Performance Evaluation on COVID-19 Prediction using Machine Learning Models

The COVID-19 pandemic has placed enormous strain on providing health care services internationally while reinforcing the argument for the need to strengthen forecasting techniques. Existing forecasting methods have drawbacks, especially in determining the long-term consequences of the pandemic and understanding its broad reach across various locations and populations. This project proposes an evaluation of machine learning (ML) models with the aim of improving predictions, particularly the accuracy in long-term forecasting, of subsequent trends of the COVID-19 pandemic. A systematic review highlights previous forecasting attempts as a reference for the approach. This project emphasizes extensive data collection, model formulation and testing to develop a strong prediction framework. The models considered for evaluation are Support Vector Regression (SVR), seasonal autoregressive integrated moving average (SARIMA), and artificial neural networks (ANN), which have overcome some of the deficiencies of epidemiological forecasting methods to date. The aim is to provide public health representatives with more rigorous forecasts, which could enhance planning and response measures and protect health and safety. Our findings show that the ANN model is superior, with high accuracy and comprehensive performance, confirming its broader use in various predictive applications. The Root Mean Square Error (RMSE) of prediction error was also relatively modest (R-square values were nearly 1).