This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.
Dr. Kazi Kutubuddin,
- Professor, BMIT, Solapur, Maharashtra, India
Abstract
Infectious diseases remain a formidable global health challenge, characterized by rapid evolution and complex transmission dynamics that often outpace traditional epidemiological surveillance and response mechanisms. This study investigates the transformative potential of machine learning (ML) methodologies to enhance our understanding and prediction of infectious disease epidemiology and transmission. Leveraging diverse datasets—including clinical records, genomic sequences, environmental factors, social mobility data, and real-time digital footprints—we studies and presented various ML models (e.g., supervised classification, deep learning, and time-series forecasting) tailored for critical epidemiological tasks. Our findings demonstrate significant advancements in several key areas: early outbreak detection through anomaly identification, precise identification of high-risk populations and geographical hotspots, accurate forecasting of disease incidence and transmission trajectories, and optimization of intervention strategies (e.g., vaccination campaigns, resource allocation). The superior predictive accuracy, scalability, and real-time analytical capabilities of these ML models underscore their critical role in building more resilient public health surveillance systems and facilitating a more rapid, precise, and proactive response to emerging pathogenic threats in an increasingly interconnected world.
Keywords: Machine Learning, Epidemic, Epidemiology, Infection Diseases, supervised classification
Dr. Kazi Kutubuddin. Epidemiology and transmission of infectious diseases study using Machine learning. International Journal of Pathogens. 2025; 02(02):-.
Dr. Kazi Kutubuddin. Epidemiology and transmission of infectious diseases study using Machine learning. International Journal of Pathogens. 2025; 02(02):-. Available from: https://journals.stmjournals.com/ijpg/article=2025/view=234948
References
- Al Meslamani AZ, Sobrino I, de la Fuente J. Machine learning in infectious diseases: potential applications and limitations. Annals of Medicine. 2024 Dec 31;56(1):2362869.
- Ryu S, Chun JY, Lee S, Yoo D, Kim Y, Ali ST, Chun BC. Epidemiology and transmission dynamics of infectious diseases and control measures. Viruses. 2022 Nov 12;14(11):2510.
- Santangelo OE, Gentile V, Pizzo S, Giordano D, Cedrone F. Machine learning and prediction of infectious diseases: a systematic review. Machine Learning and Knowledge Extraction. 2023 Feb 1;5(1):175-98.
- Provenzano S, Santangelo OE, Giordano D, Alagna E, Piazza D, Genovese D, Calamusa G, Firenze A. Predicting disease outbreaks: evaluating measles infection with Wikipedia Trends. Recenti progressi in medicina. 2019 Jun 1;110(6):292.
- Gianfredi V, Santangelo OE, Provenzano S. Correlation between flu and Wikipedia’s pages visualization. Acta Bio Medica: Atenei Parmensis. 2021 Feb 8;92(1):e2021056.
- Santangelo OE, Provenzano S, Piazza D, Giordano D, Calamusa G, Firenze A. Digital epidemiology: assessment of measles infection through Google Trends mechanism in Italy. Annali di Igiene, Medicina Preventiva e di Comunita. 2019 May 1;31(4).
- Guidance WH. Ethics and governance of artificial intelligence for health. World Health Organization. 2021 Jun 28.
- Palaniappan S, V R, David B, S PN. Prediction of epidemic disease dynamics on the infection risk using machine learning algorithms. SN computer science. 2022 Jan;3(1):47.
- Palaniappan S, V R, David B, S PN. Prediction of epidemic disease dynamics on the infection risk using machine learning algorithms. SN computer science. 2022 Jan;3(1):47.
- Ghannam RB, Techtmann SM. Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring. Computational and Structural Biotechnology Journal. 2021 Jan 1;19:1092-107.
- Atkinson A, Ellenberger B, Piezzi V, Kaspar T, Salazar-Vizcaya L, Endrich O, Leichtle AB, Marschall J. Extending outbreak investigation with machine learning and graph theory: Benefits of new tools with application to a nosocomial outbreak of a multidrug-resistant organism. Infection Control & Hospital Epidemiology. 2023 Feb;44(2):246-52.
- Mulla NR. Nano-Materials in Vaccine Formation and Chemical Formulae’s for Vaccination. Journal of Nanoscience, NanoEngineering & Applications. 2025;15(03).
International Journal of Pathogens
| Volume | 02 |
| 02 | |
| Received | 01/11/2025 |
| Accepted | 03/11/2025 |
| Published | 27/12/2025 |
| Publication Time | 56 Days |
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