Enhancing long-term forecasting: Learning from COVID-19 models
Name
journal.pcbi.1010100.pdf
Description
Published version
Size
1.26 MB
Format
Adobe PDF
Checksum (MD5)
13f50bf26b48c04a8512df57abd301c3
Author(s)
Rahmandad, Hazhir
Xu, Ran
Ghaffarzadegan, Navid
Date Issued
May 2022
Journal
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
Abstract
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).
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1371/journal.pcbi.1010100
