Predictive modeling of COVID-19 transmission using machine learning algorithms

Thesis Abstract

Abstract
The outbreak of the novel coronavirus disease 2019 (COVID-19) has led to a global health crisis, challenging healthcare systems and economies worldwide. In response to this crisis, various predictive modeling techniques have been employed to forecast the transmission dynamics of COVID-19 and aid in decision-making processes. This thesis explores the application of machine learning algorithms in predictive modeling of COVID-19 transmission to enhance our understanding of the disease spread and inform public health interventions. Chapter One of this thesis provides an introduction to the research topic, discussing the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The literature review in Chapter Two presents a comprehensive analysis of existing research on COVID-19 transmission modeling, machine learning algorithms, and their applications in epidemiology. Chapter Three outlines the research methodology, including data collection, preprocessing, feature selection, model development, and evaluation metrics. The methodology also describes the selection of appropriate machine learning algorithms such as Support Vector Machines, Random Forest, and Neural Networks for COVID-19 transmission prediction. Furthermore, it discusses the validation techniques and parameter tuning to ensure the robustness and accuracy of the predictive models. Chapter Four presents a detailed discussion of the findings from the predictive modeling experiments. The results of the machine learning algorithms are analyzed in terms of their predictive performance, interpretability, and potential implications for public health policy. The chapter highlights the strengths and limitations of each model and provides insights into the factors influencing COVID-19 transmission dynamics. In the concluding Chapter Five, the thesis summarizes the key findings, implications, and contributions to the field of epidemiology and public health. It discusses the practical utility of machine learning-based predictive modeling in understanding and controlling the transmission of COVID-19. The thesis concludes with recommendations for future research directions and policy implications based on the insights gained from the study. Overall, this thesis contributes to the growing body of knowledge on predictive modeling of COVID-19 transmission using machine learning algorithms. By leveraging advanced computational techniques, this research aims to improve the accuracy and timeliness of COVID-19 predictions, thereby supporting effective public health responses and mitigating the impact of the pandemic on society.