The comparative performance of wavelet‐based outbreak detector, exponential weighted moving average, and Poisson regression‐based methods in detection of pertussis outbreaks in Iranian infants: A simulation‐based study

Abstract

Background Early detection of outbreaks of transmissible diseases is essential for public health. This study aimed to determine the performance of the wavelet‐based outbreak detection method (WOD) in detecting outbreaks and to compare its performance with the Poisson regression‐based model and exponentially weighted moving average (EWMA) using data of simulated pertussis outbreaks in Iran. Method The data on suspected cases of pertussis from 25th February 2012 to 23rd March 2018 in Iran was used. The performance of the WOD (Daubechies 10 [db10] and Haar wavelets), Poisson regression‐based method, and EWMA Compared in terms of timeliness and detection of outbreak days using the simulation of different outbreaks. In the current study, two simulations were used, one based on retrospectively collected data (literature‐based) on pertussis cases and another one on a synthetic dataset created by the researchers. The sensitivity, specificity, false alarm, and false‐negative rate, positive and negative likelihood ratios, under receiver operating characteristics areas, and median timeliness were used to assess the performance of the methods. Results In a literature‐based outbreak simulation, the highest and lowest sensitivity, false negative in the detection of injected outbreaks were seen in db10, with sensitivity 0.59 (0.56‐0.62), and Haar wavelets with 0.57 (0.54‐0.60). In the researcher simulated data, the EWMA (K = 0.5) with sensitivity 0.92 (0.90‐0.94) had the best performance. About timeliness, the WOD methods showed the best performance in the early warning of the outbreak in both simulation approaches. Conclusion Performance of the WOD in the early alarming outbreaks was appropriate. However, this method would be best used along with other methods of public health surveillance.

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“…And finally, the final signal is used by the Shewhart chart to detect the deviation from the normal trend. 19 , 20 …”

“…In this step, the points that are in each of the mentioned levels within the upper and lower limits of the Shewhart diagram were converted to zero and the other points were used to reconstruct the final signal. And finally, the final signal is used by the Shewhart chart to detect the deviation from the normal trend 19,20 …”

Comparative performance of hybrid model based on discrete wavelet transform and ARIMA models in prediction incidence of COVID-19

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