Source detection on networks using spatial temporal graph convolutional networks

Detecting the source of an outbreak cluster during a pandemic like COVID-19 can provide insights into the transmission process, associated risk factors, and help contain the spread. In this work we study the problem of source detection from multiple snapshots of spreading on an arbitrary network structure. We use a spatial temporal graph convolutional network based model (SD-STGCN) to produce a source probability distribution, by fusing information from temporal and topological spaces. We perform extensive experiments using popular compartmental simulation models over synthetic networks and empirical contact networks. We also demonstrate the applicability of our approach with real COVID-19 case data.

Automated Detection of COVID-19 using Chest X-Ray Images and CT Scans through Multilayer-Spatial Convolutional Neural Networks

The novel coronavirus-2019 (Covid-19), a contagious disease became a pandemic and has caused overwhelming effects on the human lives and world economy. The detection of the contagious disease is vital to avert further spread and to promptly treat the infected people. The need of automated scientific assisting diagnostic methods to identify Covid-19 in the infected people has increased since less accurate automated diagnostic methods are available. Recent studies based on the radiology imaging suggested that the imaging patterns on X-ray images and Computed Tomography (CT) scans contain leading information about Covid-19 and is considered as a potential automated diagnosis method. Machine learning and deep learning techniques combined with radiology imaging can be helpful for accurate detection of the disease. A deep learning approach based on the multilayer-Spatial Convolutional Neural Network for automatic detection of Covid-19 using chest X-ray images and CT scans is proposed in this paper. The proposed model, named as the Multilayer Spatial Covid Convolutional Neural Network (MSCovCNN), provides an automated accurate diagnostics for Covid-19 detection. The proposed model showed 93.63% detection accuracy and 97.88% AUC (Area Under Curve) for chest x-ray images and 91.44% detection accuracy and 95.92% AUC for chest CT scans, respectively. We have used 5-tiered 2D-CNN frameworks followed by the Artificial Neural Network (ANN) and softmax classifier. In the CNN each convolution layer is followed by an activation function and a Maxpooling layer. The proposed model can be used to assist the radiologists in detecting the Covid-19 and confirming their initial screening.