Disease Outbreak /Epidemic in Public Health Sector

Abstract

In recent eras, the COVID-19 pandemic has become a global phenomenon, significantly impacting efforts to anticipate the disease outbreak in its early stages. Hence, timely detection and analysis within the public health sector can lead to early control of outbreaks/epidemics. Due to a high level of uncertainty and lack of outbreak essential data, standard models have shown low accuracy for long-term prediction. Although the literature review includes several attempts to address this issue, the essential generalization and robustness of the abilities of existing models need to be improved. After investigating the most recent studies, health-related data can be analyzed and interpreted using Machine Learning (ML) techniques to identify potential disease outbreaks/epidemics, facilitate prompt treatment, and ultimately result in cost savings for medical care. This research aims to assess the performance and predictive capabilities of different machine learning algorithms to identify the most accurate and reliable models for disease prediction. The results of this research are to propose a novel framework for early outbreak/epidemic detection using ML techniques. Additionally, conduct a comparative analysis of studies that have utilized ML techniques to detect disease outbreaks.