A new approach to COVID-19 data mining: A deep spatial-temporal prediction model based on tree structure for traffic revitalization index.

The outbreak of the COVID-19 epidemic has had a huge impact on a global scale and its impact has covered almost all human industries. The Chinese government enacted a series of policies to restrict the transportation industry in order to slow the spread of the COVID-19 virus in early 2020. With the gradual control of the COVID-19 epidemic and the reduction of confirmed cases, the Chinese transportation industry has gradually recovered. The traffic revitalization index is the main indicator for evaluating the degree of recovery of the urban transportation industry after being affected by the COVID-19 epidemic. The prediction research of traffic revitalization index can help the relevant government departments to know the state of urban traffic from the macro level and formulate relevant policies. Therefore, this study proposes a deep spatial-temporal prediction model based on tree structure for the traffic revitalization index. The model mainly includes spatial convolution module, temporal convolution module and matrix data fusion module. The spatial convolution module builds a tree convolution process based on the tree structure that can contain directional features and hierarchical features of urban nodes. The temporal convolution module constructs a deep network for capturing temporal dependent features of the data in the multi-layer residual structure. The matrix data fusion module can perform multi-scale fusion of COVID-19 epidemic data and traffic revitalization index data to further improve the prediction effect of the model. In this study, experimental comparisons between our model and multiple baseline models are conducted on real datasets. The experimental results show that our model has an average improvement of 21%, 18%, and 23% in MAE, RMSE and MAPE indicators, respectively.

Dynamic COVID risk assessment accounting for community virus exposure from a spatial-temporal transmission model

COVID-19 pandemic has caused unprecedented negative impacts on our society, including further exposing inequity and disparity in public health. To study the impact of socioeconomic factors on COVID transmission, we first propose a spatial-temporal model to examine the socioeconomic heterogeneity and spatial correlation of COVID-19 transmission at the community level. Second, to assess the individual risk of severe COVID-19 outcomes after a positive diagnosis, we propose a dynamic, varying-coefficient model that integrates individual-level risk factors from electronic health records (EHRs) with community-level risk factors. The underlying neighborhood prevalence of infections (both symptomatic and pre-symptomatic) predicted from the previous spatial-temporal model is included in the individual risk assessment so as to better capture the background risk of virus exposure for each individual. We design a weighting scheme to mitigate multiple selection biases inherited in EHRs of COVID patients. We analyze COVID transmission data in New York City (NYC, the epicenter of the first surge in the United States) and EHRs from NYC hospitals, where time-varying effects of community risk factors and significant interactions between individual- and community-level risk factors are detected. By examining the socioeconomic disparity of infection risks and interaction among the risk factors, our methods can assist public health decision-making and facilitate better clinical management of COVID patients. © 2021 Neural information processing systems foundation. All rights reserved.

STSIR: A Spatial Temporal Pandemic Model with Mobility Data - A COVID-19 Study

With the outbreak of COVID-19, how to mitigate and suppress its spread is a big issue to the government. Department of public health need powerful models to analyze and predict the trend and scale of such pandemic. And models that could evaluate the effect of the public policy are also essential to the fight with COVID-19. A main limitation of existing models is that they can only evaluate the policy by calculating R-0 after infection happens instead of giving observable index. To tackle this, based on the transmission characteristics of the COVID-19, we propose a novel framework Spatial-Temporal-Susceptible-Infected-Removed (STSIR) model. In particular, we combine both intra-city and inter-city mobility indices with the traditional SIR dynamics and make it a dynamic system. And we prove that the STSIR system is a closed system which makes the system self-consistent. And finally we proposed a Multi-Stage Simulated Annealing (MSSA) algorithm to find the optimal parameters of the system. In our experiments, based on Baidu Mobility dataset [1], and China pandemic dataset provided by Dingxiangyuan [2], our model can effectively predict the total scale of the pandemic and also give clear policy analysis with the observable index.