Financial performance of China's listed firms in presence of coronavirus: Evidence from corporate culture and corporate social responsibility.

Motivated from the shortage of the existing research studies on impacts of dangerously contagious diseases on firms' financial performance, this study sheds light on the impacts of Coronavirus (Covid-19) outbreak on financial performance upon on the quarterly data of 126 Chinese listed firms across 16 industries. Overall, the Covid-19 outbreak reduced Chinese listed firms' financial performance proxied by the revenue growth rate, ROA, ROE, and asset turnover. This outbreak's negative effects on Chinese firms' profitability were much smaller than that on their revenue growth rates. While this outbreak's negative effects on financial performance of Chinese listed firms were bigger for those that were seriously affected by this pandemic like airlines, travel, and entertainment (ATE), this pandemic's effects were positive for the medicine industry. In the meanwhile, Chinese listed firms that located in high-risk regions suffered a bigger financial loss during the outbreak, and especially there was a strong Hubei effect. The corporate culture and CSR moderated the inverse relationship between this outbreak and Chinese firms' financial performance. Findings of this study contribute to enrich the existing literature on impacts of the Covid-19 outbreak on firms' financial performance worldwide and suggest helpful practical and theoretical implications.

Enhancement of resilience and quality of cold supply chain under the disruptions caused by COVID-19: A case of a developing country

Cold supply chain (CSC) comprises temperature-sensitive processes, starting from the supply of raw materials, manufacturing, and finally the delivery of finished goods to the end consumers via transport services. Pandemics such as COVID-19 pose threats to its overall functioning and to cater to this issue, the study will ensure the sustainable functioning of CSC by recommending resilience strategies. To do so, the COVID-19 disruptions in the CSC and the resilient sustainability strategies were collected via a vigorous literature review and were analyzed via a Fuzzy QFD technique. The results concluded "crisis simulation,” "identification and securing of logistics,” and "digitalization of cold supply chain” as the top three strategies to ensure the resilience of CSC under disruptions caused by COVID-19. The study recommends necessary steps to the policymakers to ensure a resilient and quality effective CSC. The application of the study proves to be the first of its kind in a developing country such as Pakistan.JEL Classification: C54, D81, H12

Role of Methylprednisolone for COVID-19 Patients Admitted to a Tertiary Care Hospital

Objective: To determine the role of Methylprednisolone in managing COVID-19 patients. Study Design: Cross-sectional study. Place and Duration of Study: Pakistan Emirates Military Hospital (PEMH), Rawalpindi Pakistan, from Jan to Feb 2021. Methodology: This study was carried out at the Department of Medicine. Medical records of all moderate, severe and critical COVID-19 patients admitted and receiving Methylprednisolone were reviewed. Methylprednisolone was used in all patients at doses 0.-2 mg per kg. Results: A total of 200 cases were included. The most common presenting symptoms were cough (77.5%), fever (67.5%) and shortness of breath (63.5%). Most patients (85%) presented within the first week of their illness. One or more comorbidities were present in 75% of patients. Most common being hypertension in 70(35%) and diabetes mellitus in 63(31.5%). Complications seen in the study were Cytokine release storm 92(46%) and acute respiratory distress syndrome 44(22%). The median time for initiation of corticosteroid therapy was 4 hours (range 1-96 hours). Overall survival (OS) in the study was 83.5%. OS for patients with moderate, severe and critical diseases was 97.8%, 86.2% and 62%, respectively (p<0.001). Conclusion: Corticosteroids are useful in COVID-19-admitted patients and provide excellent survival outcomes.

COVID-19 pandemic and unemployment rate: A hybrid unemployment rate prediction approach for developed and developing countries of Asia.

Unemployment remains a serious issue for both developed and developing countries and a driving force to lose their monetary and financial impact. The estimation of the unemployment rate has drawn researchers' attention in recent years. This investigation's key objective is to inquire about the impact of COVID-19 on the unemployment rate in selected, developed and developing countries of Asia. For experts and policymakers, effective prediction of the unemployment rate is an influential test that assumes an important role in planning the monetary and financial development of a country. Numerous researchers have recently utilized conventional analysis tools for unemployment rate prediction. Notably, unemployment data sets are nonstationary. Therefore, modeling these time series by conventional methods can produce an arbitrary mistake. To overcome the accuracy problem associated with conventional approaches, this investigation assumes intelligent-based prediction approaches to deal with the unemployment data and to predict the unemployment rate for the upcoming years more precisely. These intelligent-based unemployment rate strategies will force their implications by repeating diversity in the unemployment rate. For illustration purposes, unemployment data sets of five advanced and five developing countries of Asia, essentially Japan, South Korea, Malaysia, Singapore, Hong Kong, and five agricultural countries (i.e., Pakistan, China, India, Bangladesh and Indonesia) are selected. The hybrid ARIMA-ARNN model performed well among all hybrid models for advanced countries of Asia, while the hybrid ARIMA-ANN outperformed for developing countries aside from China, and hybrid ARIMA-SVM performed well for China. Furthermore, for future unemployment rate prediction, these selected models are utilized. The result displays that in developing countries of Asia, the unemployment rate will be three times higher as compared to advanced countries in the coming years, and it will take double the time to address the impacts of Coronavirus in developing countries than in developed countries of Asia.

The impact of COVID‐19 on unemployment rate: An intelligent based unemployment rate prediction in selected countries of Europe

Unemployment remains a major cause for both developed and developing nations, due to which they lose their financial and economic impact as a whole. Unemployment rate prediction achieved researcher attention from a fast few years. The intention of doing our research is to examine the impact of the coronavirus on the unemployment rate. Accurately predicting the unemployment rate is a stimulating job for policymakers, which plays an imperative role in a country's financial and financial development planning. Classical time series models such as ARIMA models and advanced non‐linear time series methods be previously hired for unemployment rate prediction. It is known to us that mostly these data sets are non‐linear as well as non‐stationary. Consequently, a random error can be produced by a distinct time series prediction model. Our research considers hybrid prediction approaches supported by linear and non‐linear models to preserve forecast the unemployment rates much precisely. These hybrid approaches of the unemployment rate can advance their estimates by reproducing the unemployment ratio irregularity. These models' appliance is exposed to six unemployment rate statistics sets from Europe's selected countries, specifically France, Spain, Belgium, Turkey, Italy and Germany. Among these hybrid models, the hybrid ARIMA‐ARNN forecasting model performed well for France, Belgium, Turkey and Germany, whereas hybrid ARIMA‐SVM performed outclass for Spain and Italy. Furthermore, these models are used for the best future prediction. Results show that the unemployment rate will be higher in the coming years, which is the consequence of the coronavirus, and it will take at least 5 years to overcome the impact of COVID‐19 in these countries.

COVID-19 pandemic and unemployment rate prediction for developing countries of Asia: A hybrid approach.

Unemployment is an essential problem for developing countries, which has a direct and major role in economy of a country. Understanding the pattrens of unemployment rate is critical now a days and has drawn attention of researcher from all fields of study across the globe. As unemployment plays an important role in the planning of a country's monetary progress for policymakers and researcher. Determining the unemployment rate efficiently required an advance modeling approach. Recently,numerous studies have relied on traditional testing methods to estimate the unemployment rate. Unemployment is usually nonstationary in nature. As a result, demonstrating them using traditional methods will lead to unpredictable results. It needs a hybrid approach to deal with the prediction of unemployment rate in order to deal with the issue associated with traditional techniques. This research primary goal is to examine the effect of the Covid-19 pandemic on the unemployment rate in selected countries of Asia through advanced hybrid modeling approach, using unemployment data of seven developing countries of Asian: Iran, Sri Lanka; Bangladesh; Pakistan; Indonesia; China; and India,and compare the results with conventional modeling approaches. Finding shows that the hybrid ARIMA-ARNN model outperformed over its competitors for Asia developing economies. In addition, the best fitted model was utilised to predict five years ahead unemployment rate. According to the findings, unemployment will rise significantly in developing economies in the next years, and this will have a particularly severe impact on the region's economies that aren't yet developed.

Modeling Dependent Structure Among Micro-Economics Variables Through COPAR (1)-Model in Pakistan.

A great fluctuations in oil price due to COVID-19 has been observed worldwide. Expertise of complicated relationships among economic indicators has considerable significance for consumers, specialists and strategy producers the same. This exploration work is devoted to investigating the impact of oil price fluctuations due to corona virus pandemic on inflation rate, interest rate and industrial production during lock-down using recent monthly data of Pakistan economic system starting from 2008-01 to 2020-04. At analysis stage, we generally tend to contemplate a novel autoregressive model approach to model non-linear dependence structure amongst a couple of time series. Having gain from the flexibleness of R-vine copulas, the copula autoregression with efficiency investigates the have an impact on of one-time series onto some other: it really is, one-time arrangement normally plays a vital role. Through these qualities of the model, we tend to investigate fuel price effects on industrial production, expansion rate and interest rate in my homeland. One in every of the key finding of this analysis is that there's a weak tail asymmetry, however some tail dependence, that COPAR-model with efficiency absorbs to account. Furthermore, the fashions monitor lagged reactions of interest rate and industrial production on adjustments in fuel prices inside Pakistan. The oil price result on the inflation rate; on the other hand, is quite rapid.

Enhancing healthcare supply chain resilience: decision-making in a fuzzy environment

Purpose>Resilience is a fundamental component of healthcare supply chains, as the quality and endurance of human life are dependent on them. However, there are numerous resilience-building measures, and there is a need for prioritization of those strategies. This research study aims to prioritize resilience strategies for healthcare supply chains while considering the risks that most severe, probable to occur and have the lengthiest periods of recovery.Design/methodology/approach>This research study has used multi-criteria decision-making (MCDM) techniques for analysis. Initially, the criteria for prioritization of risks, i.e. severity, probability of occurrence and recovery time were assigned with importance weights through the fuzzy analytical hierarchy process (AHP). Then, these weights were used in the fuzzy technique for order preference by similarity to ideal solution (TOPIS) analysis for prioritization of risks. Subsequently, the identified risks were used for highlighting the appropriate resilience strategies through the fuzzy quality function deployment (QFD) technique.Findings>Results indicate that Industry 4.0, multiple sourcing, risk awareness, agility and global diversification of suppliers, markets and operations are the most significant resilience strategies.Research limitations/implications>This study's limitation is that it is conducted in a general perspective, rather than reducing the context to a developing or developed country. Different areas have variable market factors, due to which potential risks occur in a different form. Moreover, resilience strategies work differently in different environments. Therefore, for future endeavors, the studies should be carried out in a limited context.Originality/value>This research study proposes a novel MCDM-based approach for ranking resilience strategies, in light of the most probable, severe and long-lasting risks. In addition, this approach has been employed for the enhancement of resilience in healthcare supply chains.

Risk of mortality due to COVID-19 and air pollution in Pakistan.

The present research aims to investigate the impact of air pollution on the number of mortalities caused by COVID-19 per Pakistani province. To do so, for each independent area of Pakistan, the observed mortality due to COVID-19 has been standardized over the entire population using standard age groups ranging from 0 to 4, 5 to 9, 10 to 14,…, 65, and above years, supported by the 2017 state people census. The impact of air pollution and COVID-19 transience among Pakistani areas, Islamabad Capital Territory (ICT), and the Federally Administered Tribal Region (FATA) was analyzed by a multiple-linear regression model, while the broad collection of attributes was observed by the resources of local spatial autocorrelation indicators, including the spatial portion of COVID-19 association. The result indicates that the observed mortality rate is much higher than predicted in certain provinces, namely, the Khyber Pakhtunkhwa and Punjab provinces, and the prevalence of PM10 was independently linked to mortality due to the corona virus. Additionally, the results of the local spatial autocorrelation indicators on the standardized mortality rate and PM10 define a collection of very higher ideologies in the broad range of KPK and the southern part of Punjab province, respectively, with a definite degree of connection between the two distributions in the Khyber Pakhtunkhwa region. In brief, this research seems to find a justification for confirming the existence of a correlation between the possibility of COVID-19 mortality and air pollution, more precisely considering air pollutants (i.e., particulate (PM10) and land take-over. To this end, the need to mediate in favor of measures aimed at eliminating emissions in the environment will be reiterated by speeding up current proposals and policies aimed at all causes of atmospheric pollution: urbanization, water and manufacturing, home heating, and transportation.

The COVID-19 pandemic and its impact on environment: the case of the major cities in Pakistan.

In Wuhan city, China, a pneumonia-like disease of unknown origin triggered a catastrophe. This disease has spread to 215 nations, affecting a diverse variety of persons. It was formally called extreme acute respiratory syndrome coronavirus 2 (SARS CoV-2), also known as coronavirus disease, by the World Health Organization as a pandemic. This pandemic forced countries to enforce a socio-economic lockdown to avoid its widespread presence. This study focuses on how the pollution of particulate matter during the coronavirus pandemic in the period from 23 March 2020 to 31 December 2020 was reduced compared to the pre-pandemic situation in the country. The improvement in air quality and atmosphere due to the coronavirus pandemic in Pakistan was identified by both ground-based and satellite observations with a primary focus on the four provincial capitals and country capitals, namely, Peshawar, Karachi, Quetta, Lahore, and Islamabad, and statistically verified through paired Student's t test. Both datasets have shown a significant decrease in the levels of PM2.5 pollutions across Pakistan (ranging from 15 to 35% for satellite observations, while 27 to 61% for ground-based observations). The result shows that poor air quality is one of the key factors for a higher COVID-19 spread rate in major Pakistani cities. By extending the same investigation across the nation, there is a greater need to investigate the connections between COVID-19 spread and air pollution. However, both higher population density rates and frequent population exposure can be partially attributed to increased levels of PM2.5 concentrations before the pandemic of the coronavirus.

The impact of COVID-19 on unemployment rate: An intelligent based unemployment rate prediction in selected countries of Europe

Abstract Unemployment remains a major cause for both developed and developing nations, due to which they lose their financial and economic impact as a whole. Unemployment rate prediction achieved researcher attention from a fast few years. The intention of doing our research is to examine the impact of the coronavirus on the unemployment rate. Accurately predicting the unemployment rate is a stimulating job for policymakers, which plays an imperative role in a country's financial and financial development planning. Classical time series models such as ARIMA models and advanced non-linear time series methods be previously hired for unemployment rate prediction. It is known to us that mostly these data sets are non-linear as well as non-stationary. Consequently, a random error can be produced by a distinct time series prediction model. Our research considers hybrid prediction approaches supported by linear and non-linear models to preserve forecast the unemployment rates much precisely. These hybrid approaches of the unemployment rate can advance their estimates by reproducing the unemployment ratio irregularity. These models' appliance is exposed to six unemployment rate statistics sets from Europe's selected countries, specifically France, Spain, Belgium, Turkey, Italy and Germany. Among these hybrid models, the hybrid ARIMA-ARNN forecasting model performed well for France, Belgium, Turkey and Germany, whereas hybrid ARIMA-SVM performed outclass for Spain and Italy. Furthermore, these models are used for the best future prediction. Results show that the unemployment rate will be higher in the coming years, which is the consequence of the coronavirus, and it will take at least 5?years to overcome the impact of COVID-19 in these countries.