A. c. r.

The Covid-19 pandemic is the most important health disaster that has surrounded the world for the past eight months. There is no clear date yet on when it will end. As of 18 September 2020, more than 31 million people have been infected worldwide. Predicting the Covid-19 trend has become a challenging issue. In this study, data of COVID-19 between 20/01/2020 and 18/09/2020 for USA, Germany and the global was obtained from World Health Organization. Dataset consist of weekly confirmed cases and weekly cumulative confirmed cases for 35 weeks. Then the distribution of the data was examined using the most up-to-date Covid-19 weekly case data and its parameters were obtained according to the statistical distributions. Furthermore, time series prediction model using machine learning was proposed to obtain the curve of disease and forecast the epidemic tendency. Linear regression, multi-layer perceptron, random forest and support vector machines (SVM) machine learning methods were used. The performances of the methods were compared according to the RMSE, APE, MAPE metrics and it was seen that SVM achieved the best trend. According to estimates, the global pandemic will peak at the end of January 2021 and estimated approximately 80 million people will be cumulatively infected.

Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods.

In this paper, we discuss different predictors of times to failure of units censored in multiple stages in a progressively censored sample from Pareto distribution. The best linear unbiased predictors, maximum likelihood predictors and approximate maximum likelihood predictors are considered. We also present two methods for obtaining prediction intervals for the times to failure of units. A numerical simulation study involving two data sets is presented to illustrate the methods of prediction.

Prediction for Pareto distribution based on progressively type II censored samples

The aim of this study is to analyze the number of deaths due to COVID-19 for Europe and China. For this purpose, we proposed a novel three parametric model named as Exponentiated transformation of Gumbel Type-II (ETGT-II) for modeling the two data sets of death cases due to COVID-19. Specific statistical attributes are derived and analyzed along with moments and associated measures, moments generating functions, uncertainty measures, complete/incomplete moments, survival function, quantile function and hazard function, etc. Additionally, model parameters are estimated by utilizing maximum likelihood method and Bayesian paradigm. To examine efficiency of the ETGT-II model a simulation analysis is performed. Finally, using the data sets of death cases of COVID-19 of Europe and China to show adaptability of suggested model. The results reveal that it may fit better than other well-known models.

Exponentiated Transformation of Gumbel Type-II Distribution for Modeling COVID-19 Data

The purpose of this paper is to identify an effective statistical distribution for examining COVID-19 mortality rates in Canada and Netherlands in order to model the distribution of COVID-19. The modified Kies Frechet (MKIF) model is an advanced three parameter lifetime distribution that was developed by incorporating the Frechet and modified Kies families. In particular with respect to current distributions, the latest one has very versatile probability functions: increasing, decreasing, and inverted U shapes are observed for the hazard rate functions, indicating that the capability of adaptability of the model. A straight forward linear representation of PDF, moment generating functions, Probability weighted moments and hazard rate functions are among the enticing features of this novel distribution. We used three different estimation methodologies to estimate the pertinent parameters of MKIF model like least squares estimators (LSEs), maximum likelihood estimators (MLEs) and weighted least squares estimators (WLSEs). The efficiency of these estimators is assessed using a thorough Monte Carlo simulation analysis. We evaluated the newest model for a variety of data sets to examine how effectively it handled data modeling. The real implementation demonstrates that the proposed model outperforms competing models and can be selected as a superior model for developing a statistical model for COVID-19 data and other similar data sets.

A New Modified Kies Fréchet Distribution: Applications of Mortality Rate of Covid-19.

Background There are numerous studies dealing with analysis for the future patterns of COVID-19 in different countries using conventional time series models. This study aims to provide more flexible analytical framework that decomposes the important components of the time series, incorporates the prior information, and captures the evolving nature of model parameters. Methods We have employed the Bayesian structural time series (BSTS) models to investigate the temporal dynamics of COVID-19 in top five affected countries around the world in the time window March 1, 2020 to June 29, 2020. In addition, we have analyzed the casual impact of lockdown in these countries using intervention analysis under BSTS models. Results We achieved better levels of accuracy as compared to ARIMA models. The forecasts for the next 30 days suggest that India, Brazil, USA, Russia and UK are expected to have 101.42%, 85.85%, 46.73%, 32.50% and 15.17% increase in number of confirmed cases, respectively. On the other hand, there is a chance of 70.32%, 52.54%, 45.65%, 19.29% and 18.23% growth in the death figures for India, Brazil, Russia, USA and UK, respectively. In addition, USA and UK have made quite sagacious choices for lifting/relaxing the lockdowns. However, the pace of outbreak has significantly increased in Brazil, India and Russia after easing the lockdowns. Conclusion On the whole, the Indian and Brazilian healthcare system is likely to be seriously overburdened in the next month. Though USA and Russia have managed to cut down the rates of positive cases, but serious efforts will be required to keep these momentums on. On the other hand, UK has been successful in flattening their outbreak trajectories.

Forecasting the patterns of COVID-19 and causal impacts of lockdown in top five affected countries using Bayesian Structural Time Series Models

The paper is concerned with the preference of prior for the Bayesian analysis of the shape parameter of the mixture of Burr type X distribution using the censored data. We modeled the heterogeneous population using two components mixture of the Burr type X distribution. A comprehensive simulation scheme, through probabilistic mixing, has been followed to highlight the properties and behavior of the estimates in terms of sample size, corresponding risks and the proportion of the component of the mixture. The Bayes estimators of the parameters have been evaluated under the assumption of informative and non-informative priors using symmetric and asymmetric loss functions. The model selection criterion for the preference of the prior has been introduced. The hazard rate function of the mixture distribution has been discussed. The Bayes estimates under exponential prior and precautionary loss function exhibit the minimum posterior risks with some exceptions.

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Preference of prior for bayesian analysis of the mixed burr type x distribution under type i censored samples

In recent years analysis of the mixture models under Bayesian framework has received considerable attention. However, the Bayesian estimation of the mixture models under doubly censored samples has not yet been reported. This paper proposes a Bayesian estimation procedure for analyzing lifetime data under doubly censored sampling when the failure times belong to a two-component mixture of the Weibull model. An extended version of the likelihood function for doubly censored samples for the analysis of a mixture of lifetime models has been introduced. The posterior estimation has been considered under the assumption of gamma prior using a couple of loss functions. The performance of the different estimators has been investigated and compared through the analysis of simulated data. A real-life example has been included to demonstrate the practical applicability of the results. The results indicated the preference of the estimates under squared logarithmic loss function (SLLF) for the estimation of the mixture model. The proposed method can be extended for more than two component mixtures. DOI: http://dx.doi.org/10.4038/jnsfsr.v42i4.7731 J.Natn.Sci.Foundation Sri Lanka 2014 42 (4): 325-334

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Bayesian analysis of doubly censored lifetime data using two component mixture of Weibull distribution

The Bayesian analysis of Burr type X distribution h as been considered in this research. The uniform and Jeffre ys priors have been assumed for posterior analysis. The estimation has been made under complete data, singly type II censored and doubly t ype II censored samples. The Bayes estimators and corresponding ris ks have been obtained under five different loss functions. The s imulation study has been conducted to compare the performance of various est imators.

Bayesian Analysis of Burr Type X Distribution under Complete and Censored Samples

Azam Zaka, Navid Feroze, and Ahmad Saeed Akhter College of Statistical and Actuarial Sciences, University of the Punjab, Lahore, Pakistan Department of Statistics, Government Post Graduate College Muzaffarabad, Azad Kashmir, Pakistan College of Statistical and Actuarial Sciences, University of the Punjab, Lahore, Pakistan logic_azam@hotmail.com, azamzka@gmail.com, navidferoz@hotmail.com, akhtar@stat.pu.edu.pk

http://www.sersc.org/journals/IJAST/vol59/6.pdf

Navid Feroze

Papers

Forecasting the patterns of COVID-19 and causal impacts of lockdown in top five affected countries using Bayesian Structural Time Series Models

Preference of prior for bayesian analysis of the mixed burr type x distribution under type i censored samples

Bayesian analysis of doubly censored lifetime data using two component mixture of Weibull distribution

Bayesian Analysis of Burr Type X Distribution under Complete and Censored Samples

A Note on Modified Estimators for the Parameters of the Power Function Distribution