COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior

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dc.contributor.authorBhouri, Mohamed Aziz
dc.contributor.authorCostabal, Francisco Sahli
dc.contributor.authorWang, Hanwen
dc.contributor.authorLinka, Kevin
dc.contributor.authorPeirlinck, Mathias
dc.contributor.authorKuhl, Ellen
dc.contributor.authorPerdikaris, Paris
dc.date.accessioned2025-01-20T23:51:07Z
dc.date.available2025-01-20T23:51:07Z
dc.date.issued2021
dc.description.abstractThis paper presents a deep learning framework for epidemiology system identification from noisy and sparse observations with quantified uncertainty. The proposed approach employs an ensemble of deep neural networks to infer the time-dependent reproduction number of an infectious disease by formulating a tensor-based multi-step loss function that allows us to efficiently calibrate the model on multiple observed trajectories. The method is applied to a mobility and social behavior-based SEIR model of COVID-19 spread. The model is trained on Google and Unacast mobility data spanning a period of 66 days, and is able to yield accurate future forecasts of COVID-19 spread in 203 US counties within a time-window of 15 days. Interestingly, a sensitivity analysis that assesses the importance of different mobility and social behavior parameters reveals that attendance of close places, including workplaces, residential, and retail and recreational locations, has the largest impact on the effective reproduction number. The model enables us to rapidly probe and quantify the effects of government interventions, such as lock-down and re-opening strategies. Taken together, the proposed framework provides a robust workflow for data-driven epidemiology model discovery under uncertainty and produces probabilistic forecasts for the evolution of a pandemic that can judiciously provide information for policy and decision making. All codes and data accompanying this manuscript are available at https://github.com/PredictiveIntelligenceLab/DeepCOVID19. (C) 2021 Elsevier B.V. All rights reserved.
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.cma.2021.113891
dc.identifier.eissn1879-2138
dc.identifier.issn0045-7825
dc.identifier.urihttps://doi.org/10.1016/j.cma.2021.113891
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/94770
dc.identifier.wosidWOS:000654331500001
dc.language.isoen
dc.revistaComputer methods in applied mechanics and engineering
dc.rightsacceso restringido
dc.subjectEpidemiology model discovery
dc.subjectSensitivity analysis
dc.subjectNeural networks
dc.subjectScientific machine learning
dc.subjectDynamical systems
dc.subjectUncertainty quantification
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleCOVID-19 dynamics across the US: A deep learning study of human mobility and social behavior
dc.typeartículo
dc.volumen382
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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