The Role of Statistical Modeling in Predicting Disease Outbreaks: Applications and Challenges

This paper reviews the essential role of statistical modeling in predicting infectious disease outbreaks, highlighting the spectrum of methodologies to advanced machine learning techniques. It navigates how integrating multiple data sources improves outbreak forecasting accuracy and timeliness. The paramount applications in major epidemics such as COVID-19, Ebola, Influenza, and malaria illustrate model strengths and practical challenges. The paper also discusses crucial processes like model calibration and validation to improve reliability, as well as addressing challenges such as data quality, model interpretability, ethical concerns, and equity in access. This comprehensive review reinforces the importance of interdisciplinary collaboration, continuous technological innovation, and ethical frameworks in advancing data-driven public health strategies to better anticipate and mitigate future outbreaks.