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  3. Basic Baseline Model Design Choices Can Substantially Influence Performance In Collaborative Forecast Hubs.
  1. Home
  3. Basic Baseline Model Design Choices Can Substantially Influence Performance In Collaborative Forecast Hubs.

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

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Basic Baseline model design choices can substantially influence performance in collaborative forecast hubs.

Ehsan Suez1,2, Spencer J Fox1,2,3

  • 1Institute of Bioinformatics, University of Georgia, Athens, GA, USA.

Medrxiv : the Preprint Server for Health Sciences
|March 27, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Optimizing baseline models in outbreak forecasting significantly improves accuracy. A specific flatline model using recent transformed data outperformed standard benchmarks, highlighting the need for transparency in forecasting methods.

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Area of Science:

  • Epidemiology
  • Public Health
  • Computational Biology

Background:

  • Outbreak forecasting aids public health decision-making during epidemics.
  • Collaborative forecast hubs are the standard for real-time epidemic predictions.
  • Baseline models serve as crucial benchmarks within these hubs but their design is often subjective and understudied.

Purpose of the Study:

  • To evaluate the impact of subjective Baseline model design decisions on forecast performance.
  • To identify optimal Baseline model specifications for trend forecasting.
  • To compare trend baseline models with seasonal baseline models for specific surveillance targets.

Main Methods:

  • Retrospective forecasting was performed using multiple years of data for COVID-19, influenza, and RSV hospital admissions, and weighted influenza-like illness (wILI).
  • Three key Baseline specification decisions were evaluated: amount of historical data, data transformation, and forecast variant (flatline vs. drift).
  • Trend baseline models were compared to a seasonal baseline model for wILI data.

    • Main Results:

    • Model specification significantly altered forecast performance across all targets.
    • An optimal performing model was identified as a flatline model using 6-12 transformed recent observations.
    • This optimal model outperformed the current standard Baseline by an average of 9.6% and a seasonal baseline by 32.3%.

    Conclusions:

    • Subjective Baseline design choices materially influence forecast accuracy and model rankings within collaborative hubs.
    • Increased transparency in Baseline model specifications is necessary.
    • Routine inclusion of multiple benchmark models in forecast hubs is recommended.