Abstract:
Coronavirus is a type of virus that belongs to the family of (+)ssRNA Virus that causes a disease called Coronavirus Disease 2019 (COVID-19) which gives rise to severe acute respiratory conditions. Due to its mode of transmission, contact with respiratory droplets during coughing or sneezing and physical contact with infected persons has resulted to over 160,000 recorded deaths worldwide and counting within few months of the outbreak; hence, the disease was declared a pandemic by World Health Organization in February, 2020. In recent past, several systems have been developed to diagnose this pandemic disease, but they generated a lot of false negative during testing and were unable to detect COVID-19 and its overlapping symptoms. Hence, in this paper, we proposed and simulated a Bayesian Belief Network model to predict Coronavirus Disease 2019. The model was designed using Bayes Server and tested with data collected from COVID-19 medical repository. The model had a 99% prediction accuracy.
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