This resembles the core insight behind graph neural network models, i.e. d e j i, t ∈ ℝ H × 2 where N j active ( t ) is the number of active cases (cumulative cases minus recovered cases and deaths) and N j popu is the population of region j.Ī region’s COVID-19 dynamics can potentially be affected by regions where frequent travels occur between them. At a certain time point t, if there is any human movement from region j to region i in the past H days, we add a directed edge e ji that connects region j and i, and associate it with the inter-region mobility flow f ji( t) and flow of active cases from source region (defined as f j i active ( t ) = N j active ( t ) N j popu * f j i ( t )) as the edge feature i.e. The graph edge features are derived from the inter-region mobility by aggregating Google mobility data to the state or county level. We include daily new case count, new death count and intra-region mobility flow ( f ii( t) which represents the MF from v i to v i during time t) as the node features, i.e.
We construct a dynamic mobility graph G ( V, E, T ), where each node feature d v includes a sequence of dynamic observations regarding the the region in a history window H ≤ T. In our work, we introduce a novel method to incorporate global population mobility flows into graph-based spatial-temporal neural networks for COVID-19 dynamic forecasting. However few of the existing works investigate spatio-temporal forecasting using graph neural network (GNN) with integrated real-time mobility data. On the other side, lots of COVID-19 forecasting methods are proposed since the initial outbreak early this year, such as mechanistic methods, and time series methods using statistical regression models or deep learning model. There have been a number of recent studies along these lines, for example, in China using Baidu data, in the US using mobility data, and at a global scale using airline traffic. Using aggregate mobility data to understand COVID-19 dynamics has received wide interests recently. In this work, we focus on applying AI-based techniques to solve the above challenge by incorporating a new large-scale aggregated spatio-temporal mobility data into graph-based neural networks. As machine learning and artificial intelligence(AI) has been successful in many domains, there is an urge to investigate how we can leverage AI-based technologies for infectious disease understanding, modeling, forecasting, and controlling. To better understand COVID-19 dynamics and help to control the disease spread, it is crucial and challenging to provide accurate and timely spatio-temporal forecasting of epidemic dynamics. The social distancing measures have led to significant change in human mobility behaviour while the mobility change has also affected the disease dynamics inevitably.
The social distancing measures are one of the most effective nonpharmaceutical interventions at this stage. The COVID-19 pandemic has affected almost every country in the world and has resulted in an unprecedented response by governments across the world to control its spread.
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