Transformative Approaches in Integrating Data Science for Disease Outbreak Prediction: A Comprehensive Survey in Epidemiology

Main Article Content

Vinuthna Papana
Devireddy Sritha reddy
Kistipati Priyatham reddy

Abstract

In the contemporary realm of public health, the integration of data science into epidemiology has emerged as a transformative approach, particularly in the realm of disease outbreak prediction. This paper provides a comprehensive survey of the role of data science in epidemiology, emphasizing its application in predicting, monitoring, and responding to disease outbreaks. It explores various data sources, including clinical, epidemiological, environmental, and genomic data, and assesses their role in developing robust predictive models. This survey also delves into the challenges associated with data complexity, ethical considerations, and the limitations of current methodologies, while also forecasting future trends and opportunities in the field. Through a blend of theoretical analysis and practical case studies, this paper aims to provide a holistic view of the current state and future prospects of data science in epidemiology.

Article Details

How to Cite
[1]
Vinuthna Papana, Devireddy Sritha reddy, and Kistipati Priyatham reddy, “Transformative Approaches in Integrating Data Science for Disease Outbreak Prediction: A Comprehensive Survey in Epidemiology”, Int. J. Comput. Eng. Res. Trends, vol. 10, no. 11, pp. 55–65, Nov. 2023.
Section
Survey

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