Coronavirus (COVID-19) is a new virus of viral pneumonia . It can outbreak in the world through person-to-person transmission . Although several medical companies provide cooperative monitoring healthcare systems, these solutions lack offering of the end-to-end management of the disease . The main objective of the proposed framework is to bridge the current gap between current technologies and healthcare systems . The wireless body area network, cloud computing, fog computing, and clinical decision support system are integrated to provide a comprehensive and complete model for disease detection and monitoring . By monitoring a person with COVID-19 in real time, physicians can guide patients with the right decisions . The proposed framework has three main layers (i.e., a patient layer, cloud layer, and hospital layer). In the patient layer, the patient is tracked through a set of wearable sensors and a mobile app . In the cloud layer, a fog network architecture is proposed to solve the issues of storage and data transmission . In the hospital layer, we propose a convolutional neural network-based deep learning model for COVID-19 detection based on patient ’ s X-ray scan images and transfer learning . The proposed model achieved promising results compared to the state-of-the art (i.e., accuracy of 97.95% and specificity of 98.85 %). Our framework is a useful application, through which we expect significant effects on COVID-19 proliferation and considerable lowering in healthcare expenses.