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Biosurveillance AlgorithmsDifferent 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, review this week’s Learning Resources. Choose a disease or condition different from the one you selected for your Scholar-Practitioner Project. If your last name begins with the letters A through L, select a chronic disease that is of interest to you. If your last name begins with the letters M through Z, 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.
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