Qurat Ul An Sabir

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• Azmat, S., Sabir, Q. u., Tariq, S., Shafqat, A., Rao, G. S., & Aslam, M. (2023).

  Monitoring Air Quality using the Neural Network based Control Chart

  . MAPAN. doi:10.1007/s12647-023-00663-9
• Hushchyna, K., Sabir, Q., Mclellan, K., & Nguyen-Quang, T. (2023). Multicollinearity and Multi-regression Analysis for Main Drivers of Cyanobacterial Harmful Algal Bloom (CHAB) in the Lake Torment, Nova Scotia, Canada. Environmental Modeling & Assessment, 1--12.
• Sabir, Q. u., Shafqat, A., & Aslam, M. (2023).

  Prediction of the COVID-19 transmission: a case study of Pakistan

  . Epidemiology & Infection. doi:10.1017/s0950268823000730
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  Abstract The world has suffered a lot from COVID-19 and is still on the verge of a new outbreak. The infected regions of coronavirus have been classified into four categories: SIRD model, (1) suspected, (2) infected, (3) recovered, and (4) deaths, where the COVID-19 transmission is evaluated using a stochastic model. A study in Pakistan modeled COVID-19 data using stochastic models like PRM and NBR. The findings were evaluated based on these models, as the country faces its third wave of the virus. Our study predicts COVID-19 casualties in Pakistan using a count data model. We’ve used a Poisson process, SIRD-type framework, and a stochastic model to find the solution. We took data from NCOC (National Command and Operation Center) website to choose the best prediction model based on all provinces of Pakistan, On the values of log L and AIC criteria. The best model among PRM and NBR is NBR because when over-dispersion happens; NBR is the best model for modelling the total suspected, infected, and recovered COVID-19 occurrences in Pakistan as it has the maximum log L and smallest AIC of the other count regression model. It was also observed that the active and critical cases positively and significantly affect COVID-19-related deaths in Pakistan using the NBR model.
• Sabir, Q., Kuchumov, A. G., & Nguyen-Quang, T. (2023). USING CORRESPONDENCE ANALYSIS AND LOG-LINEAR MODELS TO INVESTIGATE THE FACTORS AFFECTING CARDIOVASCULAR DISEASE. Russian Journal of Biomechanics, 64--75.
• Sabir, Q., Shafqat, A., & Aslam, M. (2023). Prediction of the COVID-19 transmission: a case study of Pakistan. Epidemiology & Infection, 151, e89.
• Shafqat, A., Sabir, Q. u., Yang, S., Aslam, M., Albassam, M., & Abbas, K. (2023).

  Monitoring and Comparing Air and Green House Gases Emissions of Various Countries

  . Journal of Agricultural, Biological and Environmental Statistics. doi:10.1007/s13253-023-00560-3
• Shafqat, A., Sabir, Q., Yang, S., Aslam, M., Albassam, M., & Abbas, K. (2023). Monitoring and comparing air and green house gases emissions of various countries. Journal of Agricultural, Biological and Environmental Statistics, 1--24.
• Khan, K. I., Sabir, Q. u., Shafqat, A., & Aslam, M. (2022).

  Exploring the psychological and religious perspectives of cancer patients and their future financial planning: a Q-methodological approach

  . BMC Palliative Care, 21, 1-9. doi:10.1186/s12904-022-01079-z
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  Abstract Background Cancer patients are often hesitant to talk about their mental health, religious beliefs regarding the disease, and financial issues that drain them physically and psychologically. But there is a need to break this taboo to understand the perceptions and behaviours of the patients. Previous studies identified many psychological factors that are bothering cancer patients. However, it still requires exploring new elements affecting their mental and physical health and introducing new coping strategies to address patients’ concerns. Methods The current study aims to identify cancer patients’ perceived attitudes towards the severity of illness, understand their fears, tend towards religion to overcome the disease, and future financial planning by using a Q-methodological approach. Data were collected in three steps from January-June 2020, and 51 cancer patients participated in the final stage of Q-sorting. Results The findings of the study are based on the principal component factor analysis that highlighted three essential factors: (1) feelings, (2) religious beliefs about the acceptance of death, and (3) their future personal and financial planning. Further, the analysis shows that the patients differ in their beliefs, causes and support that they received as a coping mechanism. Conclusion This study explains cancer patients’ psychological discomfort and physical pain but cannot relate it to co-morbidities. Q methodology allows the contextualization of their thoughts and future planning in different sets, like acceptance of death, combating religion’s help, and sharing experiences through various platforms. This study will help health professionals derive new coping strategies for treating patients and financial managers to design insurance policies that help them to share their financial burdens.
• Khan, K. I., Qadeer, F., Mata, M. N., Neto, J. C., Sabir, Q. u., Martins, J. N., & Filipe, J. (2021).

  Core Predictors of Debt Specialization: A New Insight to Optimal Capital Structure

  . Mathematics, 9. doi:10.3390/math9090975
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  Debt structure composition is an essential topic of discussion for the management of capital structure decisions. Researchers made extensive efforts to understand the criteria for selecting debts, specifically, to know about the reasons for debt specialization, concealed in identifying its predictors. This question is essential not only for establishing the field of debt structure but also for the financial managers to design corporate financial strategy in a way that leads to attaining an optimal debt structure. Sophisticated financial modeling is applied to identify the core predictors of debt specialization, influencing the strategic choices of optimal debt structure to address this issue. Data were collected from 419 non-financial companies listed at the Karachi Stock Exchange from 2009 to 2015. This study has validated debt specialization by showing that short-term debts maintain their position over the years and remain the most popular type of loan among Pakistani firms. Further, it provides a comprehensive view of the cross-sectional differences among the firms involved in debt specialization by applying a holistic approach. Results show that small, growing, dividend-paying companies, having high expense and risk ratios, followed the debt specialization strategy. This strategy enables firms to reduce their agency conflicts, transaction costs, information asymmetry, risk management and building up their good market reputation. Conclusively, we have identified the gross profit margin, long-term debt to asset ratio, firm size, age, asset tangibility, and long-term industry debt to asset ratio as reliable and core predictors of debt specialization for sustainable business growth.