Prognosis of COVID-19 spread in over 200 US counties: A deep learning study on the effect of human mobility and social behavior
Code and data (available upon request) accompanying the manuscript titled "Prognosis of COVID-19 spread in over 200 US counties: A deep learning study on the effect of human mobility and social behavior ", authored by Mohamed Aziz Bhouri, Francisco Sahli Costabal, Hanwen Wang, Kevin Linka, Mathias Peirlinck, Ellen Kuhl and Paris Perdikaris.
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. A sensitivity analysis is also performed to assess the importance of the different mobility and social behavior parameters on the spread of the virus, showing that attendance of close places such as workplaces, residential, and retail and recreation locations has the biggest impact on the variation of the basic reproduction number. The model also enables us to quantitatively investigate the effects of government interventions, such as lock-down and re-opening plans. 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 inform policy and decision making.
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