null models demonstrated superior performance compared to other models.
  • A significant majority of the evaluated null models outperformed the commonly used grand mean null model.
  • Increasing the length of the training time series generally improved null model performance, particularly in high-incidence areas, without altering relative rankings.

Conclusions:

  • A single grand mean null model is insufficient for evaluating infectious disease forecasting models.
  • Employing a combination of diverse null models, such as Negative Binomial, Historical, and Always Absent, provides a more rigorous baseline.
  • Robust null model selection is essential for accurately assessing the predictive capabilities of disease models and advancing public health surveillance.