scholarly journals Timely detection of Pertussis outbreaks in Iran: The comparison performance of Wavelet-based outbreak detector, Exponential weighted moving average, and Poisson regression-based methods

2020 ◽  
Author(s):  
Yousef Alimohamadi ◽  
Seyed Mohsen Zahraei ◽  
Manoochehr Karami ◽  
Mehdi Yaseri ◽  
Mojtaba Lotfizad ◽  
...  
Keyword(s):  
Poisson Regression ◽  
Moving Average ◽  
False Negative ◽  
False Negative Rate ◽  
Outbreak Detection ◽  
Haar Wavelets ◽  
Surveillance Systems ◽  
Likelihood Ratios ◽  
Exponential Weighted Moving Average ◽  
Weighted Moving Average

Abstract 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.

Evaluation the Performance of Exponentially Weighted Moving Average in the Detection of Cholera Outbreaks: Using the Reported Cholera Outbreaks in Literature

2020 ◽  
Author(s):  
Yousef Alimohamadi ◽  
Firooz Esmaeilzadeh ◽  
Parisa Jalali ◽  
Mohsen Mohammadi ◽  
Mojtaba Sepandi
Keyword(s):  
Infectious Diseases ◽  
Moving Average ◽  
The Other ◽  
Outbreak Detection ◽  
Surveillance Systems ◽  
Exponentially Weighted Moving Average ◽  
Weighted Moving Average ◽  
Exponentially Weighted

Introduction: Timely Detection of outbreaks of infectious diseases can have a very important role in surveillance systems. the presence of appropriate methods can have a very important role for this purpose, the aim of the current study was to Evaluation The Performance of Exponentially Weighted Moving Average in the detection of cholera outbreaks using the reported cholera outbreaks in literature Methods: In the current study the EWMA method was evaluated. To assess the performance of the mentioned methods the six real outbreaks algorithm reported in the literature were used. These reported outbreaks were the daily counts of cholera cases in different countries. After insertion of each outbreak, 7 days inserted as nonoutbreaks days. All analyses performed by MedCalc18.11, Stata version15 and excel 2010. Results: the sensitivity of EWMA was 56.4% (95% CI: 54.3%- 58.5%). The highest sensitivity for outbreak detection was seen in EWMA1 79.18(73.56-84.09) and the lowest was seen in EWMA4 12.2(8.4-17.0). EWMA2 with λ= 0.2 had the best performance with sensitivity 69.8 (63.6-75.5) and specificity 91.4(76.9-98.2) and AUC= 0.80. nd AUC= 0.80. Conclusion: The EWMA method can be very useful in the detection of outbreaks, but the use of this method along the other models may increase the sensitivity of outbreaks detection.  

Improved exponential weighted moving average based measurement noise estimation for strapdown inertial navigation system/doppler velocity log integrated system

2020 ◽  
pp. 1-21
Author(s):  
Lanhua Hou ◽  
Xiaosu Xu ◽  
Yiqing Yao ◽  
Di Wang ◽  
Jinwu Tong
Keyword(s):  
Kalman Filter ◽  
Inertial Navigation System ◽  
Moving Average ◽  
Integrated System ◽  
Measurement Noise ◽  
Noise Estimation ◽  
Doppler Velocity ◽  
Forgetting Factor ◽  
Exponential Weighted Moving Average ◽  
Weighted Moving Average

Abstract The strapdown inertial navigation system (SINS) with integrated Doppler velocity log (DVL) is widely utilised in underwater navigation. In the complex underwater environment, however, the DVL information may be corrupted, and as a result the accuracy of the Kalman filter in the SINS/DVL integrated system degrades. To solve this, an adaptive Kalman filter (AKF) with measurement noise estimator to provide noise statistical characteristics is generally applied. However, existing methods like moving windows (MW) and exponential weighted moving average (EWMA) cannot adapt to a dynamic environment, which results in unsatisfactory noise estimation performance. Moreover, the forgetting factor has to be determined empirically. Therefore, this paper proposes an improved EWMA (IEWMA) method with adaptive forgetting factor for measurement noise estimation. First, the model for a SINS/DVL integrated system is established, then the MW and EWMA based measurement noise estimators are illustrated. Subsequently, the proposed IEWMA method which is adaptive to the various environments without experience is introduced. Finally, simulation and vehicle tests are conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the MW and EWMA methods in terms of measurement noise estimation and navigation accuracy.

scholarly journals Improving EWMA Plans for Detecting Unusual Increases in Poisson Counts

2009 ◽  
Vol 2009 ◽  
pp. 1-16 ◽  
Author(s):  
R. S. Sparks ◽  
T. Keighley ◽  
D. Muscatello
Keyword(s):  
Public Health ◽  
Time Series ◽  
Steady State ◽  
Regression Model ◽  
Moving Average ◽  
Outbreak Detection ◽  
Exponential Weights ◽  
Health Records ◽  
High Incidence ◽  
Weighted Moving Average

Automated public health records provide the necessary data for rapid outbreak detection. An adaptive exponentially weighted moving average (EWMA) plan is developed for signalling unusually high incidence when monitoring a time series of nonhomogeneous daily disease counts. A Poisson transitional regression model is used to fit background/expected trend in counts and provides “one-day-ahead” forecasts of the next day's count. Departures of counts from their forecasts are monitored. The paper outlines an approach for improving early outbreak data signals by dynamically adjusting the exponential weights to be efficient at signalling local persistent high side changes. We emphasise outbreak signals in steady-state situations; that is, changes that occur after the EWMA statistic had run through several in-control counts.

scholarly journals Memory Type Ratio and Product Estimators in Stratified Sampling

2020 ◽  
Author(s):  
Irfan Aslam ◽  
Muhammad Noor-ul-Amin ◽  
Uzma Yasmeen ◽  
Muhammad Hanif
Keyword(s):  
Simulation Study ◽  
Moving Average ◽  
Real Life ◽  
Auxiliary Information ◽  
Stratified Sampling ◽  
Real Life Data ◽  
Memory Type ◽  
Exponential Weighted Moving Average ◽  
Mean Square Errors ◽  
Weighted Moving Average

The exponential weighted moving average (EWMA) statistic is utilized the past information along with the present to enhance the efficiency of the estimators of the population parameters. In this study, the EWMA statistic is used to estimate the population mean with auxiliary information. The memory type ratio and product estimators are proposed under stratified sampling (StS). Mean square errors (MSE) expressions and relative efficiencies of the proposed estimators are derived. An extensive simulation study is conducted to evaluate the performance of the proposed estimators. An empirical study is presented based on real-life data that supports the findings of the simulation study.

scholarly journals Research on Volatility Pattern of BSE BANKEX Index & BSE SENSEX Index using Exponential Weighted moving Average Model

2020 ◽  
Vol 9 (4) ◽  
pp. 3195-3199
Keyword(s):  
Financial Services ◽  
Stock Exchange ◽  
Moving Average ◽  
Equity Market ◽  
Long Duration ◽  
Moving Average Model ◽  
Growth Prospects ◽  
High Level ◽  
Exponential Weighted Moving Average ◽  
Weighted Moving Average

India, a country with impressive growth prospects has stunned many developed nations. As far as performance of equity market concern, last 25 years among more than $1-trillion markets in the world, Indian equity market was best performer outpacing some of bigwigs such as US, Germany and Hong Kong. Last 25 years return in local money of SENSEX was so high in comparisons to others. Banking sectors have specific and an important role in the economic development of a India. With the reconstitution of BSE Sensex in last few years, the weightage of the Banking, Financial Services and Insurance (BFSI) sector. In the BSE 30 will touch its all-time high level to 40.1% which will be more than the combined weights of technology as consumer and auto. The weightage of financials in the Sensex has more than doubled from financial year 2009. In the long duration index weightage affect portfolio in major funds. The main objective of this research paper is to show the volatility patterns of Bombay Stock Exchange SENSEX and BSE BANKEX Index using Exponential weighted moving average (EWMA) model.

RISIKO PASAR: PERBANDINGAN MODEL EWMA DAN GARCH PADA NILAI TUKAR RUPIAH TERHADAP US DOLLAR

2010 ◽  
Vol 5 (2) ◽  
pp. 153
Author(s):  
Ari Christianti
Keyword(s):  
Risk Management ◽  
Financial Risk ◽  
Risk Model ◽  
Moving Average ◽  
Var Model ◽  
Banking Supervision ◽  
Us Dollar ◽  
Exponential Weighted Moving Average ◽  
The Garch Model ◽  
Weighted Moving Average

Financial risk model evaluation or backtesting is a key part of the internal model’s approach to market risk management as laid out by the Basle Committee on Banking Supervision. Using daily exchange rate from January 2006-February 2008, will be compared measuring volatility between EWMA (Exponential Weighted Moving Average) and GARCH (Generalized Autoregressive Conditional Heterocedasticity). The results show that GARCH methods have considerably better power properties in measuring the volatility than the EWMA methods. However, the number of exceptions from the GARCH model, although much less than the EWMA model but the numbers were still above 5% and 1% (confidence level of 95% and 99%). The arguments for explained this finding is a pressure from stakeholders or the existence of an economic events that result in changes in exposure due to the different policies. As a result, the VaR model would be inaccurate to reality.Keywords: volatility, backtesting, EWMA, and GARCH

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