ArticlemedRxiv : the preprint server for health sciences2026
Basic Baseline model design choices can substantially influence performance in collaborative forecast hubs.
Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Over the past decade, outbreak forecasting has become an increasingly used tool to assist public health decision-making during epidemics. Collaborative forecast hubs, where multiple teams submit predictions in real-time, are the gold standard for such efforts. For each hub, a Baseline model is used as a performance benchmark for other models. Although the Baseline is understood as a naïve forecast, its design is subjective, and the impact of model design decisions remains understudied. We evaluated how three Baseline specification decisions influence forecast performance on trend models that forecast based on historically observed dynamics: (1) the amount of historical data used for training, (2) whether the data are transformed, and (3) whether forecasts follow a
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