AN EXTENSIVE REVIEW OF THE EFFICIENCY OF MACHINE LEARNING METHODS IN DETECTING THE SPREAD AND IDENTIFYING THE PREVALENCE OF COVID-19

Abstract

The main focus of this study is to evaluate the effectiveness of machine learning for the detection and diagnosis of COVID 19. The COVID-19 pandemic has posed an unique challenge to global health, which calls for solutions that can quickly and accurately track its spread. This paper offers an extensive exploration of how machine learning (ML) methods have been employed to identify the prevalence and detect the spread of COVID-19 [1]. With a primary focus on data-driven approaches, this study synthesizes the key insights from a wide range of research papers, studies, and applications published up until September 2021[1]. This paper emphasizes the drawbacks linked to traditional epidemiological strategies and stresses the promise offered by ML techniques in overcoming these limitations. Following this, we provide a comprehensive overview of various data sources employed in COVID-19 detection and prevalence estimation such as clinical notes, medical imaging scans, genetic data, social media posts, and mobile phone location logs. We discuss both advantages and disadvantages offered by each source when it comes to integrating them with ML solutions while also providing cases where these integrations have proven fruitful [1,2]. This extensive examination emphasizes significant advances achieved by capitalizing on machine learning methodologies to identify spreading tendencies and gauge pervasiveness indicators for COVID-19. Insights gleaned from this review offer invaluable perspectives regarding prospects held by data-driven strategies aimed at augmenting pandemic monitoring capabilities as well as response capabilities [1]. As COVID-19's evolution journey persists, these findings serve as informative tools to drive ongoing attempts centered around leveraging machine learning for public health and epidemiology purposes which will ultimately contribute towards formulation of more efficacious adaptive strategies suited for future global scale health crises.