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


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
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.


Hamada, T., Keum, N., Nishihara, R., & Ogino, S. (2017). Molecular pathological epidemiology: new devel-oping frontiers of big data science to study etiologies and pathogenesis. Journal of gastroenterology, 52, 265-275.

Subhani, M. M., Anjum, A., Koop, A., & Antonopoulos, N. (2016, December). Clinical and genomics data inte-gration using meta-dimensional approach. In Proceedings of the 9th International Conference on Utility and Cloud Computing (pp. 416-421).

Kostkova, P., Saigí-Rubió, F., Eguia, H., Borbolla, D., Verschuuren, M., Hamilton, C., ... & Novillo-Ortiz, D. (2021). Data and digital solutions to support surveillance strategies in the context of the COVID-19 pandem-ic. Frontiers in Digital Health, 3, 707902.

Esposito, D., Dipierro, G., Sonnessa, A., Santoro, S., Pascazio, S., & Pluchinotta, I. (2021). Data-driven epidem-ic intelligence strategies based on digital proximity tracing technologies in the fight against COVID-19 in cit-ies. Sustainability, 13(2), 644.

Carone, M., Dominici, F., & Sheppard, L. (2020). In pursuit of evidence in air pollution epidemiology: the role of causally driven data science. Epidemiology (Cambridge, Mass.), 31(1), 1.

Rodríguez-Almonacid, D. V., Ramírez-Gil, J. G., Hi-guera, O. L., Hernández, F., & Díaz-Almanza, E. (2023). A Comprehensive Step-by-Step Guide to Using Data Science Tools in the Gestion of Epidemiological and Climatological Data in Rice Production Systems. Agronomy, 13(11), 2844.

Tremblay, M. (2019). Systematic Pattern Recognition and Modeling with Imperfect Data: An integration of data science, data mining, machine learning, and epidemiolo-gy (Doctoral dissertation, Utrecht University).

Li Vigni, F. (2022). Data and Model Operations in Computational Sciences: The Examples of Computa-tional Embryology and Epidemiology. Perspectives on Science, 30(4), 696-731.

Gómez-Losada, Á., Santos, F. M., Gibert, K., & Pires, J. C. (2019). A data science approach for spatiotemporal modelling of low and resident air pollution in Madrid (Spain): Implications for epidemiological stud-ies. Computers, Environment and Urban Systems, 75, 1-11.

Polonsky, J. A., Baidjoe, A., Kamvar, Z. N., Cori, A., Durski, K., Edmunds, W. J., ... & Jombart, T. (2019). Out-break analytics: a developing data science for informing the response to emerging pathogens. Philosophical Transactions of the Royal Society B, 374(1776), 20180276.

Prosperi, M., Min, J. S., Bian, J., & Modave, F. (2018). Big data hurdles in precision medicine and precision public health. BMC medical informatics and decision making, 18, 1-15.

Ramaswami, R., Bayer, R., & Galea, S. (2018). Preci-sion medicine from a public health perspective. Annual Review of Public Health, 39, 153-168.

Matějíček, L., Engst, P., & Jaňour, Z. (2006). A GIS-based approach to spatio-temporal analysis of environmental pollution in urban areas: A case study of Prague's environment extended by LIDAR data. Ecological Modelling, 199(3), 261-277.

Rimando, M., Brace, A. M., Namageyo-Funa, A., Parr, T. L., Sealy, D. A., Davis, T. L., ... & Christiana, R. W. (2015). Data collection challenges and recommendations for early career researchers. The Qualitative Report, 20(12), 2025-2036.

Salerno, J., Knoppers, B. M., Lee, L. M., Hlaing, W. M., & Goodman, K. W. (2017). Ethics, big data and com-puting in epidemiology and public health. Annals of Epi-demiology, 27(5), 297-301.

Klein, B. D., & Rossin, D. F. (1999). Data quality in neural network models: effect of error rate and magnitude of error on predictive accuracy. Omega, 27(5), 569-582.

Danese, M., Masini, N., Biscione, M., & Lasaponara, R. (2014). Predictive modeling for preventive Archaeology: overview and case study. Open Geo-sciences, 6(1), 42-55.

Becker, D., van Breda, W., Funk, B., Hoogendoorn, M., Ruwaard, J., & Riper, H. (2018). Predictive modeling in e-mental health: a common language framework. Internet interventions, 12, 57-67.

Baig, M. M., Afifi, S., GholamHosseini, H., & Mirza, F. (2019). A systematic review of wearable sensors and IoT-based monitoring applications for older adults–a focus on ageing population and independent living. Journal of medical systems, 43, 1-11.

Kim, J., & Ahn, I. (2021). Infectious disease outbreak prediction using media articles with machine learning mod-els. Scientific reports, 11(1), 4413.

Jonkmans, N., D’Acremont, V., & Flahault, A. (2021). Scoping future outbreaks: a scoping review on the outbreak prediction of the WHO Blueprint list of priority diseases. BMJ global health, 6(9), e006623.

Rothman, D. (2020). Artificial Intelligence By Exam-ple: Acquire advanced AI, machine learning, and deep learning design skills. Packt Publishing Ltd.

Helm, J. M., Swiergosz, A. M., Haeberle, H. S., Kar-nuta, J. M., Schaffer, J. L., Krebs, V. E., ... & Ramkumar, P. N. (2020). Machine learning and artificial intelligence: definitions, applications, and future directions. Current reviews in musculoskeletal medicine, 13, 69-76.

Galbusera, F., Casaroli, G., & Bassani, T. (2019). Arti-ficial intelligence and machine learning in spine re-search. JOR spine, 2(1), e1044.

Campbell, C. E., & Nehm, R. H. (2013). A critical analysis of assessment quality in genomics and bioinfor-matics education research. CBE—Life Sciences Educa-tion, 12(3), 530-541.