The re-parameterized inverse Gaussian regression to model length of stay of COVID-19 patients in the public health care system of Piracicaba, Brazil.

Among the models applied to analyze survival data, a standout is the inverse Gaussian distribution, which belongs to the class of models to analyze positive asymmetric data. However, the variance of this distribution depends on two parameters, which prevents establishing a functional relation with a linear predictor when the assumption of constant variance does not hold. In this context, the aim of this paper is to re-parameterize the inverse Gaussian distribution to enable establishing an association between a linear predictor and the variance. We propose deviance residuals to verify the model assumptions. Some simulations indicate that the distribution of these residuals approaches the standard normal distribution and the mean squared errors of the estimators are small for large samples. Further, we fit the new model to hospitalization times of COVID-19 patients in Piracicaba (Brazil) which indicates that men spend more time hospitalized than women, and this pattern is more pronounced for individuals older than 60 years. The re-parameterized inverse Gaussian model proved to be a good alternative to analyze censored data with non-constant variance.

Bayesian computational methods for state-space models with application to SIR model

The state-space model is a powerful statistical tool to estimate linear or non-linear discrete-time dynamic systems. This model naturally leads to the estimation problem of the time-varying parameters of the discovery-time demographic version of the susceptible-infected-recovered (SIR) model that we consider. In this paper, we consider computational methods to perform Bayesian inference on state-space models for analysing time-series data. We compare the three popular Bayesian computational methods for state-space models: the adaptive Metropolis-within-Gibbs algorithm, Liu and West's algorithm and variational approximation method based on Gaussian distributions. The performances of the three methods are compared based on synthetic datasets. Furthermore, we analyse the trend of the spread of COVID-19 in South Korea to point out the limitations of existing methods and derive meaningful results. [ FROM AUTHOR] Copyright of Journal of Statistical Computation & Simulation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

COVID-19 Dataset Clustering based on K-Means and EM Algorithms

In this paper, a COVID-19 dataset is analyzed using a combination of K-Means and Expectation-Maximization (EM) algorithms to cluster the data. The purpose of this method is to gain insight into and interpret the various components of the data. The study focuses on tracking the evolution of confirmed, death, and recovered cases from March to October 2020, using a two-dimensional dataset approach. K-Means is used to group the data into three categories: "Confirmed-Recovered”, "Confirmed-Death”, and "Recovered-Death”, and each category is modeled using a bivariate Gaussian density. The optimal value for k, which represents the number of groups, is determined using the Elbow method. The results indicate that the clusters generated by K-Means provide limited information, whereas the EM algorithm reveals the correlation between "Confirmed-Recovered”, "Confirmed-Death”, and "Recovered-Death”. The advantages of using the EM algorithm include stability in computation and improved clustering through the Gaussian Mixture Model (GMM). © 2023,International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Investigating the impact of CO2emissions on the COVID-19 pandemic by generalized linear mixed model approach with inverse Gaussian and gamma distributions

Carbon dioxide (CO2) rate within the atmosphere has been rising for decades due to human activities especially due to usage of fuel types such as coal, cement, flaring, gas, oil, etc. Especially in 2020, COVID-19 pandemic caused major economic, production, and energy crises all around the world. As a result of this situation, there was a sharp decrease in the global CO2 emissions depending on the fuel types used during this pandemic. The aim of this study was to explore the effects of "CO2 emissions due to the fuel types"on "percentage of deaths in total cases"attributed to the COVID-19 pandemic using generalized linear model and generalized linear mixed model (GLMM) approaches with inverse Gaussian and gamma distributions, and also to obtain global statistical inferences about 169 World Health Organization member countries that will disclose the impact of the CO2 emissions due to the fuel types during this pandemic. The response variable is taken as "percentage of deaths in total cases attributed to the COVID-19 pandemic"calculated as "(total deaths/total confirmed cases attributed to the COVID-19 pandemic until December 31, 2020)∗100."The explanatory variables are taken as "production-based emissions of CO2 from different fuel types,"measured in tonnes per person, which are "coal, cement, flaring, gas, and oil."As a result of this study, according to the goodness-of-fit test statistics, "GLMM approach with gamma distribution"called "gamma mixed regression model"is determined as the most appropriate statistical model for investigating the impact of CO2 emissions on the COVID-19 pandemic. As the main findings of this study, 1 t CO2 emissions belonging to the fuel types "cement, coal, flaring, gas, and oil"per person cause increase in deaths in total cases attributed to the COVID-19 pandemic by 2.8919, 2.6151, 2.5116, 2.5774, and 2.5640%, respectively. © 2023 the author(s), published by De Gruyter.

Analysis Pre and Post COVID-19 Pandemic Rorschach Test Data of Using EM Algorithms and GMM Models

The global spread of the COVID-19 virus has become one of the greatest challenges that humanity has faced in recent years. The unprecedented circumstances of forced isolation and uncertainty that it has imposed on us continue to impact our mental well-being, whether or not we have been directly affected by the virus. Over a period of nearly three years (2017-2020), data was collected from multiple administrations of the Rorschach test, one of the most renowned and extensively studied psychological tests. This study involved the clustering of data, collected through the RAP3 software, to analyze the distinctive trends in data recorded before and after the pandemic. This was achieved through the implementation of the well-established machine learning algorithm, Expectation-Maximization. The proposed solution effectively identifies the key variables that significantly influence the subject's score and provides a reliable solution. Additionally, the solution offers an intuitive visualization that can assist psychologists in accurately interpreting shifts in trends and response distributions within a large amount of data in the two periods. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

Learning linear non-Gaussian directed acyclic graph with diverging number of nodes

An acyclic model, often depicted as a directed acyclic graph (DAG), has been widely employed to represent directional causal relations among collected nodes. In this article, we propose an efficient method to learn linear non-Gaussian DAG in high dimensional cases, where the noises can be of any continuous non-Gaussian distribution. The proposed method leverages the concept of topological layer to facilitate the DAG learning, and its theoretical justification in terms of exact DAG recovery is also established under mild conditions. Particularly, we show that the topological layers can be exactly reconstructed in a bottom-up fashion, and the parent-child relations among nodes can also be consistently established. The established asymptotic DAG recovery is in sharp contrast to that of many existing learning methods assuming parental faithfulness or ordered noise variances. The advantage of the proposed method is also supported by the numerical comparison against some popular competitors in various simulated examples as well as a real application on the global spread of COVID-19. ©2022 Ruixuan Zhao, Xin He, and Junhui Wang.

DNN and Model Combined Passive Localization and Social Distancing with Partial Inertial Aiding

At the outbreak of COVID-19, researchers worldwide are seeking approaches to containing this disease. It is necessary to monitor social distance in enclosed public areas, such as subways or shopping malls. Passive localization, such as surveillance cameras, is a natural candidate for this issue, which is meaningful for rapid response to finding the infected suspect. However, the latest surveillance camera system is rotatable, even movable. And it is impossible for professionals to regularly calibrate the extrinsic parameters in a large-scale application, like COVID-19 suspect monitoring. We propose an inertial-aided passive localization method using surveillance camera for social distance measurement without the necessity to obtain extrinsic parameters. Moreover, the hardware modification cost of the off-the-shelf commercial camera is low, which suits the immediate application. The method uses SGBM (Semi-Global Block Matching) for 3D reconstruction and combines YOLOv3 and Gaussian Mixture Model (GMM) clustering algorithm to extract pedestrian point clouds in real time. Combining the 2D DNN-based and model-based methods makes a better balance between the computational load and the detection accuracy than end-to-end 3D DNN-based method. The inertial sensor provides an extra observation for the coordinate transformation from the camera frame into the world ground frame. Results show we can get a decimeter-level social distancing accuracy under noisy background and foreground environments at a low cost, which is promising for urgent COVID-19 public area monitoring. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A novel PRFB decomposition for non-stationary time-series and image analysis

This work presents a novel perfect reconstruction filterbank decomposition (PRFBD) method for nonlinear and non-stationary time-series and image data representation and analysis. The Fourier decomposition method (FDM), an adaptive approach based on Fourier representation (FR), is shown to be a special case of the proposed PRFBD. In addition, adaptive Fourier–Gauss decomposition (FGD) based on FR and Gaussian filters, and adaptive Fourier–Butterworth decomposition (FBD) based on Butterworth filters are developed as the other special cases of the proposed PRFBD method. The proposed theory of PRFBD can decompose any signal (time-series, image, or other data) into a set of desired number of Fourier intrinsic band functions (FIBFs) that follow the amplitude-modulation and frequency-modulation (AM-FM) representations. A generic filterbank representation, where perfect reconstruction can be ensured for any given set of lowpass or highpass filters, is also presented. We performed an extensive analysis on both simulated and real-life data (COVID-19 pandemic, Earthquake and Gravitational waves) to demonstrate the efficacy of the proposed method. The resolution results in the time-frequency representation demonstrate that the proposed method is more promising than the state-of-the-art approaches. © 2023 Elsevier B.V.

MODELING AND CLASSIFICATION OF DEATHS DUE TO COVID-19 BASED ON MACHINE LEARNING TECHNIQUE

Statistical classification is recently considered one of the most important and most common methods in machine learning models and consists of building mod-els that define the target of research interest. There are many classification methods that can be used to predict the value of a response. In this article, we are interested in machine learning applications to classify the new deaths due to Covid-19. Under consideration BIC criterion, the experimental results have shown that the E (Equal variance) with four is the best mixture model. The con-vergence in the algorithm of expectation-maximization is satisfied after 167 itera-tions. The World Health Organization has presented the source of data over the period of March 2, 2020 to August 5, 2020. © 2023 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, Belgrade, Serbia. This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions.

Mathematical Model for Anisotropic diffusion Filter and GLRLM Feature Extraction to Detect Covid-19 from Chest X-Ray Images

In December 2019, the SARS-CoV-2 virus, often referred to as COVID-19, was discovered in Wuhan, China. It is very virulent and has spread very quickly throughout the world. With COVID-19, people have described a wide variety of symptoms, from little discomfort to life-threatening respiratory illness. In this study, chest X-ray scan images are preprocessed using an anisotropic diffusion filter and three classifiers, and the Covid-19 cases are classified from the chest X-ray images using the GLRLM feature extraction approach. Common metrics like sensitivity, selectivity, and accuracy are utilized to compare the performance of the classifiers. When compared to other classifiers in this study, the Gaussian Mixture Model had the best accuracy of 91.07%. © 2022 IEEE.

EPIDEMICS FROM THE EYE OF THE PATHOGEN

While a common trend in disease modeling is to develop models of increasing complexity, it was recently pointed out that outbreaks appear remarkably simple when viewed in the incidence vs. cumulative cases (ICC) plane. This article details the theory behind this phenomenon by analyzing the stochastic Susceptible, Infected, Recovered (SIR) model in the cumulative cases domain. We prove that the Markov chain associated with this model reduces, in the ICC plane, to a pure birth chain for the cumulative number of cases, whose limit leads to an independent increments Gaussian process that fluctuates about a deterministic ICC curve. We calculate the associated variance and quantify the additional variability due to estimating incidence over a finite period of time. We also illustrate the universality brought forth by the ICC concept on real-world data for Influenza A and for the COVID-19 outbreak in Arizona. © 2022 SIAM.Published by SIAM.

COVID-19 Detection from Audio Signals Using LR-MLP-RF-GMM Classifiers

The COVID-19 pandemic bestows global challenges surpassing boundaries of country, religion race, and economy. Testing of COVID-19 patients conditions are remains a challenging task due to the lack of adequate medical supplies, well-trained personnel and conducting reverse transcription polymerase chain reaction (RT-PCR) testing is expensive, long-drown-out process violates social distancing. In this direction, we used microbiologically confirmed COVID-19 dataset based on cough recordings from Coswara dataset. The Coswara dataset is also one of the open challenge dataset for researchers to investigate sound recordings of the Coswara dataset, collected from COVID-19 infected and non-COVID-19 individuals, for classification between Positive and Negative detection. These COVID-19 recordings were collected from multiple countries, through the provided crowd-sourcing website. Here, our work mainly focuses on cough sound based recordings. The dataset is released open access. We developed an acoustic biosignature feature extractors to screen for potential problems from cough recordings, and provide personalized advice to a particular patient's state to monitor his suitable condition in real-time. In our work, cough sound recordings are converted into Mel Frequency Cepstral Coefficients (MFCCs) and passed through a Gaussian Mixture Model (GMM) based pattern recognition, decision making based on a binary pre-screening diagnostic. When validated with infected and non-infected patients, for a two-class classification, using a Coswara dataset. The GMM is applied for developing a model for detection of biomarker based detection and achieves COVID-19 and non-COVID-19 patients accuracy of 73.22% based on the Coswara dataset and also compared with existing classifiers. © 2022 IEEE.

Impact of Upsurge in Covid-19 Cases on Indian Stock Market: A Machine Learning Approach

In the present study, the influence of the surge in pandemic cases and fatalities due to pandemics on the stock performance of NIFTY 50 have been analyzed by employing a regression analysis algorithm using Python Software. The data have been collected for 27 months starting from 1st Jan 2020 to 31st March 2022 and for the application of machine learning tools the data connected to the stock market and COVID 19 have been integrated into the first phase and thereafter preprocessing has been carried out on the data to bring uniformity to data in the second phase. After preprocessing in the third phase data has been evaluated using five leading regression algorithms. The findings of the study reflected that COVID 19 figures and fatalities have severally impacted the stock market returns of the leading index i.e., NIFTY 50. Further, it was gathered from the study that REPTree regression would be a better fit to the model and Gaussian Process would be least fitted to the model as REPTree the lower values of MAE, RMSE, RAE, and R2 error in case of performance evaluation of upsurge in COVID 19 fatalities and surge in stock market returns. © 2022 IEEE.

Dynamic Inferences of Coronavirus Epidemiology Spread in Iraq Region

Covid illness (COVID-19) happened in December 2019 first in Wuhan city of Hubei region of China. World Health Organization (WHO) proclaimed the spread or transmission of this infection as a pandemic. The infection named as severe intense respiratory disorder Covid 2 (SARS-CoV-2) by the International Committee on Taxonomy of Viruses on February 11, 2020. Disease due to this novel-coronavirus is infectious. Therefore, modeling such disease is required to understand methods of transmission, spread, epidemic. Several researchers have found that the transfer of the virus occurs through human contact via their pathogens, such as coughing, sneezing, and breathing. With all sorts of preventive measures (social distancing, wearing mask and lockdown), there is a need to develop a dynamic model of epidemiology for infectious disease. In this article, we have developed a new epidemiological dynamical model-design towards simulating spread and awareness towards this genomic-virus. This model studies the complexity analysis including time series and phase dynamics for the development of the virus in Iraq. Examination helps the comprehension of episode of this infection towards different pieces of the mainland and the world. Accuracy in addition to, validity for the assessment would prove to be superior if models fit less of the data on basis of the features: population-mobility with natural-history, epidemiological-characteristics, & transmission-contrivances for virus-gene. It is concluded that for Iraq the spread is following the log normal behavior with long tail. © 2022 American Institute of Physics Inc.. All rights reserved.

Forecasting and classification of new cases of COVID 19 before vaccination using decision trees and Gaussian mixture model

Regarding the pandemic taking place in the world from the spread of the Coronavirus pandemic and viral mutations, the need has arisen to analyze the epidemic data in terms of numbers of infected and deaths, different geographical regions, and the dynamics of the spread of the virus. In China, the total number of reported infections is 224,659 on June 11, 2022. In this paper, the Gaussian Mixture Model and the decision tree method were used to classify and predict new cases of the virus. Although we focus mainly on the Chinese case, the model is general and adapted to any context without loss of validity of the qualitative results. The Chi-Squared (χ2) Automatic Interaction Detection (CHAID) was applied in creating the decision tree structure, the data has been classified into five classes, according to the BIC criterion. The best mixture model is the E (Equal variance) with five components. The considered data sets of the world health organization (WHO) were used from January 5, 2020, to 12, November 2021. We provide numerical results based on the Chinese case. © 2022 THE AUTHORS

Multi-Output Gaussian Process-Based Data Augmentation for Multi-Building and Multi-Floor Indoor Localization

Location fingerprinting based on Received Signal Strength Indicator (RSSI) has become a mainstream indoor localization technique due to its advantage of not requiring the installation of new infrastructure and the modification of existing devices, especially given the prevalence of Wi-Fi-enabled devices and the ubiquitous Wi-Fi access in modern buildings. The use of Artificial Intelligence (AI)/Machine Learning (ML) technologies like Deep Neural Networks (DNNs) makes location fingerprinting more accurate and reliable, especially for large-scale multi-building and multi-floor indoor localization. The application of DNNs for indoor localization, however, depends on a large amount of preprocessed and deliberately-labeled data for their training. Considering the difficulty of the data collection in an indoor environment, especially under the current epidemic situation of COVID-19, we investigate three different methods of RSSI data augmentation based on Multi-Output Gaussian Process (MOGP), i.e., by a single floor, by neighboring floors, and by a single building;unlike Single-Output Gaussian Process (SOGP), MOGP can take into account the correlation among RSSI observations from multiple Access Points (APs) deployed closely to each other (e.g., APs on the same floor of a building) by collectively handling them. The feasibility of the MOGP-based RSSI data augmentation is demonstrated through experiments using a recently-published work based on Recurrent Neural Network (RNN) indoor localization model and the UJIIndoorLoc, i.e., the most popular publicly-available multi-building and multi-floor indoor localization database;the RNN model trained with the UJIIndoorLoc database, augmented by using the whole RSSI data of a building in fitting an MOGP model (i.e., by a single building), outperforms the other two augmentation methods and reduces the mean three-dimensional positioning error from 8.62 m to 8.42 m in comparison to the RNN model trained with the original UJIIndoorLoc database. © 2022 IEEE.

Masked Face Detection Using Artificial Intelligent Techniques

Nowadays, wearing a face mask is a vital routine in life, but threats are increasing in public due to the advantage of wearing face masks. Existing works do not perfectly detect the human face and also not possible to apply for different faces detection. To overwhelm this issue, in this paper we proposed real-time face mask detection. The proposed work consists of six steps: video acquisition and keyframes selection, data augmentation, facial parts segmentation, pixel-based feature extraction, Bag of Visual Words (BoVW) generation, and face mask detection. In the first step, a set of keyframes are selected using the histogram of gradient (HoG) algorithm. Secondly, data augmentation is involved with three steps as color normalization, illumination correction (parameterized CLAHE), and pose normalization (Angular Affine Transformation). In the third step, facial parts are segmented using the clustering approach i.e., Expectation Maximization with Gaussian Mixture Model (EM-GMM), in which facial regions are segmented into Eyes, Nose, Mouth, Chin, and Forehead. Then, CapsNet based Feature Extraction is performed using CapsNet approach, which performance is higher and lightweight model than the Yolo Tiny V2 and Yolo Tiny V3, and extracted features are constructed into Codebook by Hassanat Similarity with K-Nearest neighbor (H-M with KNN) algorithm. For mask detection, L2 distance function is used. Experiments conducted using Python IDLE 3.8 for the proposed model and also previous works as GMM with Deep learning (GMM + DL), Convolutional Neural Network (CNN) with VGGF, Yolo Tiny V2, and Yolo Tiny V3 in terms of various performance metrics. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Emotion Mining from Speech in the Covid-19 Era: An Exploratory Study

Speech is the most effective form of communication because it is not limited to just the linguistic components but carries the speaker's emotions laced within the components like tone of voice and cues like cries and sighs. This paper aims at studying the research done in the past and applying it to the Covid-19 era.The pandemic is of a great magnitude, affecting every aspect of life including emotions. This time period requires research in determining the most dominant emotions in conversations, to serve as a reference for future research and as a contrast to the research done in the past. Previous papers have identified emotions like happiness, anger, fear and sadness using feature extraction algorithms like MFCC (Mel Frequency Cepstral Coefficients and numerous classification algorithms like GMM (Gaussian Mixture Model), SVM (Support Vector Machine), KNN (K-Nearest-neighbor) and HMM (Hidden Markov Model). Some research has pointed towards ASR (Automatic Speech Recognition), N-Grams and vector space modeling. This paper aims at recognizing the most suitable algorithms for determining the pandemic specific emotions in speech. © 2021 IEEE.

Identifying the Effective Restriction and Vaccination Policies During the COVID-19 Crisis in Sydney: A Machine Learning Approach

This study identified effective COVID-19 restriction policies and the best times to deploy them to minimise locally acquired COVID-19 cases in Sydney. We normalised stringency levels of individual COVID-19 policies, usage levels of urban mobility, and vaccination rates to establish unbiased multivariate time-series features. We introduced the time-lag from 1 day to 15 d before when the governments have officially announced the number of locally acquired COVID-19 cases to the multivariate features. This time-lag dimension allows us to decide critical timings for announcing various COVID-19 related policies and vaccinations to control rapidly increasing infections. We used principal component analysis (PCA) to reduce the dimensions of the multivariate features. A Gaussian process regression (GPR) estimated the daily number of locally acquired COVID-19 cases based on the reduced dimensional features. The model outperformed diverse parametric and non-parametric models in estimating the daily number of infections. We successfully identified effective restriction policies and the best times to implement them to minimise the rate of confirmed COVID-19 cases by analysing PCA coefficients and kernel functions in GPR. © 2022, Springer Nature Switzerland AG.

Analysis of Prediction Data for the Third Wave of COVID-19 in Bogor Regency

The COVID-19 pandemic is far from over. The government has carried out several policies to suppress the development of COVID-19 is no exception in Bogor Regency. However, the public still has to be vigilant especially now we will face a year-end holiday that can certainly be a trigger for the third wave of COVID-19. Therefore, researchers aim to make predictions of the increase in positive cases, especially in the Bogor Regency area to help the government in making policies related to COVID-19. The algorithms used are Gaussian Process, Linear Regression, and Random Forest. Each Algorithm is used to predict the total number of COVID-19 cases for the next 21 days. Researchers approached the Time Series Forecasting model using datasets taken from the COVID-19 Information Center Coordinationn Center website. The results obtained in this study, the method that has the highest probability of accurate and appropriate data contained in the Gaussian Process method. Prediction data on the Linear Regression method has accurate results with actual data that occur with Root Mean Square Error 1202.6262. © 2021 IEEE.