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

Journal Article · · Computer Methods in Applied Mechanics and Engineering
 [1];  [2];  [3];  [4];  [4];  [4];  [3]
  1. University of Pennsylvania, Philadelphia, PA (United States); OSTI
  2. Pontificia Universidad Católica de Chile, Santiago (Chile)
  3. University of Pennsylvania, Philadelphia, PA (United States)
  4. Stanford University, CA (United States)
+ Show Author Affiliations

This 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. Furthermore, 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.

Research Organization:
University of Pennsylvania, Philadelphia, PA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); Defense Advanced Research Projects Agency (DARPA); US Air Force Office of Scientific Research (AFOSR); National Institutes of Health (NIH)
Grant/Contract Number:
SC0019116
OSTI ID:
1852839
Alternate ID(s):
OSTI ID: 1782008
Journal Information:
Computer Methods in Applied Mechanics and Engineering, Journal Name: Computer Methods in Applied Mechanics and Engineering Journal Issue: C Vol. 382; ISSN 0045-7825
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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Figures / Tables (20)

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Related Subjects

60 APPLIED LIFE SCIENCES
Dynamical systems
Engineering
Epidemiology model discovery
Mathematics
Mechanics
Neural networks
Scientific machine learning
Sensitivity analysis
Uncertainty quantification