2021
DOI: 10.1016/j.cma.2021.113891
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COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior

Abstract: 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 sp… Show more

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Cited by 43 publications
(45 citation statements)
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“…The ongoing COVID-19 pandemic has caused immense disruptions to our day-to-day normal life. Admittedly, human mobility plays an important role in spreading the virus ( Bhouri et al, 2021 , Bryant and Elofsson, 2020 , Chang et al, 2021 ). For this reason, to control the spread of the virus various human mobility restrictions, such as stay at home order, limited public gathering, non-essential business closures had been extensively exercised as a crucial non-pharmaceutical intervention tool all over the world.…”
Section: Introductionmentioning
confidence: 99%
“…The ongoing COVID-19 pandemic has caused immense disruptions to our day-to-day normal life. Admittedly, human mobility plays an important role in spreading the virus ( Bhouri et al, 2021 , Bryant and Elofsson, 2020 , Chang et al, 2021 ). For this reason, to control the spread of the virus various human mobility restrictions, such as stay at home order, limited public gathering, non-essential business closures had been extensively exercised as a crucial non-pharmaceutical intervention tool all over the world.…”
Section: Introductionmentioning
confidence: 99%
“…Using a LSTM network, for example, the combination of mobility and meteorological data was found to be the primary factor in case prediction [19]. A susceptible to infectious transition rate, as a function of mobility and social behavior with time-dynamics parameters, was modeled by a deep LSTM, which was further integrated into a susceptibleexposed-infectious-recovered (SEIR) model for case forecast [20]. A graph neural network is another attempt to capture the spatio-temporal dynamics, where spatial edges represent mobility-based inter-region connectivity and temporal edges represent node features through time [21].…”
Section: Existing Research On Mobility Patterns and Covid-19mentioning
confidence: 99%
“…We consider a time window of 15 days. • DeepCovid-19 [24]: This is an existing deep learningbased model for COVID forecasting, that was designed for county level forecasting of positive cases using mobility features. Similar to LSTM, we use our ML-Dataset as a mobility feature.…”
Section: Baseline Modelsmentioning
confidence: 99%
“…Bhouri et al [24] used Google's mobility data to use as features for their proposed DL-based model for doing countywise forecasting of positive cases for over 200 counties. However, they do not address the correlation among different time series or the spatial dependency among different counties.…”
Section: Introductionmentioning
confidence: 99%