Background: Better decisions for the control of HIV/AIDS and other infectious diseases
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 disease epidemic. 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 infectious disease 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 infectious disease dynamics. To overcome these issues, we
introduced the statistical tool using statistical process control, and proposed a new method under that framework.
Methods: 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. We proposed a new method using statistical process control. 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.
Results: We test this novel method with the recorded data of the number of AIDS 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 AIDS
epidemic for individual states and for US as a whole. Although AIDS data are used in our demonstration, this method can
be used for monitoring other infectious diseases.