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Abstract Most of the current public health surveillance methods used in epidemiological studies to identify hotspots of diseases assume that the regional disease case counts are independently distributed and they lack the ability of adjusting for confounding covariates. This article proposes a new approach that uses a simultaneous autoregressive (SAR) model, a popular spatial regression approach, within the classical space‐time cumulative sum (CUSUM) framework for detecting changes in the spatial distribution of count data while accounting for risk factors and spatial correlation. We develop expressions for the likelihood ratio test monitoring statistics based on a SAR model with covariates, leading to the proposed space‐time CUSUM test statistic. The effectiveness of the proposed monitoring approach in detecting and identifying step shifts is studied by simulation of various shift scenarios in regional counts. A case study for monitoring regional COVID‐19 infection counts while adjusting for social vulnerability, often correlated with a community's susceptibility towards disease infection, is presented to illustrate the application of the proposed methodology in public health surveillance.