School Models Rare Sets

School Models Rare Sets

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To understand the importance of time and place for outbreak detection, two scenarios were evaluated using the same system that was used in the example described earlier, the inclusion model. An effect of time was assessed by monitoring the system at different times of the day (7:00 a.m., 3:00 p.m., 9:00 p.m.) and at two different times of the week (Monday/Wednesday and Tuesday/Thursday).

To assess surveillance systems for early detection of potential biologic terrorism events, several metrics are used. The sensitivity of a system is the proportion of true positives it detects and is commonly referred to as the fraction of actual outbreaks that are detected. The specificity is the proportion of false positives it produces. The positive predictive value is the proportion of detected events that are indeed positives. The negative predictive value is the proportion of false negatives that are true negatives. A practical limitation of a system that can be detected is that the system has a higher probability of being detected if an outbreak is detected. The combination of specificity and positive predictive value determines the positive predictive value, which is the ability of the system to detect simulated outbreaks that are actually positives. The combination of sensitivity and negative predictive value determines the sensitivity, which is the ability of the system to detect simulated outbreaks that are actually negatives. The denominators of these performance measures are the number of true positives, true negatives, and false positives, and the numerators are the number of simulated outbreaks that were detected or were not detected. To obtain unbiased measures, each of these metrics should be measured for a set of outbreaks, and then an average should be calculated. In the cases that follow, the number of true positives is the number of outbreaks detected. The number of true negatives is the number of simulated outbreaks not detected. The number of false positives is the number of detected events in which there was no outbreak. The number of false negatives is the number of simulated outbreaks not detected. The results of the simulation should be compared to the actual performance of the system as measured in a field test.

[1] R. J. Garey, S. Petridou, M. J. Cochi, N. M. Dickerson, F. Walther, P. M. Smith. The inclusion model: a school-based model for the identification of students with autism spectrum disorders. Child and Adolescent Psychiatry and Mental Health. 2008; 9(6): 405–414. doi:10.1176/appi.cancercare.8.4.405.

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