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

Forecasting the spread of infectious diseases is increasingly used to support public health decision-making during outbreaks. Many large forecasting efforts operate through collaborative forecast hubs, where multiple research teams submit predictions that are evaluated together. In these systems, a simple Baseline model serves as a benchmark against which more complex models are compared. However, the design of these Baseline models often involves subjective choices that have rarely been systematically studied. We examined how several common design decisions affect the performance of widely used Baseline approaches using multiple years of data for several respiratory diseases. We found that small changes in parameterization can substantially alter forecast accuracy and, consequently, the relative rankings of other models. Across all datasets, a single simple Baseline configuration consistently achieved the strongest performance, outperforming the most commonly used model. Our findings highlight the central and understudied role that Baseline models play in collaborative forecast hubs, and provide guidance to hub organizers regarding the appropriate design and communication of Baseline models.