TFA-CLSTMNN: Novel convolutional network for sound-based diagnosis of COVID-19

The outbreak of the global COVID-19 pandemic has become a public crisis and is threatening human life in every country. Recently, researchers have developed testing methods via patients cough recordings. In order to improve the testing accuracy, in this paper, we establish a novel COVID-19 sound-based diagnosis framework, i.e. TFA-CLSTMNN, which integrates time-frequency domain features of the recorded cough with the Attention-Convolution Long Short-Term Memory Neural Network. Specifically, we calculate the Mel-frequency cepstrum coefficient (MFCC) of the cough data to extract the time-frequency domain features. We then apply the convolutional neural network and the attentional mechanism on the time-frequency features, which is followed by the long short-term memory neural network to analyze the MFCC features of the data. The recognition and classification can be then carried out to evaluate the positiveness or negativeness of the tested samples. Experimental results show that the proposed TFA-CLSTMNN framework outperforms the baseline neural networks in sound-based COVID-19 diagnosis and derives an accuracy over 0.95 on the public real-world datasets.

Assessment of Infection Risk Based on Travel Behavior Analysis -Example of Jiangsu Province Travel during COVID-19

In order to correctly evaluate the travel infection risks during the COVID-19 pandemic, this study proposed an infection risk assessment model based on the travel behavior analysis. Using the epidemiological survey data and online questionnaire data in Jiangsu, China this study developed and calibrated the travel behavior models for virus carriers and ordinary individuals. The travel behaviors of virus carriers and ordinary individuals were also compared. The infection risks were evaluated for different travel modes and travel activities. The results indicate that: (1) implementing strict traffic control measures significantly reduces the infection risk;(2) the infection risk of medical treatment travel activities is significantly higher than other travel activities;(3) business or leisure travel activities expose to a higher infection risk in the early stages of the pandemic;(4) the risk of non-motor vehicle travel is relatively low. Copyright © 2020 by Science Press.