ABSTRACT
Epidemic simulation traditionally serves as one of the important methods to forecast how an epidemic may spread among a population. However, there are two key limitations that restrict the scope of such methods. The first limitation is that the existing tools rely on different sets of static parameters (e.g., infection probability, recovering probability) for simulating an epidemic spread that may fail to capture the dynamic nature of population interactions that acts as a dominant factor in an epidemic spread scenario such as COVID-19 pandemic. To handle this challenge, we propose a machine learning based model that combines a Graph Convolutional Neural Network (GCN) and a Recurrent Neural Network (RNN). It integrates the ability of the GCN to capture spatial dependency in human interaction and the ability of the RNN to incorporate temporal effects of the virus spread. The second limitation is that these methods do not address the computation overhead problem when dealing with time-dynamic graphs. Training a GCN on a very large graph suffers from the communication overhead from different graph partitions and the computation overheads stemming from partitioning dynamic graphs. This limitation impacts the scalability of the existing systems. To solve this challenge, we partition the graph in a computationally less expensive manner by partitioning the graph using the min-cut principle. We conducted comprehensive large scale real-world human mobility data driven experiments. Our experimental result shows that the proposed machine learning based forecasting model achieves overall 84% classification accuracy with greater than 72% precision and 62% recall. Also, the proposed graph partitioning approach reduces computation time and commutation overhead by a significant margin.