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A review of some methods for analysis of space-time disease surveillance data is presented. Increasingly, surveillance systems are capturing spatial and temporal data on disease and health outcomes in a variety of public health contexts. A vast and growing suite of methods exists for detection of outbreaks and trends in surveillance data and the selection of appropriate methods in a given surveillance context is not always clear. While most reviews of methods focus on algorithm performance, indoi:10.1016/j.sste.2009.12.001 pmid:22749467 pmcid:PMC7185413 fatcat:wcujb3hxirad7je5trchfapt2a