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Enhancing long-term forecasting: Learning from COVID-19 models

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dc.contributor.authorRahmandad, Hazhiren
dc.contributor.authorXu, Ranen
dc.contributor.authorGhaffarzadegan, Naviden
dc.date.accessioned2023-01-30T18:01:10Zen
dc.date.available2023-01-30T18:01:10Zen
dc.date.issued2022-05-01en
dc.date.updated2023-01-28T15:46:06Zen
dc.description.abstractWhile 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).en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1371/journal.pcbi.1010100en
dc.identifier.eissn1553-7358en
dc.identifier.issn1553-734Xen
dc.identifier.issue5en
dc.identifier.orcidGhaffarzadegan, Navid [0000-0003-3632-8588]en
dc.identifier.otherPCOMPBIOL-D-21-02038 (PII)en
dc.identifier.pmid35587466en
dc.identifier.urihttp://hdl.handle.net/10919/113560en
dc.identifier.volume18en
dc.language.isoenen
dc.publisherPLOSen
dc.relation.urihttps://www.ncbi.nlm.nih.gov/pubmed/35587466en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectepidemicsen
dc.subjectforecasten
dc.subjectSEIRen
dc.subjectbehavior changeen
dc.subjectsystem dynamicsen
dc.subject.meshHumansen
dc.subject.meshForecastingen
dc.subject.meshPandemicsen
dc.subject.meshCOVID-19en
dc.titleEnhancing long-term forecasting: Learning from COVID-19 modelsen
dc.title.serialPLoS Computational Biologyen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherJournal Articleen
dcterms.dateAccepted2022-04-12en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Industrial and Systems Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen
pubs.organisational-group/Virginia Tech/Report testen

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