Impact of Economic Policy Uncertainty and Pandemic Uncertainty on International Tourism: What do We Learn From COVID-19?

Uncertainty is an overarching aspect of life that is particularly pertinent to the present COVID-19 pandemic crisis; as seen by the pandemic's rapid worldwide spread, the nature and level of uncertainty have possibly increased due to the possible disconnects across national borders. The entire economy, especially the tourism industry, has been dramatically impacted by COVID-19. In the current study, we explore the impact of economic policy uncertainty (EPU) and pandemic uncertainty (PU) on inbound international tourism by using data gathered from Italy, Spain, and the United States for the years 1995-2021. Using the Quantile on Quantile (QQ) approach, the study confirms that EPU and PU negatively affected inbound tourism in all states. Wavelet-based Granger causality further reveals bi-directional causality running from EPU to inbound tourism and unidirectional causality from PU to inbound tourism in the long run. The overall findings show that COVID-19 has had a strong negative effect on tourism. So resilient skills are required to restore a sustainable tourism industry.

How maritime logistic SMEs lead and gain competitive advantage by applying information technology?

The aim of this study was to examine the importance of information technology for logistics Small Medium Entreprise (SMEs) in Pakistan. It is the time of technological rapidness; especially after COVID-19, the word business has majorly transformed into a digital business. If an organization did not shift toward technology, it would be hard for it to even sustain in this rapid era. This study adopts the questionnaire after extensive literature review. A quantitative study was conducted among logistics SMEs in Pakistan to empirically verify what competitive advantages they are leading and gaining from information technology and how much information technology is important for their sustainability. The literature lacks information about the ways in which information technology has been integrated into logistics SMEs operating model, and more specifically, there is no information about IT valence, IT resource commitment, IT managerial commitment, and IT competency. The research is primarily quantitative in nature, where the data were collected via a close-ended questionnaire from 340 logistics SMEs in Pakistan. The independent variable of this research was information technology (i.e., IT valence, IT resource commitment, IT managerial commitment, and IT competency), whereas the dependent variable was competitive advantage. The study found that IT had a significant impact on the competitive advantage of logistics SMEs operating in Pakistan. All the variables related to IT had a significant impact on competitive advantage, which included IT valence, IT resource commitment, IT managerial commitment, and IT competency. This study helps managers and owners of logistics SMEs in decision-making, who can understand how much IT can enhance their performance and reduce their risks. This study has been specifically conducted with logistics SMEs operating in Pakistan, which means that there is much scope to be worked upon, i.e., by selecting companies operating in other countries and comparing them with current findings. This study observes the impact of information technology (i.e., IT valence, IT resource commitment, IT managerial commitment, and IT competency) on competitive advantage, and other independent variables can be studied to find the impact on competitive advantage.

Pneumonia Classification: A limited data approach for global understanding (preprint)

With the evolution of the human race, the associated diseases have also evolved. Pneumonia treated as the simple flu and allergy in the early stages of its inception is now threatening to humankind in various shapes like SARs and Covid. The advanced disease requires equal treatments and diagnosis. Our research tried to find and classify pneumonia inflammation within chest x-rays (CXR) with very limited datasets and has attempted to ensure a global perspective, i.e. one that addresses all possible inflammation regions within the CXR. In addition to having medical grade classification outputs in terms of accuracy and recall, we have also guaranteed to meet the medical requirements of classification justification with the help of modified class activation maps (mCAM). The training of a model having a global perspective understanding is carried out with the help of capsules network cluster (CsNC), which enables us to learn various geometrical, orientation, and positional views of the inflammation within the CXR. Our 16-capsules cluster helped understand different views easily within the same CXR without going through any image augmentation, as generally required by current detection models, thus reducing the overall training and evaluation time. We performed extensive experiments on the RSNA pneumonia dataset of CXR images using a set of evaluation metrics. We have been able to acquire up to 98.3% accuracy with a 99.5% recall during our final trials. We tested our final trained model over generic x-ray images acquired from clinics and found promising results over that.

An Optimized Decision Support Model for COVID-19 Diagnostics Based on Complex Fuzzy Hypersoft Mapping

COVID-19 has shaken the entire world economy and affected millions of people in a brief period. COVID-19 has numerous overlapping symptoms with other upper respiratory conditions, making it hard for diagnosticians to diagnose correctly. Several mathematical models have been presented for its diagnosis and treatment. This article delivers a mathematical framework based on a novel agile fuzzy-like arrangement, namely, the complex fuzzy hypersoft (CFHS) set, which is a formation of the complex fuzzy (CF) set and the hypersoft set (an extension of soft set). First, the elementary theory of CFHS is developed, which considers the amplitude term (A-term) and the phase term (P-term) of the complex numbers simultaneously to tackle uncertainty, ambivalence, and mediocrity of data. In two components, this new fuzzy-like hybrid theory is versatile. First, it provides access to a broad spectrum of membership function values by broadening them to the unit circle on an Argand plane and incorporating an additional term, the P-term, to accommodate the data's periodic nature. Second, it categorizes the distinct attribute into corresponding sub-valued sets for better understanding. The CFHS set and CFHS-mapping with its inverse mapping (INM) can manage such issues. Our proposed framework is validated by a study establishing a link between COVID-19 symptoms and medicines. For the COVID-19 types, a table is constructed relying on the fuzzy interval of [0,1]. The computation is based on CFHS-mapping, which identifies the disease and selects the optimum medication correctly. Furthermore, a generalized CFHS-mapping is provided, which can help a specialist extract the patient's health record and predict how long it will take to overcome the infection.

Empirical assessment of government policies and flattening of the COVID19 curve.

The objective of the study is 2-fold. First, it estimates the 2019 new coronavirus disease (COVID19) flattening curve using Panel Random Coefficient Model. This allows each country to have its trajectory while allowing for random error effects to transfer across countries. Second, it calculates the expected number of days to reach the flattening point of COVID19 curve and estimate the empirical effectiveness of government policies around the world using Poisson regression. This study avails global COVID19 incidence data for 190 countries between January 22, 2020 and May 11, 2020. In the absence of a vaccine or of more appropriate treatment options, non-pharmaceutical approaches must be used to control the spread of the COVID19. This study proposed that the contact tracing, stay at home restrictions and international movement restrictions are most effective in controlling the spread and flattening the COIVD19 curve. At the same time, habits that hurt the immune system like smoking have a negative effect on the flattening of the curve. The government should integrate these policies in their lockdown plan to make it smart lockdown.

The role of healthcare supply chain management in the wake of COVID-19 pandemic: hot off the press

Purpose>The study aims to examine the role of health-care supply chain management during the COVID-19 pandemic in a cross-section of 42 selected sub-Saharan African (SSA) countries.Design/methodology/approach>The study used cross-sectional robust least square regression for parameter estimates.Findings>The results confirmed the N-shaped relationship between the health-care logistics performance index (HLPI) and COVID-19 cases. It implies that initially HLPI increases along with an increase in COVID-19 cases. Later down, it decreases COVID-19 cases by providing continued access to medical devices and personal protective equipment. Again, it increases due to resuming economic activities across countries.Practical implications>The continuing health-care supply chain is crucial to minimize COVID-19 cases. The international support from the developed world in providing health-care equipment, debt resettlement and resolving regional conflicts is deemed desirable to escape the SSA countries from the COVID-19 pandemic.Originality/value>The importance of the health-care supply chain during the COVID-19 pandemic is evident in the forecasting estimates, which shows that from August 2021 to April 2022, increasing the health-care supply chain at their third-degree level would reduce coronavirus registered cases. The results conclude that SSA countries required more efforts to contain coronavirus cases by thrice increasing their health-care logistics supply chain.

Contact tracing along with epidemiological and clinical characteristics of Novel corona virus 2019 in prisoners of Camp Jail, Lahore – A prospective case ascertaining study

Background & Objectives: Prisons are reported as hub for communicable disease, as the closed environment, overcrowding, poor hygienic conditions facilitate the disease transmission. This study was conducted to describe contact tracing to identify, educate and manage COVID-19 in camp jail Lahore and to describe clinical and epidemiological features of disease in prisoners. Methods: After diagnosis of primary case of COVID-19 on 24th March, 2020 in camp jail, 527 suspected cases were identified through contact tracing. The health department-initiated case identification through contact tracing, isolation of confirmed and suspected cases, and quarantine of exposed persons and establishment of 100 bedded hospital in jail for infection prevention and control and treatment. Baseline characteristics of primary case and secondary cases were described along with the secondary attack rate of infection. Results: Mean age of secondary cases was 36.9(11.5) years with mean stay of 14.9(13.6) months. Two third of the prisoners were from Punjab. 11 % were illiterate and almost half were under metric. 527 prisoners were labelled as suspected cases through contact tracing and 59 out of 527 suspected prisoners tested COVID positive through RT-PCR with few reporting mild respiratory symptoms. Fifty five out of 59 tested negatives on day-5 and all have uneventful recovery by day-21. Secondary attack rate was 11%. Conclusions: In order to prevent Covid-19 outbreaks, proactive containment and comprehensive contact tracing to identify monitor and manage cases and contacts, in incarcerated facilities like prisons is a public health solution to prevent and control large scale epidemic. Active monitoring for infected patients, and implementing timely infection prevention and control measures are mandatory for highly infectious Covid-19 in this vulnerable population.

Knowledge management process, knowledge based innovation: Does academic researcher's productivity mediate during the pandemic of covid-19?

Drucker's knowledge-worker productivity theory and knowledge-based view of the firm theory are widely employed in many disciplines but there is little application of these theories in knowledge-based innovation among academic researchers. Therefore, this study intends to evaluate the effects of the knowledge management process on knowledge-based innovation alongside with mediating role of Malaysian academic researchers' productivity during the Pandemic of COVID-19. Using a random sampling technique, data was collected from 382 academic researchers. Questionnaires were self-administered and data was analyzed via Smart PLS-SEM. Knowledge management process and knowledge workers' productivity have a positive and significant relationship with the knowledge-based innovation among academic researchers during the Pandemic of COVID-19. In addition, knowledge workers' productivity mediates the relationship between the knowledge management process (knowledge creation, knowledge acquisition, knowledge sharing, and knowledge utilization) and knowledge-based innovation during the Pandemic of COVID-19. Results have also directed knowledge sharing as the key factor in knowledge-based innovation and a stimulating task for management discipline around the world during the Pandemic of COVID-19. This study provides interesting insights on Malaysian academic researchers' productivity by evaluating the effects of knowledge creation, acquisition, sharing, and application on the knowledge-based innovation among academic researchers during the Pandemic of COVID-19. These useful insights would enable policymakers to develop more influential educational strategies. By assimilating the literature of defined variables, the main contribution of this study is the evaluation of knowledge creation, acquisition, sharing, and utilization into knowledge-based innovation alongside the mediating role of knowledge workers productivity in the higher education sector of Malaysia during the Pandemic of COVID-19.

How world uncertainties and global pandemics destabilized food, energy and stock markets? Fresh evidence from quantile on quantile regressions

In this study, we analyse the impact of world uncertainty, global pandemics, including the recent COVID-19, and geopolitical risk on global food, energy commodities, and stock markets from a global perspective. The study uses quantile on quantile regression (QQR) and a quantile causality test using quarterly data from 1996Q1 to 2020Q1. Overall, the study results indicate heterogeneity in the influence of the world uncertainty index, global pandemic index, and geopolitical risk on the global food, energy, and stock markets. However, our findings predominantly show a negative impact of world uncertainty, and global pandemic on global food, energy commodities, and stock markets with substantial variations across markets (food, energy, and stock) and quantiles within each market. For robustness, this study applied the geopolitical risk and found the similar impact on food, energy and stock markets. Additionally, the quantile causality test confirms unidirectional causality running from world uncertainty, global pandemic uncertainty, and geopolitical risk to world food, energy, and stock markets. Our findings give a clear guideline to policymakers and investors managing food, energy, and equity markets during uncertainty and pandemic periods.

Factors Associated with Disease Severity and Mortality among Patients with Coronavirus Disease 2019: A Systematic Review and Meta-Analysis (preprint)

Background: Understanding the factors associated with disease severity and mortality in Coronavirus disease (COVID19) is imperative to effectively triage patients. We performed a systematic review to determine the demographic, clinical, laboratory and radiological factors associated with severity and mortality in COVID-19. Methods: We searched PubMed, Embase and WHO database for English language articles from inception until May 8, 2020. We included Observational studies with direct comparison of clinical characteristics between a) patients who died and those who survived or b) patients with severe disease and those without severe disease. Data extraction and quality assessment were performed by two authors independently. Results: Among 15680 articles from the literature search, 109 articles were included in the analysis. The risk of mortality was higher in patients with increasing age, male gender (RR 1.45; 95%CI 1.23,1.71), dyspnea (RR 2.55; 95%CI 1.88,2.46), diabetes (RR 1.59; 95%CI 1.41,1.78), hypertension (RR 1.90; 95%CI 1.69,2.15). Congestive heart failure (OR 4.76; 95%CI 1.34,16.97), hilar lymphadenopathy (OR 8.34; 95%CI 2.57,27.08), bilateral lung involvement (OR 4.86; 95%CI 3.19,7.39) and reticular pattern (OR 5.54; 95%CI 1.24,24.67) were associated with severe disease. Clinically relevant cut-offs for leukocytosis(>10.0 x109/L), lymphopenia(< 1.1 x109/L), elevated C-reactive protein(>100mg/L), LDH(>250U/L) and D-dimer(>1mg/L) had higher odds of severe disease and greater risk of mortality. Conclusion: Knowledge of the factors associated of disease severity and mortality identified in our study may assist in clinical decision-making and critical-care resource allocation for patients with COVID-19.

Predictors of Severity and Mortality Among Patients with Coronavirus Disease 2019: A Systematic Review and Meta-Analysis (preprint)

Background: The COVID-19 pandemic has overwhelmed the global health systems, and it is imperative to understand how to effectively triage patients. Deeper understanding of predictors of disease severity and mortality is pivotal to effectively triage patients with COVID-19 to maximize the benefit of scarce intensive care unit resources, while minimizing the potential harm of outpatient management of ill patients. Methods: We performed a systematic review and meta-analysis of observational studies, assessing factors associated with severity and mortality among laboratory confirmed COVID-19 patients. We searched PubMed, Embase and WHO database for articles up to May 8, 2020. Randomized trials were excluded. Odds ratios (with 95% CI) and risk ratios (with 95% CI) were used to determine the association between the various demographic, clinical, laboratory and radiological factors and the development of severe disease or mortality. We performed meta-regression to determine the percentage change in the occurrence of the outcomes. Heterogeneity across studies were assessed using I2 and Tau2 statistics. Findings: Among 15680 articles obtained from the literature search, 109 articles were included in the analysis. Increasing age and male gender were associated with higher mortality rates and severe disease. The risk of mortality was higher in patients presenting with dyspnea (RR 2·55, 95% CI 1·88–2·46) and hemoptysis (RR 1·62, 95%CI 1·25–2·11). Co-morbidities such as diabetes (RR 1·59, 95%CI 1·41–1·78), hypertension (RR 1·90, 95%CI 1·69–2·15), cardiovascular diseases (RR 2·27, 95% CI 1·88–2·79) and chronic obstructive pulmonary disease (RR 2·29, 95% CI 1·90–2·75) were associated with a higher risk of death. In-hospital complications such as acute respiratory distress syndrome (ARDS), sepsis, shock and acute cardiac injury had adverse outcomes, with ARDS having the highest risk ratio (RR 20·19, 95% CI 10·87–37·52). Lung consolidation on computed tomography (CT) had significant association with death (RR 2·07, 95% CI 1·35–3·16). Congestive heart failure (OR 4·76, 95% CI 1·34–16·97) had greater odds of developing severe disease. Among the radiological features, hilar lymphadenopathy (OR 8·34, 95%CI 2·57–27·08), bilateral lung involvement (OR 4·86, 95%CI 3·19–7·39) and reticular pattern (OR 5·54, 95%CI 1·24–24·67) were more frequently seen in patients with severe disease. Patients with leukocytosis, lymphopenia, elevated C-reactive protein and D-dimer levels had higher odds of severe disease and greater risk of mortality. Interpretation: Our study identified several important predictors of disease severity and mortality among patients with COVID-19. Knowledge of these predictors might help in the prioritization and management of these patients. Funding: NoneDeclaration of Interests: The authors declare no competing interests.