Compounded Bell-G class of statistical models with applications to COVID-19 and actuarial data

The compounded Bell generalized class of distributions is proposed in this article as an alternative to the compounded Poisson generalized family of distributions. Some properties and actuarial measures are presented. The properties of a special model named Bell Weibull (BellW) are obtained such as the linear representation of density, rth moment, incomplete moment, moment generating function using Wright generalized hypergeometric function and Meijer's G function, the pth moment of order statistics, reliability, stochastic ordering, and residual and reversed residual life. Moreover, some commonly used entropy measures, namely, Rényi, Havrda and Charvat, and Arimoto and Tsallis entropy are obtained for the special model. From the inferential side, parameters are estimated using maximum likelihood estimation. The simulation study is performed to highlight the behavior of estimates. Some actuarial measures including expected shortfall, value at risk, tail value at risk, tail variance, and tail variance premium for the BellW model are presented with the numerical illustration. The usefulness of the proposed family is evaluated using insurance claims and COVID-19 datasets. Convincing results are obtained. © 2023 the author(s), published by De Gruyter.

Do Social Media and Messaging Apps Influence Loneliness? The Case of Young Malaysian Higher Education Students during the COVID-19 Pandemic

Research into the impact of social media on psychological well-being has produced conflicting findings, with a corresponding lack of certainty about the direction of the effect. Since early 2020, the COVID-19 pandemic has necessitated a re-assessment of the available evidence, also in Malaysia. The present study focuses on feelings of loneliness among unmarried young Malaysian adults, the so-called Generation Z, a demographic most likely to suffer mental health problems due to social confinement. An exploratory online survey (n = 103) was conducted to find out how lonely Malaysian youngsters are, focusing on those in higher education. To measure this, use was made of the UCLA Loneliness Scale. The main research question is whether loneliness relates to participants' usage of social networking sites (e.g. Instagram), messaging apps (e.g. WhatsApp) or their reasons for doing so (e.g., meeting diversion or affective needs). Results show that in Malaysia, young adults' feelings of loneliness under COVID restrictions vary independently of their social media and messaging app usage and the needs these digital technologies fulfil. Even the weak or moderate associations that were identified are likely due to chance factors. Our study supports, therefore, scholarship that questions the direct effect of social media platforms on loneliness regardless of the role they play in promoting meaningful in-person interactions. Possible explanations and implications of these findings will be discussed. One conjecture is that Malaysia's shared cultural values might form a buffer against adverse well-being effects of both the pandemic and SMMA usage. © 2023, Penerbit Universiti Kebangsaan Malaysia. All rights reserved.

Best selected forecasting models for COVID-19 pandemic

This study sought to identify the most accurate forecasting models for COVID-19-confirmed cases, deaths, and recovered patients in Pakistan. For COVID-19, time series data are available from 16 April to 15 August 2021 from the Ministry of National Health Services Regulation and Coordination's health advice portal. Descriptive as well as time series models, autoregressive integrated moving average, exponential smoothing models (Brown, Holt, and Winters), neural networks, and Error, Trend, Seasonal (ETS) models were applied. The analysis was carried out using the R coding language. The descriptive analysis shows that the average number of confirmed cases, COVID-19-related deaths, and recovered patients reported each day were 2,916, 69.43, and 2,772, respectively. The highest number of COVID-19 confirmed cases and fatalities per day, however, were recorded on April 17, 2021 and April 27, 2021, respectively. ETS (M, N, M), neural network, nonlinear autoregressive (NNAR) (3, 1, 2), and NNAR (8, 1, 4) forecasting models were found to be the best among all other competing models for the reported confirmed cases, deaths, and recovered patients, respectively. COVID-19-confirmed outbreaks, deaths, and recovered patients were predicted to rise on average by around 0.75, 5.08, and 19.11% daily. These statistical results will serve as a guide for disease management and control.

Neutralizing IgG antibody seroprevalence, efficacy and safety post covishield vaccination: A follow up study

COVID-19, a pandemic caused by severe acute respiratory syndrome (SARS) corona virus-2 has been a worldwide challenge for the entire mankind. However, a safe and efficacious vaccine would be clinically valuable to reduce the risks of COVID-19. In this study, we aimed to assess the seroprevalence of the Neutralizing IgG Antibodies six months after completion of both the doses of Covishield Vaccination (ChAdox1 nCoV-19) and compare it with the IgG titres one month after the first dose and one month after the second dose of the vaccine respectively. The efficacy, safety and comorbidities related to the vaccine were also assessed at different time intervals. The levels of IgG antibodies were estimated in 72 subjects from the Teerthanker Mahaveer Medical College & Research Centre (who turned up after six months of the complete regimen of Covishield Vaccination) using the Enzyme-Linked Immunosorbent Assay (ELISA) Technique. A highly significant increase (p= 0.00) in the neutralizing Antibodies titre was seen in fully vaccinated individuals post six months when compared to the titres of post one month of 1st & 2nddoses. It can therefore be concluded that (ChAdox1 nCoV-19) Covishield Vaccination if administered in full regimen has both acceptable efficacies as well as safety profile. Hence, a complete vaccine regimen may prove as an effective strategy against COVID-19 and consequent minimization of long-term morbid effects. © 2022 International Journal of Health Sciences.

Indices and Parameters in the Diagnosis and The Management of COVID-19: A Comprehensive Review

Coronavirus outbreak has challenged the medical, public health infrastructure and economic status of nearly all countries across the World. The main issue of concern with SARS-CoV-2 has been the high infectivity rate and the high mortality rate especially, observed during the wave 2 of COVID-19. The symptoms associated with SARS-CoV-2 are often misleading with influenza and another respiratory tract seasonal viral infection as, majority of the patients report common constitutional symptoms. Hence, correct diagnosis for COVID-19 may play a key role in early detection, management and curtailment in the transmission rate.Therefore, apart from limiting this outbreak, efforts need to be made to plan comprehensive and stringent measures to develop diagnostic tools to prevent future outbreaks of this zoonotic disease. The present manuscript provides a detailed description of various indices and parameters which are currently being used for easier, rapid, accurate diagnosis, medical managementand prognosis assessment of COVID-19 patients in various stages of the disease.

Theory and application of the power ailamujia distribution

Statistical modeling is constantly in demand for simple and flexible probability distributions. We are helping to meet this demand by proposing a new candidate extending the standard Ailamujia distribution, called the power Ailamujia distribution. The idea is to extend the adaptability of the Ailamujia distribution through the use of the power transform, introducing a new shape parameter in its definition. In particular, the new parameter is able to produce original non-monotonic shapes for the main functions that are desirable for data fitting purposes. Its interest is also shown through results about stochastic orders, quantile function, moments (raw, incomplete and probability weighted), stress-strength parameter and Tsallis entropy. New classes of distributions based on the power Ailamujia distribution are also presented. Then, we investigate the corresponding statistical model to analyze two kinds of data: complete data and data in presence of censorship. In particular, a goodness-of-fit statistical test allowing the processing of right-censored data is developed. The potential of the new model is demonstrated by its application to four data sets, two being related to the Covid-19 pandemic. © 2021 University of Guilan.