Browsing by Author "Pathak, Ashok Kumar"

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A Bivariate Teissier Distribution: Properties, Bayes Estimation and Application
(Springer, 2023-08-29T00:00:00) Sharma, Vikas Kumar; Singh, Sudhanshu Vikram; Pathak, Ashok Kumar
This article presents a bivariate extension of the Teissier distribution, whose univariate marginal distributions belong to the exponentiated Teissier family. Analytic expressions for the different statistical quantities such as conditional distribution, joint moments, and quantile function are explicitly derived. For the proposed distribution, the concepts of reliability and dependence measures are also explored in details. Both the maximum likelihood technique and the Bayesian approach are utilised in the process of parameter estimation for the proposed distribution with unknown parameters. Several numerical experiments are reported to study the performance of the classical and Bayes estimators for varying sample size. Finally, a bivariate data is fitted using the proposed distribution to show its applicability over the bivariate exponential, Rayleigh, and linear exponential distributions in real-life situations. � 2023, Indian Statistical Institute.
COVID-19 pandemic: An outlook on its impact on air quality and its association with environmental variables in major cities of Punjab and Chandigarh, India
(Bellwether Publishing, Ltd., 2020-10-31T00:00:00) Sahoo, Prafulla Kumar; Chauhan, Amit Kumar; Mangla, Sherry; Pathak, Ashok Kumar; Garg, V.K.
The present study aims to evaluate the impact of COVID-19 lockdown on air quality and to explore the association of daily COVID-19 confirmed cases with meteorological parameters and criteria pollutants in the major cities of Punjab and Chandigarh, India during the different phase of pre-lockdown (March 1 to March 24), lockdown (1.0, 2.0, 3.0, 4.0; March 25 to May 31), and unlock (1.0, 2.0; > June 1) in 2020. Our results show that the COVID-19 lockdown has drastically improved the quality of air in major cities of Punjab and Chandigarh. Compared to pre-lockdown, maximum reduction of PM2.5 and PM10 levels (up to ?52 and ?53.5%, respectively) was witnessed during lockdown 1.0, but their levels were rising again during the last phase of lockdown and unlock phases. This is due to more relaxation and traffic returned on the road. Among other pollutants, NO2 also reduced during lockdown 1.0, but remained variable between cities and different phases of lockdown and unlock periods. However, surface-level ozone resulted in an overall increase trend during the lockdown and unlock phases. Regarding the relationship between COVID-19 and meteorological parameters, Spearman correlation test shows that ambient temperature is positively correlated with COVID-19 daily confirmed cases (r < 0.77, p < 0.01). This result indicates that the study region�s hot tropical weather is less effective in controlling the spread of COVID-19. Relative humidity and wind speed are also weakly correlated with COVID-19. Furthermore, among criteria pollutants, PM2.5 and PM10 are positively correlated (r < 0.55, p < 0.01) with COVID-19 pandemic, especially in Jalandhar and Ludhiana, suggesting that these pollutants could lead to the spreading of the virus. However, further in-depth studies are required to validate this finding. The results of this study can contribute to the understanding of the role of environmental factors in the transmission of COVID-19 in tropical and sub-tropical countries like India, Brazil, etc. This study also indicates that the temporarylockdown like COVID-19 can be emerged as an effective way to control environmental imbalancein the study area, as well as in other areas. � 2020 Informa UK Limited, trading as Taylor & Francis Group.
Impact of environmental indicators on the COVID-19 pandemic in Delhi, India
(MDPI, 2021-08-09T00:00:00) Mangla, Sherry; Pathak, Ashok Kumar; Arshad, Mohd.; Ghosh, Doyel; Sahoo, Prafulla Kumar; Garg, Vinod Kumar; Haque, Ubydul
Currently, there is a massive debate on whether meteorological and air quality parameters play a crucial role in the transmission of COVID-19 across the globe. With this background, this study aims to evaluate the impact of air pollutants (PM2.5, PM10, CO, NO, NO2, and O3) and meteorological parameters (temperature, humidity, wind speed, and rainfall) on the spread and mortality due to the COVID-19 outbreak in Delhi from 14 Mar 2020 to 3 May 2021. The Spearman�s rank correlation method employed on secondary data shows a significant correlation between the COVID-19 incidences and the PM2.5, PM10, CO, NO, NO2, and O3 concentrations. Amongst the four meteorological parameters, temperature is strongly correlated with COVID-19 infections and deaths during the three phases, i.e., pre-lockdown (14 March 2020 to 24 March 2020) (r = 0.79), lockdown (25 March 2020 to 31 May 2020) (r = 0.87), and unlock (1 June 2020 to 3 May 2021) (r = ?0.75), explaining the variability of about 20�30% in the lockdown period and 18�19% in the unlock period. NO2 explained the maximum variability of 10% and 7% in the total confirmed cases and deaths among the air pollutants, respectively. A generalized linear model could explain 80% and 71% of the variability in confirmed cases and deaths during the lockdown and 82% and 81% variability in the unlock phase, respectively. These findings suggest that these factors may contribute to the transmission of the COVID-19 and its associated deaths. The study results would enhance the ongoing research related to the influence of environmental factors. They would be helpful for policymakers in managing the outbreak of COVID-19 in Delhi, India. � 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Modeling Bivariate Data Using Linear Exponential and Weibull Distributions as Marginals
(De Gruyter Open Ltd, 2023-08-04T00:00:00) Arshad, Mohd; Pathak, Ashok Kumar; Azhad, Qazi J.; Khetan, Mukti
Modeling bivariate data with different marginals is an important problem and have numerous applications in diverse disciplines. This paper introduces a new family of bivariate generalized linear exponential Weibull distribution having generalized linear and exponentiated Weibull distributions as marginals. Some important quantities like conditional distributions, conditional moments, product moments and bivariate quantile functions are derived. Concepts of reliability and measures of dependence are also discussed. The methods of maximum likelihood and Bayesian estimation are considered to estimate model parameters. Monte Carlo simulation experiments are performed to demonstrate the performance of the estimators. Finally, a real data application is also discussed to demonstrate the usefulness of the proposed distribution in real-life situations. � 2023 Mathematical Institute Slovak Academy of Sciences.
Moderation effects of serotype on dengue severity across pregnancy status in Mexico
(BioMed Central Ltd, 2023-03-26T00:00:00) Annan, Esther; Nguyen, Uyen-Sa D. T.; Trevi�o, Jes�s; Wan Yaacob, Wan Fairos; Mangla, Sherry; Pathak, Ashok Kumar; Nandy, Rajesh; Haque, Ubydul
Background: Pregnancy increases a woman�s risk of severe dengue. To the best of our knowledge, the moderation effect of the dengue serotype among pregnant women has not been studied in Mexico. This study explores how pregnancy interacted with the dengue serotype from 2012 to 2020 in Mexico. Method: Information from 2469 notifying health units in Mexican municipalities was used for this cross-sectional analysis. Multiple logistic regression with interaction effects was chosen as the final model and sensitivity analysis was done to assess potential exposure misclassification of pregnancy status. Results: Pregnant women were found to have higher odds of severe dengue [1.50 (95% CI 1.41, 1.59)]. The odds of dengue severity varied for pregnant women with DENV-1 [1.45, (95% CI 1.21, 1.74)], DENV-2 [1.33, (95% CI 1.18, 1.53)] and DENV-4 [3.78, (95% CI 1.14, 12.59)]. While the odds of severe dengue were generally higher for pregnant women compared with non-pregnant women with DENV-1 and DENV-2, the odds of disease severity were much higher for those infected with the DENV-4 serotype. Conclusion: The effect of pregnancy on severe dengue is moderated by the dengue serotype. Future studies on genetic diversification may potentially elucidate this serotype-specific effect among pregnant women in Mexico. � 2023, The Author(s).
A New Alpha Power Transformed Weibull Distribution: Properties and Applications
(CRC Press, 2023-03-16T00:00:00) Pathak, Ashok Kumar; Arshad, Mohd; Bakshi, Sanjeev; Khetan, Mukti; Mangla, Sherry
The Weibull distribution is one of the most widely used distributions in applied sciences and has been extensively utilized in reliability and survival analysis. By introducing additional parameters, several generations of the Weibull distribution have been proposed in the recent past to enhance the flexibility of the model. In this chapter, we introduced a three-parameter new alpha power transformed Weibull distribution. New alpha power transformed exponential, Weibull, Rayleigh, and exponential distributions are important sub-models of the proposed distribution. We study several statistical properties of the proposed distribution. Estimation of parameters using the method of maximum likelihood, weighted least squares, and Anderson Darling are discussed. Finally, some real data sets are also considered to demonstrate the applicability of the proposed distribution. � 2023 Mir Masoom Ali, Irfan Ali, Haitham M. Yousof and Mohamed Ibrahim.
A Novel Bivariate Generalized Weibull Distribution with Properties and Applications
(Taylor and Francis Ltd., 2023-09-09T00:00:00) Pathak, Ashok Kumar; Arshad, Mohd; J. Azhad, Qazi; Khetan, Mukti; Pandey, Arvind
Univariate Weibull distribution is a well known lifetime distribution and has been widely used in reliability and survival analysis. In this paper, we introduce a new family of bivariate generalized Weibull (BGW) distributions, whose univariate marginals are exponentiated Weibull distribution. Different statistical quantiles like marginals, conditional distribution, conditional expectation, product moments, correlation and a measure component reliability are derived. Various measures of dependence and statistical properties along with aging properties are examined. Further, the copula associated with BGW distribution and its various important properties are also considered. The methods of maximum likelihood and Bayesian estimation are employed to estimate unknown parameters of the model. A Monte Carlo simulation and real data study are carried out to demonstrate the performance of the estimators and results have proven the effectiveness of the distribution in real-life situations. � 2023 Taylor & Francis Group, LLC.
ON A BIVARIATE XGAMMA DISTRIBUTION DERIVED FROM COPULA
(University of Bologna, 2022-07-12T00:00:00) Abulebda, Mohammed; Pathak, Ashok Kumar; Pandey, Arvind; Tyagi, Shikhar
In this paper, a new bivariate XGamma (BXG) distribution is presented using Farlie-Gumbel-Morgenstern (FGM) copula. We derive the expressions for conditional distribution, regression function and product moments for the BXG distribution. Concept of reliability and various measures of local dependence are also studied for the proposed model. Furthermore, estimation of the parameters of the BXG distribution is obtained through maximum likelihood estimation and inference function of margin estimation procedures. Finally, an application of the same is also demonstrated to a real data set. � 2022 University of Bologna. All rights reserved.
Pre-to-post lockdown impact on air quality and the role of environmental factors in spreading the COVID-19 cases - a study from a worst-hit state of India
(Springer Science and Business Media Deutschland GmbH, 2020-10-09T00:00:00) Sahoo, Prafulla Kumar; Mangla, Sherry; Pathak, Ashok Kumar; Sal�mao, Gabriel Negreiros; Sarkar, Dibyendu
The present�study aims to�examine the changes in air quality during different phases of the COVID-19 pandemic, including the lockdown (LD1�4) and unlock period (UL1�2) (post-lockdown) as compared to�pre-lockdown (PL1�3) and to�establish the relationships of the environmental and demographic variables with COVID-19 cases in the state of Maharashtra, the worst-hit state in India. Atmospheric pollutants such as PM2.5, PM10, NOx, and CO were substantially reduced during the lockdown and unlock�phases with the greatest reduction in cities having larger traffic volumes. Compared with the immediate pre-lockdown period (PL3), the averaged PM2.5 and PM10 reduced by up to 51% and 47% respectively during the lockdown periods, which resulted in �satisfactory� level of air quality index (AQI) as a result of reduced�vehicular traffic and industrial closing. These parameters continued to reduce as much as 80% during the unlock periods due to the additive impact of weather (rainfall and temperature) combined with the lockdown conditions. Kendall�s correlation matrix showed a significant negative correlation between temperature and air pollutants (r= ? 0.35 to ? 057). Conversely, SO2 and O3 did not improve, and in some cases, they increased during the lockdown and unlocking. COVID-19 spreading incidences were strongly and positively correlated with temperature (r < 0.62) and dew point (r < 0.73). Thus, this indicates that the increase in temperature and dew point cannot weaken the transmission of this virus. The number of COVID-19 cases relative to air pollutants was negatively correlated (r = ? 0.33 to ? 0.74), which may be�a mere coincidence as a result of lockdown. However, based on pre-lockdown�air quality data and demographic factors, it�was found that�particulate matter (PM2.5 and PM10) and population density are closely linked with higher morbidity and mortality although a more in-depth research is required in this direction to validate this finding. The onset of COVID-19 has allowed us to determine that �immediate� changes in air quality within densely populated/industrialized areas can improve livelihood based on pollution mitigation. These findings could be used by policymakers to set new benchmarks for air�pollution that would improve the quality of life for major sectors of the World�s population. COVID-19 has shown us that we can make changes when necessary, and findings may pave the way for future research to inform policy on the tough choices we will have to make between quality of life and survival.�Also, our results will enrich the ongoing discussion on the role of environmental factors on the transmission of COVID-19 and will help to take necessary steps for its control. � 2020, ISB.
Record-based transmuted generalized linear exponential distribution with increasing, decreasing and bathtub shaped failure rates
(Taylor and Francis Ltd., 2022-07-29T00:00:00) Arshad, Mohd; Khetan, Mukti; Kumar, Vijay; Pathak, Ashok Kumar
The linear exponential distribution is a generalization of the exponential and Rayleigh distributions. This distribution is one of the best models to fit data with increasing failure rate (IFR). But it does not provide a reasonable fit for modeling data with decreasing failure rate (DFR) and bathtub shaped failure rate (BTFR). To overcome this drawback, we propose a new record-based transmuted generalized linear exponential (RTGLE) distribution by using the technique of Balakrishnan and He. The family of RTGLE distributions is more flexible to fit the data sets with IFR, DFR, and BTFR, and also generalizes several well-known models as well as some new record-based transmuted models. This paper aims to study the statistical properties of RTGLE distribution, like, the shape of the probability density function and hazard function, quantile function and its applications, moments and its generating function, order and record statistics, R�nyi entropy. The maximum likelihood estimators, least squares and weighted least squares estimators, Anderson-Darling estimators, Cram�r-von Mises estimators of the unknown parameters are constructed and their biases and mean squared errors are reported via Monte Carlo simulation study. Finally, the real data sets illustrate the goodness of fit and applicability of the proposed distribution; hence, suitable recommendations are forwarded. � 2022 Taylor & Francis Group, LLC.
Short-term forecasting of the COVID-19 outbreak in India
(Oxford University Press, 2021-05-28T00:00:00) Mangla, Sherry; Pathak, Ashok Kumar; Arshad, Mohd; Haque, Ubydul
As the outbreak of coronavirus disease 2019 (COVID-19) is rapidly spreading in different parts of India, a reliable forecast for the cumulative confirmed cases and the number of deaths can be helpful for policymakers in making the decisions for utilizing available resources in the country. Recently, various mathematical models have been used to predict the outbreak of COVID-19 worldwide and also in India. In this article we use exponential, logistic, Gompertz growth and autoregressive integrated moving average (ARIMA) models to predict the spread of COVID-19 in India after the announcement of various unlock phases. The mean absolute percentage error and root mean square error comparative measures were used to check the goodness-of-fit of the growth models and Akaike information criterion for ARIMA model selection. Using COVID-19 pandemic data up to 20 December 2020 from India and its five most affected states (Maharashtra, Karnataka, Andhra Pradesh, Tamil Nadu and Kerala), we report 15-days-ahead forecasts for cumulative confirmed cases and the number of deaths. Based on available data, we found that the ARIMA model is the best-fitting model for COVID-19 cases in India and its most affected states. � 2021 The Author(s) 2021.