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Objectives The purpose of the current study was to determine the upper threshold number of cases for which pertussis infection would reach an outbreak level nationally in Iran. Methods Data on suspected cases of pertussis from the 25th February 2012 to the 23rd March 2018 from the Center for Disease Control and Prevention in Iran was used. The national upper threshold level was estimated using the exponentially weighted moving average (EWMA) method and the Poisson regression method. Results In total, 2,577 (33.6%) and 1,714 (22.3%) cases were reported in the Spring and Summer respectively. There were 1,417 (18.5%) and 1,971 (25.6%) cases reported in the Autumn and Winter, respectively. The overall upper threshold using the EWMA and the Poisson regression methods, was estimated as a daily occurrence of 8 (7.55) and 7.50 (4.48-11.06) suspected cases per 10,000,000 people, respectively. The daily seasonal thresholds estimated by the EWMA and the Poisson regression methods were 10, 7, 6, 8 cases and 10, 7, 7, 9 cases for the Spring, Summer, Autumn, and Winter, respectively. Conclusion The overall and seasonal estimated thresholds by the 2 methods were similar. Therefore, the estimated thresholds of 6-10 cases in a day, per 10,000,000 people could be used to detect pertussis outbreaks and epidemics by health policymakers.

Objective: To define the level of alarm threshold for pertussis aberrations and to detect the aberrations of the reported suspected cases of pertussis from the Mazandaran province in the north of Iran. Methods: The included cases were composed of the suspected pertussis patients who came from Mazandaran province and registered in the Center for Disease Control and Prevention from 20 March 2012 to 20 March 2018. A discrete wavelet transform- based method was used to detect the aberrations. All analyses were performed using MATLAB Software version 2018a and Excel 2010. Results: A total of 1 162 cases were recruited in the study, including 545 (46.90%) males and 617 (53.10%) females, with median age of 1.47 (0.22-9.56) years. The median age of males was 1.18 (0.21-8.24) years, while that of females was 1.82 (0.21-10.75) years. Concerning the level of the alarm threshold, it was 1.28 case/d when k=2, while it was 1.34 case/d when k=3. The total detected aberration days were 123 d and 57 d by considering k=2 and 3, respectively. The most defined alarm threshold was related to spring (>2 cases/d) and summer (>1 case/d), respectively. Conclusions: The sensitivity of the surveillance system is subjected to a different time. Thus, determining the level of alarm threshold periodically using different methods is recommended.

Background: In spite of existing vaccination programs in many countries, outbreaks of pertussis are still reported. In Iran, the suspected and confirmed cases of pertussis are reported annually. Due to the lack of similar studies, the purpose of the current study was to determine the Spatio-temporal distribution of Pertussis using Geographic Information System (GIS) to identify high-risk areas in Iran during 2012-2018. Methods: In the current cross-sectional study, registered data in the department of vaccine-preventable diseases in the Iranian ministry of health were used. To assess the temporal trend, the Cochran–Armitage test was used. To show the spatial distribution and to identify hotspot areas, Choropleth map and Getis-Ord Gi statistics were used. All analyses performed by Arc.map10.5, Stata 15 and Excel 2010. Results: The incidence of suspect pertussis cases had an increasing trend but did not have a linear trend (p=0.06). Most of the cases happened in under 1 year infants (62.66%). The incidence of reported cases in northern areas was higher than in the Southern areas. The Zanjan had the most reported cases during the understudied period with a median of 7.63 reported cases per 100,000. The clustering of infection and hotspots were identified in northern areas of Iran including Qazvin, Qom, Markazi, and Hamadan. Conclusion: Our results showed that the cumulative incidence of reported cases is increasing. The northern provinces had the highest incidence of Pertussis. Therefore, the causes of this spatio-temporal pattern of pertussis should be determined. Also, supervision on vaccination programs in high-risk areas is recommended.

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 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 data set created by the researchers. The sensitivity, specificity, false alarm, and false-negative rate, positive and negative likelihood ratios, under ROC 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 Daubechies 10 (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. This article is protected by copyright. All rights reserved.

Background Early detection of outbreaks is very important for surveillance systems. Due to the importance of the subject and lack of similar studies in Iran, the aim of this study was to determine the performance of the Wavelet-Based Outbreak detection method)WOD(in detecting outbreaks and to compare its performance with Poisson regression-based model and Exponential weighted moving average (EWMA) using data of simulated pertussis outbreaks in Iran. Methods 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 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 (literature-based and researcher-made outbreaks). The sensitivity, specificity, false alarm and false negative rate, positive and negative likelihood ratios, under ROC 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 Daubechies 10 (db10), with sensitivity 0.59 (0.56-0.62), and Haar wavelets with 0.57 (0.54-0.60). In the researcher-made outbreaks, 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. Conclusions Performance of the WOD in the early alarming outbreaks was appropriate. However, it's better as the method was used along with other methods in public health surveillance systems.

This paper proposes a novel architecture for the digital error correction logic block that is used in pipeline analog-to-digital converters. The new architecture is implemented with HA and OR_HA blocks instead of HA and FA in the conventional architecture. This architecture for digital error correction logic is simulated in 0.18µm CMOS process by using Cadence. The simulation results show that the proposed architecture improves speed and power consumption. Also this architecture occupies less area than the conventional digital error correction block.

this paper proposes a novel dual-stage resistor string digital to analog converter (DAC) architecture with a 12-bit resolution and less than 10ns settling time. The proposed architecture is constructed from the combination of two 6-bit RDACs in the first- and second stage with a reduced number of taps. In the proposed RDAC, the unity-gain buffer used for the isolation between the two stages is removed. The 12-bit resistor-string RDAC is designed using the 0.35-μm CMOS technology. The Mont Carlo simulations show a worst-case DNL = 0.2 LSB, INL = 0.3 LSB, and SNDR = 68.2 dB at 50MHz sampling frequency.

This paper introduces a novel structure for the comparator of analog – to -digital convertor with built- in threshold. Thus the resistor string can be eliminated. The proposed comparator is simulated using HSpice in TSMC 0.18μm CMOS process. The proposed structure reduces the power consumption and occupies less area compared to conventional comparators due to the elimination of the resistor string in analog-to-digital converters.

In this paper we introduce an Modified Clipped LMS (MCLMS) algorithm with a variable step size. In the MCLMS algorithm two parameters, the step size and the threshold control the convergence rate of the adaptive filter coefficients and also determine the final mean-square error. The computational complexity decreased dramatically by a large threshold. However, this selection results in a low convergence rate. Since the convergence time is inversely proportional to the step size, a large step size is often selected for fast convergence. But a large step size results in an increased final mean square error. Therefore in this paper we choose a large threshold and propose a variable step size for the MCLMS algorithm. The advantages of this proposed variable step size and a large threshold selection are that the computation complexity is low, final mean square error is low and that the convergence is fast.