UNASSIGNED: Better decisions for the control of @entity645 / @entity791 and other @entity166 require better information. The large amount of available public health data makes it possible to extract such information to monitor and predict significant disease events in @entity66 . The detection of unusual events often involves a combination of a forecasting and a decision mechanism assessing the extent to which an observed event differs significantly from a forecast event. A number of methods and models have been proposed to monitor the trend of @entity166 and to detect unusual events. Although these existing methods and models are useful, many new issues remain to be addressed, including the complicated data structure and the @entity166 dynamics. In this paper, we first reviewed the most commonly used methods and models, including the historical limit method, the time series analysis, the hidden Markov models, and the process control charts. Then, we further discussed issues with the current available methods. To overcome these issues, we introduced the statistical tool using statistical process control, and proposed a new method under that framework. A major feature of the new method is that it prospectively monitors the disease incidence using sequentially collected data over time. It also takes into account a wide variety of longitudinal patterns and possible autocorrelation in the data. We further test this novel method with the recorded data of the number of @entity791 cases in different states of US from 1985 to 2011. The results show that our new method is effective in detecting and predicting the time trends of @entity791 epidemic for individual states and for US as a whole. Although @entity791 data are used in our demonstration, this method can be used for monitoring other @entity166 .

Statistical monitoring-based alarming systems in modeling the XXXX epidemic in the United States, 1985-2011.

@entity166 :: ('MESH:D003141', 'Disease') :: ['infectious diseases', 'infectious disease'] @entity791 :: ('MESH:D000163', 'Disease') :: ['AIDS'] @entity645 :: ('57667', 'Species') :: ['HIV'] @entity66 :: ('MESH:D004194', 'Disease') :: ['disease epidemic']
@entity791 :: (MESH:D000163,Disease) :: ['AIDS']
