A statistical analysis of COVID-19 using Gaussian and probabilistic model

Nayak, Soumya Ranjan; Arora, Vaibhav; Sinha, Utkarsh; Poonia, Ramesh Chandra

doi:10.1080/09720502.2020.1833442

Cited by 20 publications

(9 citation statements)

References 10 publications

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“…Awareness of Health and promotion of healthy habits. Digital transformation for enhanced connectivity and ease of operation of execution of plans [33][34]. Even after a century exact origin of Spanish flu is not found.…”

Section: Discussionmentioning

confidence: 99%

Pros and Cons of Infectious Outbreak and Pandemic Driven Ruralisation

JC¹,

GN²

2022

AustinJPublicHealthEpidemiol

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Infectious diseases played a major role and have influenced modern society, culture, and farming practices. The threat posed by emerging zoo noses to the world’s economy, health and safety is tremendous and the way Covid-19 pandemic have disrupted the civilisation is a complete failure and exposes the vulnerabilities of twenty first century. The frequency of Emerging Infectious Diseases (EIDs) and there is increased burden of economic loss. The emergence of these diseases is frequently caused by interactions between human, wild animals, and domestic animals as well as by rapidly changing environmental conditions. As pandemic redefines the living style there is different patterns of adaptation of the human beings. This article examines the driving forces responsible for emerging infectious pandemic and its impact on lives and livelihood of people. The preventive measures to tackle with EIDs are explained and how new norms are to be adopted for better co habiting with other species.

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Section: Discussionmentioning

confidence: 99%

Pros and Cons of Infectious Outbreak and Pandemic Driven Ruralisation

JC¹,

GN²

2022

AustinJPublicHealthEpidemiol

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“…Fong et al [ 6 ] considered small data for early forecasting, while Petropoulos and Makridakis [ 7 ] also applied the forecasting model. Chen et al [ 8 ] designed an algorithm for predicting COVID-19 data, while Nayak et al [ 9 ] and Wolkewitz et al [ 10 ] applied a probabilistic model to analyze COVID-19 data. The size of the COVID-19 epidemic has been worked out by Yue et al [ 11 ] with the help of surveillance systems, and a similar study to estimate the final size of the COVID-19 epidemic has also been discussed by Syed and Sibgatullah [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of the COVID-19 Mortality Rates with Probability Distributions: The Case of Pakistan and Afghanistan

Dar¹,

Ijaz

Almanjahie

et al. 2022

Computational and Mathematical Methods in Medicine

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The COVID-19 pandemic has shocked nations due to its exponential death rates in various countries. According to the United Nations (UN), in Russia, there were 895, in Mexico 303, in Indonesia 77, in Ukraine 317, and in Romania 252, and in Pakistan, 54 new deaths were recorded on the 5th of October 2021 in the period of months. Hence, it is essential to study the future waves of this virus so that some preventive measures can be adopted. In statistics, under uncertainty, there is a possibility to use probability models that leads to defining future pattern of deaths caused by COVID-19. Based on probability models, many research studies have been conducted to model the future trend of a particular disease and explore the effect of possible treatments (as in the case of coronavirus, the effect of Pfizer, Sinopharm, CanSino, Sinovac, and Sputnik) towards a specific disease. In this paper, varieties of probability models have been applied to model the COVID-19 death rate more effectively than the other models. Among others, exponentiated flexible exponential Weibull (EFEW) distribution is pointed out as the best fitted model. Various statistical properties have been presented in addition to real-life applications by using the total deaths of the COVID-19 outbreak (in millions) in Pakistan and Afghanistan. It has been verified that EFEW leads to a better decision rather than other existing lifetime models, including FEW, W, EW, E, AIFW, and GAPW distributions.

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“…Many research studies have been conducting on Covid-19, for example, Yousaf et al [1] studies the forecasting of the infections, deaths, and recoveries using Auto-Regressive Integrated Moving Average Model (ARIMA), Fong et al [2] discussed the accurate early forecasting model using small data, Petropoulos and Makridakis [3] also discussed the forecasting model, Chen et al [4] worked on the reconstruction and prediction algorithm for Covid-19, Nayak et al [5] presented the statistical analysis of Covid-19 using the probabilistic model, Wolkewitz [6] discussed the statistical analysis of the clinical Covid-19 data, Yue et al [7] worked out the Size of a COVID-19 Epidemic from Surveillance Systems, Syed and Sibgatullah [8] discussed the estimation of the final size of the COVID-19 epidemic in Pakistan using the SIR model, Mizumot et al [9] estimates the asymptomatic proportion of COVID-19. Many other research studies have been conducted where the investigator applied various statistical tools for data analysis, For example, zeri et al [10] discussed the comparison of the climate indices using GEE model, Mahmoudi et al [11] worked on the estimation of the simple harmonizable processes, Maleki and Mahmoudi [12] studied the Two-piece locationscale distribution, Mahmoudi et al [13] explored the large sample inference in two independent populations, and Pan et al [14] worked on classifying and comparing several linear and non-linear regression models with a symmetric error.…”

Section: Introductionmentioning

confidence: 99%

A new approach for modeling COVID-19 death data

Farooq

2022

IFS

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The Covid-19 infections outbreak is increasing day by day and the mortality rate is increasing exponentially both in underdeveloped and developed countries. It becomes inevitable for mathematicians to develop some models that could define the rate of infections and deaths in a population. Although there exist a lot of probability models but they fail to model different structures (non-monotonic) of the hazard rate functions and also do not provide an adequate fit to lifetime data. In this paper, a new probability model (FEW) is suggested which is designed to evaluate the death rates in a Population. Various statistical properties of FEW have been screened out in addition to the parameter estimation by using the maximum likelihood method (MLE). Furthermore, to delineate the significance of the parameters, a simulation study is conducted. Using death data from Pakistan due to Covid-19 outbreak, the proposed model applications is studied and compared to that of other existing probability models such as Ex-W, W, Ex, AIFW, and GAPW. The results show that the proposed model FEW provides a much better fit while modeling these data sets rather than Ex-W, W, Ex, AIFW, and GAPW.

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A statistical analysis of COVID-19 using Gaussian and probabilistic model

Cited by 20 publications

References 10 publications

Pros and Cons of Infectious Outbreak and Pandemic Driven Ruralisation

Pros and Cons of Infectious Outbreak and Pandemic Driven Ruralisation

Statistical Analysis of the COVID-19 Mortality Rates with Probability Distributions: The Case of Pakistan and Afghanistan

A new approach for modeling COVID-19 death data

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