Different diseases may share many of the same symptoms. Because diseases have a broad range of symptoms (many of which overlap), biosurveillance algorithms must be constructed to identify those indicators that can (individually or in some combination) accurately discriminate the presence or absence of the condition of interest, properly monitor those indicators, and provide reliable output on each specific disease’s trends. Matching the process of analyzing the data with the necessary types of data is of utmost importance when trying to obtain an early identification of a health event with minimum false positives.
To prepare for this Discussion,select an infectious disease or condition. Consider the best approach/algorithm to monitor the disease or condition you selected. Determine the number and type of covariates the algorithm should have.
Post a description of the algorithm that you think would best monitor the disease/condition you selected. Explain which covariates you would include in the algorithm. Finally, explain the limitations of the algorithm and the implications for public health.
Support your suggestions with additional scholarly resources
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