On the early detecting of the COVID-19 outbreak

Introduction: This paper aims to measure the performance of early detection methods, which are usually used for infectious diseases. Methodology: By using real data of confirmed Coronavirus cases from the Kingdom of Saudi Arabia and Italy, the moving epidemic method (MEM) and the moving average cumulative sums (Mov. Avg Cusum) methods are used in our simulation study. Results: Our results suggested that the CUSUM method outperforms the MEM in detecting the start of the Coronavirus outbreak.

Statistical control charts to assess the incidence of presumably infectious diarrhea reported between 2009 and 2019 in children under 4 years of age in the macro regions of Araçatuba, Marília and Presidente Prudente, São Paulo, Brazil.

Objective: To evaluate the monthly rates of hospitalizations for childhood diarrhea in macro-regions of Araçatuba, Marília and Presidente Prudente, SP, between 2019 -June Between June 2009. Methods: The average rates and their standard deviations for admission of diarrhea in the target population were obtained from DATASUS and standardized for cases x 100,000 inhabitants. Confidence limits were established, occurrences above confidence limits were considered epidemic events. The normality of the data and serial autocorrelation were tested using the Shapiro-Wilk and Durbin-Watson method. Results: All methods detected epidemic occurrences in the three regions. Araçatuba and Marília, the peaks were concentrated in the first half of the decade and Presidente Prudente, close to the middle. The CUSUM method was more sensitive to detect epidemic periods, however the normality data and assumptions have been violated by serial autocorrelation in a few months. The EWMA method was considered the most appropriate. Conclusions: Statistical process control charts can be used to monitor and compare disease incidence between different regions.

Detecting Change between Urban Road Environments along a Route Based on Static Road Object Occurrences

For over a decade, urban road environment detection has been a target of intensive research. The topic is relevant for the design and implementation of advanced driver assistance systems. Typically, embedded systems are deployed in these for the operation. The environments can be categorized into road environment-types. Abrupt transitions between these pose a traffic safety risk. Road environment-type transitions along a route manifest themselves also in changes in the distribution of traffic signs and other road objects. Can the placement and the detection of traffic signs be modelled jointly with an easy-to-handle stochastic point process, e.g., an inhomogeneous marked Poisson process? Does this model lend itself for real-time application, e.g., via analysis of a log generated by a traffic sign detection and recognition system? How can the chosen change detector help in mitigating the traffic safety risk? A change detection method frequently used for Poisson processes is the cumulative sum (CUSUM) method. Herein, this method is tailored to the specific stochastic model and tested on realistic logs. The use of several change detectors is also considered. Results indicate that a traffic sign-based road environment-type change detection is feasible, though it is not suitable for an immediate intervention.

Application-Specific Learning Curve With a Modern Computer-Assisted Orthopedic Surgery System for Joint Arthroplasty

A commonly acknowledged barrier for the adoption of new computer-assisted orthopedic surgery (CAOS) technologies relates to a perceived long and steep learning curve. However, this perception has not been objectively tested with the consideration of surgeon-specific learning approaches. This study employed the cumulative sum control chart (CUSUM) to investigate individual surgeon's learning of CAOS technology by monitoring the stability of the surgical process regarding surgical time. Two applications for total knee arthroplasty (TKA) and two applications for total shoulder arthroplasty (TSA) provided by a modern CAOS system were assessed with a total of 21 surgeons with different levels of previous CAOS experience. The surgeon-specific learning durations identified by CUSUM method revealed that CAOS applications with “full guidance” (i.e., those that offer comprehensive guidance, full customization, and utilize CAOS-specific instrumentation) required on average less than ten cases to learn, while the streamlined application designed as a CAOS augmentation of existing mechanical instrumentation demonstrated a minimal learning curve (less than three cases). During the learning phase, the increase in surgical time was found to be moderate (approximately 15 min or less) for the “full guidance” applications, while the streamlined CAOS application only saw a clinically negligible time increase (under 5 min). The CUSUM method provided an objective and consistent measurement on learning, and demonstrated, contrary to common perception, a minimal to modest learning curve required by the modern CAOS system studied.

Change-point detection: application of Cusum method to real life data

Several procedures have been developed for the detection of abrupt changes in time series. Among these procedures, it can be mentioned the Cumulative Sum (Cusum) type method. It is in such a perspective that Katchekpele et al. (2017) proposed a method using a Cusum type test to detect a change-point in the unconditional variance of the generalised autoregressive conditional heteroskedasticity(GARCH) models. The aim of this paper is to present an application of their technique. After briefly recalling how the test statistic was constructed, the change-point detection algorithm is given and it is shown how it is applied to some real life data.

Monitoring Volatility Change for Time Series Based on Support Vector Regression

This paper considers monitoring an anomaly from sequentially observed time series with heteroscedastic conditional volatilities based on the cumulative sum (CUSUM) method combined with support vector regression (SVR). The proposed online monitoring process is designed to detect a significant change in volatility of financial time series. The tuning parameters are optimally chosen using particle swarm optimization (PSO). We conduct Monte Carlo simulation experiments to illustrate the validity of the proposed method. A real data analysis with the S&P 500 index, Korea Composite Stock Price Index (KOSPI), and the stock price of Microsoft Corporation is presented to demonstrate the versatility of our model.

Fault Detection and Isolation for a Cooling System of Fuel Cell via Model-based Analysis

The development of fuel cell electric vehicles in recent years has led to increased interest in the use of fuel cells as sources of renewable energy. To achieve successful commercialization of fuel cell vehicles, it will be necessary to guarantee the safety, reliability, and lifetime of fuel cell systems by predictive fault detection and isolation (FDI). In this study, the parity equation, an observer, and a Kalman filter are employed together to compare the characteristics of FDI, focusing on the sensors of the thermal management system. Residuals corresponding to the difference between temperature outputs of linear models under driving cycles and nonlinear temperature outputs are used to isolate faults. Then, assessment of three model-based sensor FDI schemes is used to isolate sensor faults using the Cumulative Sum Control Chart (CUSUM) method. Generated residuals are evaluated by CUSUM to detect the presence of a sensor fault. As a result, isolated sensor faults are assessed.

Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm

The presented method reconstructs a network (a graph) from AIS data, which reflects vessel traffic and can be used for route planning. The approach consists of three main steps: maneuvering points detection, waypoints discovery, and edge construction. The maneuvering points detection uses the CUSUM method and reduces the amount of data for further processing. The genetic algorithm with spatial partitioning is used for waypoints discovery. Finally, edges connecting these waypoints form the final maritime traffic network. The approach aims at advancing the practice of maritime voyage planning, which is typically done manually by a ship’s navigation officer. The authors demonstrate the results of the implementation using Apache Spark, a popular distributed and parallel computing framework. The method is evaluated by comparing the results with an on-line voyage planning application. The evaluation shows that the approach has the capacity to generate a graph which resembles the real-world maritime traffic network.