Integrating Spatial Modeling and Machine Learning for Infectious Disease Surveillance in U.S. Urban and Rural Settings

Background: The use of spatial models together with machine learning is changing how infectious diseases are monitored across the United States, making it easier to detect threats earlier, sort risks more accurately, and improve live updates on outbreak conditions. Conventional monitoring methods are usually slowed by late reports, scattered information, and uneven regional reaches have difficulty reflecting how infections spread differently from place to place or shift over time. Scope: By contrast, combining machine learning with geographic information system (GIS) allows diverse fusion of epidemiology, environmental, behavioral, and movement patterns to be merged into detailed predictions that pinpoint rising danger zones much more reliably. This narrative review synthesizes peer-reviewed literature published from 2020 to present, focusing on recent advancements in spatial-machine learning surveillance, examining methodological evolution, contrasting applications in urban versus rural contexts, and highlighting ethical and operational considerations. Findings: Studies suggest cities gain strong digital networks and abundant information flows that boost accuracy, whereas remote areas deal with scarce records, weak systems, or unequal access, restricting broader use. Issues like varying local patterns, mismatched sources, or unclear results emphasize combining approaches, using consistent analysis steps, along with open oversight. Implications: Future directions emphasize designs that protect user data; machine learning models suited for remote regions alongside broader use of One Health ideas to tackle diseases tied to animals or shifting climates. This review, taken together, shows how combining spatial data with machine learning could reshape disease tracking, offering more accurate forecasts, fairer outcomes, and stronger adaptability for U.S. public health efforts in varied regions.