Epidemiology and Oral Health: Harnessing Big Data for Monitoring and Predicting Epidemic Outbreaks

Authors

  • Varshitha Reddy Davarapalli Master Student, Health Informatics, Michigan Technological University, Michigan, USA

Keywords:

Epidemiology, Oral Health, Big Data, Epidemic Outbreaks, Proactive Interventions, Public Health, Disease Prevention

Abstract

This paper delves into the transformative potential of big data within epidemiology, particularly in the context of oral health-related epidemic outbreaks. With the advent of digital technologies and the proliferation of interconnected systems, vast amounts of data are being generated and can be harnessed to monitor and predict disease trends. Leveraging big data analytics enables proactive interventions and the implementation of effective public health measures for disease prevention. Through a comprehensive review of existing literature and case studies, this paper elucidates the role of big data in revolutionizing epidemiological research, particularly in the realm of oral health. By analyzing diverse datasets ranging from social media posts to electronic health records, researchers can gain valuable insights into the dynamics of oral health epidemics, identify at-risk populations, and anticipate future outbreaks. Furthermore, the integration of advanced computational techniques, such as machine learning algorithms, empowers epidemiologists to forecast disease trajectories with unprecedented accuracy. This paper underscores the importance of interdisciplinary collaboration between public health experts, data scientists, and policymakers in harnessing the full potential of big data for epidemic monitoring and prediction. Ultimately, leveraging big data analytics in epidemiology offers promising avenues for mitigating the burden of oral health-related epidemics and improving overall population health.

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Published

15-04-2024

How to Cite

[1]
V. R. Davarapalli, “Epidemiology and Oral Health: Harnessing Big Data for Monitoring and Predicting Epidemic Outbreaks”, J. Computational Intel. & Robotics, vol. 4, no. 1, pp. 86–102, Apr. 2024.

Issue

Vol. 4 No. 1 (2024): Journal of Computational Intelligence and Robotics

Section

Articles

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