Design and Implementation of an Online Coronavirus Testing Software

Abstract

The coronavirus disease (COVID-19) has spread across the world and has been classified as a pandemic and a public health emergency of global importance. Researchers have claimed that one of the reasons the coronavirus is deadly is because it attacks the respiratory system, which is like severe acute respiratory syndrome (SARS). The coronavirus causes shortness of breath and reduces the oxygen level in the blood. The coronavirus can also cause fever, taste and smell loss, and other symptoms. Early detection of infected persons and thorough contact tracing helps in reducing and mitigating the transmission of this virus. However, in rural communities where resources for testing are little or are not even available, this aim becomes unachievable. This system focuses on designing and implementing a coronavirus testing software that utilizes a deep neural model to detect the probability of COVID-19 based on user input data. After a one-time registration, users answer a few questions about how they feel, and the input data is then fed into the DNN model. The model is designed to detect if the individual is a suspect of COVID-19, providing a list of COVID-19 test laboratories based on their current location. With early detection of infected individuals and thorough contact tracing, this system aims to reduce and mitigate the transmission of COVID-19, especially in rural communities with limited resources for testing.

Keywords — COVID-19; Web application; Deep learning; Coronavirus