Abstract
Several methods have been proposed in open literatures for detecting changes in disease outbreak or incidence. Most of these methods are likelihood-based as well as the direct application of Shewhart, CUSUM and EWMA schemes. We use CUSUM, EWMA and EWMA-CUSUM multi-chart schemes to detect changes in disease incidence. Multi-chart is a combination of several single charts that detects changes in a process and have been shown to have elegant properties in the sense that they are fast in detecting changes in a process as well as being computationally less expensive. Simulation results show that the multi-CUSUM chart is faster than EWMA and EWMA-CUSUM multi-charts in detecting shifts in the rate parameter. A real illustration with health data is used to demonstrate the efficiency of the schemes.
Highlights
In this era of bioterrorism, outbreak of diseases and surge in disease incidence; statisticians, epidemiologists, informaticians and surveillance scientists are designing algorithms to detect changes in disease occurrence or outbreak in order to avert any possible public health pandemonium
The simulation results for the out-control average run length ðARLλÞ of the Poisson CUmulative SUM (CUSUM) charts with parameter μ1, μ2, μ3, average CUSUM chart and multi-chart ðTCMÞ were listed in column two, column three, column four, column five, and column six, respectively
We chose values of the smoothing parameter wi to be w1 = 0:1, w2 = 0:5, and w3 = 0:9: The simulation results for the out-control average run length ðARLλÞ of the Exponentially Weighted Moving Average (EWMA) and EWMA multi-charts were listed on Tables 3 and 4, respectively
Summary
In this era of bioterrorism, outbreak of diseases and surge in disease incidence; statisticians, epidemiologists, informaticians and surveillance scientists are designing algorithms to detect changes in disease occurrence or outbreak in order to avert any possible public health pandemonium. Most of these models or algorithms are modifications of the statistical process control (SPC) schemes, namely Shewhart, CUmulative SUM (CUSUM) and Exponentially Weighted Moving Average (EWMA) statistics. Bravata et al [2] in their review identified 115 health surveillance systems and 9 syndromic surveillance systems Most of these surveillance systems have been developed and are in use in countries like US, UK, China and Japan among others
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