Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace

Area of Science:

• * Public Health
• * Data Science
• * Epidemiology

Background:

• * Growing discussion on leveraging big data for pandemic prevention and treatment.
• * Need for a framework to guide enterprises and organizations in big data-based epidemic control.
• * Importance of understanding research trends in big data for pandemic management.

Purpose of the Study:

• * To visually analyze research and development trends in big data for pandemic control using CiteSpace.
• * To assist academics in identifying future research directions.
• * To establish a framework for organizational planning in big data-driven epidemic control.

Main Methods:

• * Scientometric analysis of 202 original papers from Web of Science (2011-2022) using CiteSpace software.
• * Visualization techniques including co-authorship, co-occurrence, and network analysis.
• * Parameter settings: date range, top 20% selection criteria, specific node forms, and pruning methods.

Main Results:

• * "COVID-19 infection" identified as the most prominent research cluster in 2020.
• * "Internet of things (IoT) platform and unified health algorithm" emerged as a key research topic.
• * Emerging keywords (2021-2022) include "Influenza, internet, China, human mobility, and province."
• * Top institution: Chinese Academy of Sciences; top authors: Qadri and Wilson.
• * Leading journal: The Lancet; dominant regions: United States, China, and Europe.

Conclusions:

• * Big data analytics offer valuable insights for understanding and controlling pandemics.
• * The study highlights key research areas and emerging trends in the field.
• * Findings can inform future research and strategic planning for epidemic control.