ABSTRACT
In Chapter 28, on patient monitoring, many core signal processing and interpretation concepts relevant to population surveillance were introduced. The approaches taken to capture and interpret signals at the population level are exactly the same as those for an individual patient. Signals must be acquired that are as free of noise as possible and at a sample rate that is sufficient to detect the process of interest. Statistical analyses can be used to detect important population changes, through the creation of process control charts or computational models such as neural networks and Markov models. These models need to be evaluated to ensure that they are sensitive and specific, to avoid false alarms or missed events – and the waste of public health resources associated with late detection or unnecessary investigation.
