Integrated Epidemiologic Modeling for Next-Generation U.S. Infectious Disease Surveillance Systems: A Narrative Review

Background: The surveillance system for infectious diseases in the United States relies on a complex system that utilizes case reporting systems that are limited by reporting delay, data infrastructure, and population coverage. The recent outbreak of the COVID-19 pandemic has underlined the importance of timeliness, sensitivity, and interoperability in the existing architectures for surveillance. Objective: This narrative review examines how next-generation infectious disease surveillance systems in the United States can be improved by incorporating heterogeneous data sources with advanced epidemiologic models. Approach: We discuss the integration of traditional case surveillance, syndromic surveillance, wastewater epidemiology, genomic epidemiology, and digital data sources into mechanistic, machine learning, and hybrid models, focusing on real-time assimilation, nowcasting in the presence of reporting delay, interoperability, governance, and privacy-preserving analytics. Our aim is to provide an updated literature synthesis from 2020 to present. Findings: Integrated modeling frameworks that infer epidemic dynamics from multiple data streams always improve timeliness and forecasting compared to single data streams. Despite this, there remain several challenges in data standardization, capacity, and governance that have limited operational use of such frameworks. Conclusion: To improve infectious disease surveillance in the United States, there is a need for coordinated investments in interoperable infrastructure, integrated epidemiologic models, and adaptive governance frameworks to facilitate effective public health decision-making.