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COVID-19 (coronavirus disease 2019) pandemic caused by SARS-CoV-2 has led toa treacherous and devastating catastrophe for humanity. At the time of writing,no specific antivirus drugs or vaccines are recommended to control infectiontransmission and spread. The current diagnosis of COVID-19 is done byReverse-Transcription Polymer Chain Reaction (RT-PCR) testing. However, thismethod is expensive, time-consuming, and not easily available in straitenedregions. An interpretable and COVID-19 diagnosis AI framework is devised anddeveloped based on the cough sounds features and symptoms metadata to overcomethese limitations. The proposed framework's performance was evaluated using amedical dataset containing Symptoms and Demographic data of 30000 audiosegments, 328 cough sounds from 150 patients with four cough classes (COVID-19, Asthma, Bronchitis, and Healthy). Experiments' results show that themodel captures the better and robust feature embedding to distinguish betweenCOVID-19 patient coughs and several types of non-COVID-19 coughs with higherspecificity and accuracy of 95.04 $\pm$ 0.18% and 96.83$\pm$ 0.18%respectively, all the while maintaining interpretability.

Pay Attention to the cough: Early Diagnosis of COVID-19 using Interpretable Symptoms Embeddings with Cough Sound Signal Processing