Biostatistics & Epidemiology

Trends in Big Data for Public Health Surveillance and Disease Outbreak Prediction

A wide range of models spanning statistical, computational learning, and epidemiological techniques are used to predict disease outbreaks. The intricate process of predicting and comprehending the dynamics of infectious diseases is aided by the distinct strengths and capacities of each type of model. When it comes to tracking and predicting the evolution of infectious diseases over time, time-series modeling is still a mainstay in the field of disease outbreak prediction. By using previous data to find temporal patterns, this method makes it possible to model disease trends and forecast outbreaks in the future by using historical data. In order to comprehend the connections between different contributing elements and the occurrence of disease outbreaks, regression models are essential. The basic goals of the infectious illnesses field are to manage existing outbreaks, lower the potential of new ones, and give afflicted people the best care possible in the most effective manner. The less evident goal is to increase readiness for potential breakouts in the future.

There is an urgent need for including unstructured sources of data like social media and carrying out opinion analysis while publishing national health and epidemic outbreak advisories in the near future. Advancements in data analytics and the proliferation of the Internet of Things have opened new frontiers in disease surveillance and early outbreak detection. Digital epidemiology is the process of investigating the dynamics of disease-related patterns, both social and clinical, as well as the causes of these trends in epidemiology. Digital epidemiology, utilizing big data from a variety of digital sources, has emerged as a viable method for early detection and monitoring of viral outbreaks. Therefore, early recognition and evaluation in the field of public health might result in the containment of outbreaks or epidemics. For long-term prediction, traditional models have demonstrated limited accuracy due to a significant degree of uncertainty and a lack of vital data regarding outbreaks. The key generality and robustness of the skills of the current models need to be enhanced, even if the literature evaluation includes various attempts at tackling this issue.

For this special issue, we are seeking papers on:

• Utilizing big data and deep learning to forecast infectious diseases
• A large data analytics-based paradigm for pandemic predicted outcomes
• Using Big Data to Forecast Disease Outbreaks and Surveillance for Public Health
• A methods and models for analyzing data to forecast disease outbreaks
• Machine learning using large data from healthcare groups to forecast diseases
• Utilizing big data in predictive medicine to monitor public health
• An algorithm for predicting the dynamics of disease using mobile big data
• Utilizing machine learning and sentiment analysis methods to forecast illness outbreaks
• Ethical implications of infectious diseases, big data statistics, and associated issues
• Seeking insights in publicly available data to forecast epidemic diseases
• Enhancing models of disease outbreak forecasting to effectively allocate public health resources

Manuscript submission deadline:

• Submission Deadline: 15.11.2025
• Notification to Author: 20.02.2026
• Revised Version Submission: 15.04.2026
• Final Acceptance: 20.06.2026