Adaptive multi-model ensembles for improved epidemic projections and decision support

作者：Stefania Fiandrino, Daniela Paolotti, Clara Bay, Matteo Chinazzi, Jessica T. Davis, Samantha J. Bents, Amanda C. Perofsky, James Turtle, Pete Riley, M. Ben-Nun, Sean M. Moore, T. Alex Perkins, Guido España, Ajitesh Srivastava, Majd Al Aawar, Shraddha Ramdas Bandekar, Kaiming Bi, Anass Bouchnita, Spencer J. Fox, Lauren Ancel Meyers, Srinivasan Venkatramanan, Przemyslaw Porebski, Aniruddha Adiga, Bryan Lewis, Madhav Marathe, Fardad Haghpanah, Eili Klein, Sara L. Loo, Sung-mok Jung, Claire P. Smith, Lucie Contamin, Harry Hochheiser, Erica C. Carcelén, Emily Howerton, Katriona Shea, Katie Yan, Michael C. Runge, Cécile Viboud, Carl A. B. Pearson, Shaun Truelove, Justin Lessler, Rebecca K. Borchering, Matthew Biggerstaff, Nicolò Gozzi, Alessandro Vespignani · 发表于：medRxiv · 年份：2026 · DOI：10.64898/2026.06.26.26356648 · 被引用次数：1 · 研究领域：COVID-19 epidemiological studies、Data-Driven Disease Surveillance、Influenza Virus Research Studies

In recent years, the use of multi-model ensemble projections in infectious disease modeling has become an established methodological approach to account for and integrate across uncertainties and structural differences present in individual models. However, the creation of long-term ensemble projections through these coordinated efforts is resource-intensive, demanding the input of multiple research teams and substantial computational power. This typically limits the ability to refine projections, update the selection of plausible epidemic trajectories, or expand the number of scenarios that can be assessed, even as new empirical data become available. To address this challenge, we define an adaptive ensemble approach that, analogously to a multi-model particle filtering method, dynamically selects individual model trajectories based on observed data throughout the epidemic projection period. We demonstrate the effectiveness of this methodology using the U.S. Flu Scenario Modeling Hub (SMH) projections for influenza hospitalizations in the United States during the 2023-2024 and 2024-2025 winter seasons. Our findings show that the adaptive ensemble yields improved predictive accuracy with respect to the original SMH ensemble projections across several scoring rules and geographical resolutions. Furthermore, the adaptive ensemble approach offers two additional applications: i) the dynamic assignment of posterior probabilities to epidemic scenarios, identifying the most plausibl...