Enhancing outbreak analytics and forecasting with electronic health records
This study aims to create a more effective method of modeling infectious diseases that addresses limitations learned as a result of the COVID-19 pandemic and its public health impact. Its methods adapt and create modeling and data integration methods, software, clinical data, and training to achieve this goal. This includes addressing multiple challenges such as sharing and coordinating data access, increasing access understanding of machine learning (ML), understanding the true patterns of disease transmission, having a sample to represent all infections and timelines, and reducing the gap between transmission and diagnosis. Through multiple strategies, this study will provide a more comprehensive approach to data modeling in an effort to prepare for future outbreaks.