{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T17:14:43Z","timestamp":1775841283244,"version":"3.50.1"},"reference-count":36,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T00:00:00Z","timestamp":1652918400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<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>","DOI":"10.1371\/journal.pcbi.1010100","type":"journal-article","created":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T17:26:22Z","timestamp":1652981182000},"page":"e1010100","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":40,"title":["Enhancing long-term forecasting: Learning from COVID-19 models"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2784-9042","authenticated-orcid":true,"given":"Hazhir","family":"Rahmandad","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5832-9226","authenticated-orcid":true,"given":"Ran","family":"Xu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3632-8588","authenticated-orcid":true,"given":"Navid","family":"Ghaffarzadegan","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,5,19]]},"reference":[{"key":"pcbi.1010100.ref001","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-306-47630-3","volume-title":"Principles of forecasting: a handbook for researchers and practitioners","author":"J.S. 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