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A modified CUSUM test to control postoutbreak false alarms.

Lauren M Hall1, Joshua P French1

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Summary
This summary is machine-generated.

This study introduces a modified hypothesis test for cumulative sum (CUSUM) charts to improve disease outbreak detection. The new method maintains CUSUM

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Area of Science:

  • Epidemiology
  • Statistical Process Control
  • Public Health Surveillance

Background:

  • Cumulative sum (CUSUM) charts are used to detect shifts in time series process means, applicable in industrial settings and disease surveillance.
  • Traditional CUSUM charts are reset when a process is out of control; however, resetting is impractical for disease surveillance.
  • Nonrestarting CUSUM charts are used in disease surveillance but exhibit a high false alarm rate after an outbreak ends.

Purpose of the Study:

  • To propose a modified hypothesis test for nonrestarting CUSUM charts in disease surveillance.
  • To enhance the detection of out-of-control processes while controlling the false alarm rate post-outbreak.

Main Methods:

  • Developed a modified hypothesis test for nonrestarting CUSUM charts.
  • Employed simulations of statistics conditional on recent out-of-control process presence.
  • Applied the method to a real-world Salmonella Newport outbreak in Germany, 2011.

Main Results:

  • The modified test retains the CUSUM's power to detect out-of-control processes.
  • The method effectively controls the false alarm rate following an outbreak.
  • In 7 of 8 detected states, the outbreak was identified as quickly as with unmodified CUSUM charts.

Conclusions:

  • The proposed modified hypothesis test offers a valuable improvement for nonrestarting CUSUM charts in disease surveillance.
  • This approach balances timely outbreak detection with a reduced false alarm rate, enhancing public health response.
  • The method demonstrates practical utility and effectiveness in real-world epidemiological data.