An Overview of Healthcare Data Analytics With Applications to the COVID-19 Pandemic

TL;DR

This paper reviews how advanced data analytics, machine learning, and digital tools are transforming healthcare, especially in managing COVID-19, by improving diagnosis, treatment, and epidemiological understanding through big data solutions.

Contribution

It provides a comprehensive overview of innovative analytical methods and digital technologies applied to healthcare challenges during the COVID-19 pandemic, emphasizing multidisciplinary approaches.

Findings

Application of machine learning to COVID-19 diagnosis and treatment

Use of data integration systems for epidemiological analysis

Development of novel tools for infection source tracking

Abstract

In the era of big data, standard analysis tools may be inadequate for making inference and there is a growing need for more efficient and innovative ways to collect, process, analyze and interpret the massive and complex data. We provide an overview of challenges in big data problems and describe how innovative analytical methods, machine learning tools and metaheuristics can tackle general healthcare problems with a focus on the current pandemic. In particular, we give applications of modern digital technology, statistical methods, data platforms and data integration systems to improve diagnosis and treatment of diseases in clinical research and novel epidemiologic tools to tackle infection source problems, such as finding Patient Zero in the spread of epidemics. We make the case that analyzing and interpreting big data is a very challenging task that requires a multi-disciplinary effort to continuously create more effective methodologies and powerful tools to transfer data information into knowledge that enables informed decision making.