Enhancing long-term forecasting: Learning from COVID-19 models
Author(s)
Rahmandad, Hazhir; Xu, Ran; Ghaffarzadegan, Navid
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<jats:p>While much effort has gone into building predictive models of the COVID-19 pandemic, some have argued that early exponential growth combined with the stochastic nature of epidemics make the long-term prediction of contagion trajectories impossible. We conduct two complementary studies to assess model features supporting better long-term predictions. First, we leverage the diverse models contributing to the CDC repository of COVID-19 USA death projections to identify factors associated with prediction accuracy across different projection horizons. We find that better long-term predictions correlate with: (1) capturing the physics of transmission (instead of using black-box models); (2) projecting human behavioral reactions to an evolving pandemic; and (3) resetting state variables to account for randomness not captured in the model before starting projection. Second, we introduce a very simple model, SEIRb, that incorporates these features, and few other nuances, offers informative predictions for as far as 20-weeks ahead, with accuracy comparable with the best models in the CDC set. Key to the long-term predictive power of multi-wave COVID-19 trajectories is capturing behavioral responses endogenously: balancing feedbacks where the perceived risk of death continuously changes transmission rates through the adoption and relaxation of various Non-Pharmaceutical Interventions (NPIs).</jats:p>
Date issued
2022-05Department
Sloan School of ManagementJournal
PLOS Computational Biology
Publisher
Public Library of Science (PLoS)
Citation
Rahmandad, Hazhir, Xu, Ran and Ghaffarzadegan, Navid. 2022. "Enhancing long-term forecasting: Learning from COVID-19 models." PLOS Computational Biology, 18 (5).
Version: Final published version