Integrative Machine Learning Framework for Accurate COVID-19 Forecasting

This research paper introduces a machine learning (ML) forecasting model for COVID-19cases to investigate the performance of machine learning algorithms and develop a new procedure toimprove prediction efficiency. Utilizing the Multilayer Perceptron (MLP), Linear Regression (LR),K-Nearest Neighbours (KNN), Support Vector Machines (SVM), and the proposed approach, the studyconsiders the COVID-19 data to forecast case numbers. The study contextualizes its findings througha systematic methodology of dataset compilation, algorithm interpretation, and framework development.It uses measures such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and RootMean Squared Error (RMSE) to evaluate the predictive performance. The trend in COVID-19 dataand predictions from different algorithms is shown through graphical illustrations. The same goes forthe proposed framework predictions. This study demonstrates that the proposed LR approach and theframework outperform previous MLP, KNN, and SVM models, suggesting the relevance of explainableand robust modelling solutions for COVID-19 risk assessment. Our suggested framework, whichoutperforms individual algorithms by averaging their results after combining them, significantly reducesprediction errors. Discussion of implications of forecasts for interventions, resource allocation, andpolicy decisions is done, with the need for accurate forecasting in the pandemic response highlighted.Further research can be expected to improve the framework, incorporate new machine learning (ML)techniques, and deploy real-time adaptive modelling systems. Collaboration between researchers,policymakers, and healthcare practitioners is crucial for adopting research results into practice andpromoting evidence-based decision-making in outbreak response and preparedness. Generally, thisstudy shows the progress of epidemiological forecasting and provides important information infighting COVID-19 and future epidemics.