DefinePK

Abstract

This review explores the dynamic field of predictive modelling for disease outbreaks, focusing on the data sources, modelling techniques, accuracy, challenges, and future directions integral to its advancement. It underscores the significance of diverse data sources, including epidemiological, environmental, social media, and mobility data. It also discusses various modelling approaches, from statistical models to advanced machine learning algorithms and network analysis. The review highlights the critical role of accuracy and validation in predictive models, alongside the challenges posed by data quality, model complexity, and the ethical use of personal data. It outlines promising research avenues, such as improving data collection methods, integrating novel data sources like genomic data, and leveraging emerging technologies such as AI and IoT to enhance predictive capabilities. This comprehensive overview emphasizes the importance of predictive modelling in informing public health decisions and the continuous need for innovation and collaboration to address the complex dynamics of disease outbreaks.
Keywords:  Predictive Modeling, Disease Outbreaks, Data Sources, Machine Learning, Public Health, Emerging Technologies.