More Like this

1. Using Mobility Data to Understand and Forecast COVID19 Dynamics

   Wang, L; Ben, X; Adiga, A; Tendulkar, A; Venkatramanan, S; Vullikanti, A; Aggarwal, G; Talekar, A; Chen, J; Lewis, B; et al (December 2020, medRxiv)

   null (Ed.)

   Disease dynamics, human mobility, and public policies co-evolve during a pandemic such as COVID-19. Understanding dynamic human mobility changes and spatial interaction patterns are crucial for understanding and forecasting COVID- 19 dynamics. We introduce a novel graph-based neural network(GNN) to incorporate global aggregated mobility flows for a better understanding of the impact of human mobility on COVID-19 dynamics as well as better forecasting of disease dynamics. We propose a recurrent message passing graph neural network that embeds spatio-temporal disease dynamics and human mobility dynamics for daily state-level new confirmed cases forecasting. This work represents one of the early papers on the use of GNNs to forecast COVID-19 incidence dynamics and our methods are competitive to existing methods. We show that the spatial and temporal dynamic mobility graph leveraged by the graph neural network enables better long-term forecasting performance compared to baselines. 

   more » « less
2. High-Resolution Spatio-Temporal Model for County-Level COVID-19 Activity in the U.S.

   https://doi.org/10.1145/3468876  

   Zhu, Shixiang; Bukharin, Alexander; Xie, Liyan; Santillana, Mauricio; Yang, Shihao; Xie, Yao (December 2021, ACM Transactions on Management Information Systems)

   We present an interpretable high-resolution spatio-temporal model to estimate COVID-19 deaths together with confirmed cases 1 week ahead of the current time, at the county level and weekly aggregated, in the United States. A notable feature of our spatio-temporal model is that it considers the (1) temporal auto- and pairwise correlation of the two local time series (confirmed cases and deaths from the COVID-19), (2) correlation between locations (propagation between counties), and (3) covariates such as local within-community mobility and social demographic factors. The within-community mobility and demographic factors, such as total population and the proportion of the elderly, are included as important predictors since they are hypothesized to be important in determining the dynamics of COVID-19. To reduce the model’s high dimensionality, we impose sparsity structures as constraints and emphasize the impact of the top 10 metropolitan areas in the nation, which we refer to (and treat within our models) as hubs in spreading the disease. Our retrospective out-of-sample county-level predictions were able to forecast the subsequently observed COVID-19 activity accurately. The proposed multivariate predictive models were designed to be highly interpretable, with clear identification and quantification of the most important factors that determine the dynamics of COVID-19. Ongoing work involves incorporating more covariates, such as education and income, to improve prediction accuracy and model interpretability. 

   more » « less
3. Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting

   Wang, L; Adiga, A; Venkatramanan, S; Chen, J; Lewis, B; Marathe, M (November 2020, ArXivorg)

   The COVID-19 pandemic represents the most significant public health disaster since the 1918 influenza pandemic. During pandemics such as COVID-19, timely and reliable spatiotemporal forecasting of epidemic dynamics is crucial. Deep learning-based time series models for forecasting have recently gained popularity and have been successfully used for epidemic forecasting. Here we focus on the design and analysis of deep learning-based models for COVID-19 forecasting. We implement multiple recurrent neural network-based deep learning models and combine them using the stacking ensemble technique. In order to incorporate the effects of multiple factors in COVID-19 spread, we consider multiple sources such as COVID-19 confirmed and death case count data and testing data for better predictions. To overcome the sparsity of training data and to address the dynamic correlation of the disease, we propose clustering-based training for high-resolution forecasting. The methods help us to identify the similar trends of certain groups of regions due to various spatio-temporal effects. We examine the proposed method for forecasting weekly COVID-19 new confirmed cases at county-, state-, and country-level. A comprehensive comparison between different time series models in COVID-19 context is conducted and analyzed. The results show that simple deep learning models can achieve comparable or better performance when compared with more complicated models. We are currently integrating our methods as a part of our weekly forecasts that we provide state and federal authorities. 

   more » « less
4. Graph Frequency Analysis of COVID-19 Incidence to Identify County-Level Contagion Patterns in the United States

   https://doi.org/10.1109/ICASSP39728.2021.9414854  

   Li, Yang; Mateos, Gonzalo (June 2021, 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

   null (Ed.)

   The COVID-19 pandemic severely changed the way of life in the United States (US). From early scattered regional outbreaks to current country-wide spread, and from rural areas to highly populated cities, the contagion exhibits diverse patterns at various timescales and locations. We thus conduct a graph frequency analysis to inves- tigate the spread patterns of COVID-19 in different US counties. The commute flows between all 3142 US counties were used to construct a graph capturing the population mobility. The numbers of daily confirmed COVID-19 cases per county were collected and represented as graph signals, which were then mapped into the frequency domain via the graph Fourier transform. The concept of graph frequency in Graph Signal Processing (GSP) enables the decomposition of graph signals (i.e., daily confirmed cases) into modes with smooth or rapid variations with respect to the underlying mobility graph. These different modes of variability are shown to relate to COVID-19 spread patterns within and across counties. Changes in the nature of spread within geographical regions are also revealed by graph frequency analysis at finer temporal scales. Overall, our GSP-based approach leverages case count and mobility data to unveil spatio-temporal contagion patterns of COVID-19 incidence for each US county. Results here support the promising prospect of using GSP tools for epidemiology knowledge discovery on graphs. 

   more » « less
5. COVID-GAN: Estimating Human Mobility Responses to COVID-19 Pandemic through Spatio-Temporal Conditional Generative Adversarial Networks

   https://doi.org/10.1145/3397536.3422261  

   Bao, Han; Zhou, Xun; Zhang, Yingxue; Li, Yanhua; Xie, Yiqun (November 2020, Proceedings of the 28th International Conference on Advances in Geographic Information Systems)

   null (Ed.)

   The COVID-19 pandemic has posed grand challenges to policy makers, raising major social conflicts between public health and economic resilience. Policies such as closure or reopen of businesses are made based on scientific projections of infection risks obtained from infection dynamics models. While most parameters in infection dynamics models can be set using domain knowledge of COVID-19, a key parameter - human mobility - is often challenging to estimate due to complex social contexts and limited training data under escalating COVID-19 conditions. To address these challenges, we formulate the problem as a spatio-temporal data generation problem and propose COVID-GAN, a spatio-temporal Conditional Generative Adversarial Network, to estimate mobility (e.g., changes in POI visits) under various real-world conditions (e.g., COVID-19 severity, local policy interventions) integrated from multiple data sources. We also introduce a domain-constraint correction layer in the generator of COVID-GAN to reduce the difficulty of learning. Experiments using urban mobility data derived from cell phone records and census data show that COVID-GAN can well approximate real-world human mobility responses, and that the proposed domain-constraint based correction can greatly improve solution quality. 

   more » « less