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The COVID-19 pandemic is one of the most challenging healthcare crises duringthe 21st century. As the virus continues to spread on a global scale, themajority of efforts have been on the development of vaccines and the massimmunization of the public. While the daily case numbers were following adecreasing trend, the emergent of new virus mutations and variants still pose asignificant threat. As economies start recovering and societies start openingup with people going back into office buildings, schools, and malls, we stillneed to have the ability to detect and minimize the spread of COVID-19.Individuals with COVID-19 may show multiple symptoms such as cough, fever, andshortness of breath. Many of the existing detection techniques focus onsymptoms having the same equal importance. However, it has been shown that somesymptoms are more prevalent than others. In this paper, we present a multimodalmethod to predict COVID-19 by incorporating existing deep learning classifiersusing convolutional neural networks and our novel probability-based weightingfunction that considers the prevalence of each symptom. The experiments wereperformed on an existing dataset with respect to the three considered modes ofcoughs, fever, and shortness of breath. The results show considerableimprovements in the detection of COVID-19 using our weighting function whencompared to an equal weighting function.

Multimodal Detection of COVID-19 Symptoms using Deep Learning & Probability-based Weighting of Modes