GPR and ANN based Prediction Models for COVID-19 Death Cases

COVID-19 pandemic now affects the entire world and has a major effect on the global economy. A number of medical researchers are currently working in various fields to tackle this pandemic and its circumstances. This paper aims of developing a model that can estimate the number of deaths in the affected cases based on the documented number of older (above 65 years of age), diabetic and smoking cases. The Gaussian Process Regression (GPR) approach has been used to build the model and its performance was compared with a corresponding Artificial Neural Network (ANN) model. The model was applied to reliable data published by the World Health Organization (WHO) for different countries in North America, Europe and the Gulf region. The model provided impressive results with an excellent prediction of data from all the countries under investigation. The model may be useful in estimating the number of deaths due to any arbitrary number of inputs. It would also help to prepare effective measures to minimize the number of deaths.