The Road Ahead (Editors)

This final chapter of the book "Frontiers of COVID-19: Scientific and Clinical Perspectives of the Novel SARS-CoV-2" takes the contents of the book and lesson learned of the clinical and epidemiological aspects of COVID-19 to the next level and provides guidelines and the road map into the future, beyond COVID-19. The aim is to present the most recent understanding of the fast-changing dynamics of COVID-19 and to help our clinicians and physicians better prepare for the road ahead. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Frontiers of COVID-19: Scientific and Clinical Aspects of the Novel Coronavirus 2019

This book aims to serve the critical interests of the global community by supplying the most current knowledge and understanding of Covid-19 epidemiology, treatment, and prognoses. There was much uncertain and contradictory information published in the first year of the novel coronavirus. The dynamics of COVID-19 have now been realized, including the type of antibodies produced in infected patients and their limited lasting endurance. This book will set the record straight on the concept of "herd immunity” and explore the current vaccine trials taking place in different countries. This comprehensive book will illuminate recent advances regarding COVID-19 and offer a possible roadmap on how to move forward. Frontiers of COVID-19: A Pathophysiology and Epidemiology Roadmap of Novel Coronavirus Disease will be a vital and forward-looking guide for infectious disease clinicians, scientists and researchers, and students at the graduate level. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Bayesian Modeling of Labor Earnings in Construction

Labor costs constitute a significant portion of construction costs. Reliable forecasts provide insight into the movements of labor costs and are critical to the success of projects. Past studies have primarily focused on forecasting construction cost indexes or material costs. Only a few studies have concentrated on forecasting construction labor costs. This study presents a multivariate Bayesian structural time series (MBSTS) model to characterize the future values of construction labor's average hourly earnings (AHE) using a set of candidate predictors. The methodologies commonly used by past studies do not adequately address the uncertainties associated with the modeling process. In contrast, MBSTS recognizes the uncertainty in its modeling process, which enables practitioners to quantify and account for future labor cost risks in decisions. Furthermore, the MBSTS method results in a transparent model, helping analysts investigate the rationality of the parameters. This article trains MBSTS models under four different data subset lengths (i.e., 150, 144, 138, and 132 months) to study the consistency of the explanatory variables and their corresponding coefficients. The analysis results indicate that the gross domestic product (GDP), housing starts (HS), number of building permits (BP), Construction Cost Index (CCI), Dow Jones Industrial Average (DJI), and Standard and Poor's 500 index (SPI) are the most frequently used predictors in the regression component of the MBSTS models. The results indicate an inverse relationship between AHE from one side and HS and BP from the other. Likewise, a direct relationship exists between AHE and GDP, CCI, DJI, and SPI. The MBSTS model performed well on the validation subset in the midrange prediction intervals (i.e., 12- and 18-month periods). However, it was outperformed by conventional time series models [i.e., seasonal autoregressive integrated moving average (SARIMA)] when used for short-term forecasting. The proposed framework can be applied to facilitate monetary resource allocation in projects.

Understanding the Relation of Psychological/Behavioral Factors and Cycling During the Covid-19 Pandemic: A Case Study in Iran

Previous research on travel behavior has concentrated on the behavior of traveling by cars, especially by private vehicles, while the research on cycling has focused on cycling infrastructure, the built environment, and the natural environment. Furthermore, the studies conducted during pandemics are mostly based on behavioral changes in motorized transportation. The present research tries to identify and evaluate the variables influencing cyclist behavior during covid-19 pandemic. In this research, the sample size retrieved from a survey of 375 participants was checked with Cronbach's alpha standard and estimated using confirmatory factor analysis. Results show that the variables related to health protocols can greatly impact knowing the behavior of cyclists in the time of Covid-19. Furthermore, the results show that the health issues of shared bikes can be an obstacle for people to use them more. © 2023, The Author(s), under exclusive licence to Intelligent Transportation Systems Japan.

Bayesian Modeling of Labor Earnings in Construction

Labor costs constitute a significant portion of construction costs. Reliable forecasts provide insight into the movements of labor costs and are critical to the success of projects. Past studies have primarily focused on forecasting construction cost indexes or material costs. Only a few studies have concentrated on forecasting construction labor costs. This study presents a multivariate Bayesian structural time series (MBSTS) model to characterize the future values of construction labor's average hourly earnings (AHE) using a set of candidate predictors. The methodologies commonly used by past studies do not adequately address the uncertainties associated with the modeling process. In contrast, MBSTS recognizes the uncertainty in its modeling process, which enables practitioners to quantify and account for future labor cost risks in decisions. Furthermore, the MBSTS method results in a transparent model, helping analysts investigate the rationality of the parameters. This article trains MBSTS models under four different data subset lengths (i.e., 150, 144, 138, and 132 months) to study the consistency of the explanatory variables and their corresponding coefficients. The analysis results indicate that the gross domestic product (GDP), housing starts (HS), number of building permits (BP), Construction Cost Index (CCI), Dow Jones Industrial Average (DJI), and Standard and Poor's 500 index (SPI) are the most frequently used predictors in the regression component of the MBSTS models. The results indicate an inverse relationship between AHE from one side and HS and BP from the other. Likewise, a direct relationship exists between AHE and GDP, CCI, DJI, and SPI. The MBSTS model performed well on the validation subset in the midrange prediction intervals (i.e., 12- and 18-month periods). However, it was outperformed by conventional time series models [i.e., seasonal autoregressive integrated moving average (SARIMA)] when used for short-term forecasting. The proposed framework can be applied to facilitate monetary resource allocation in projects.

Do People Desire to Cycle More During the COVID-19 Pandemic? Investigating the Role of Behavioural Characteristics through a Structural Model

Background: Most cycling behaviour studies have defined it using objective variables and focused on normal conditions. Objective: This study applies latent class analysis to a sample of 375 survey respondents in Tehran, the Capital city of Iran, exploring the variables influencing cycling behaviour during pandemic covid-19. Methods: We made a statistical comparison among the data obtained from the questionnaires and the statistical data of the 2016 census. A structural equation modeling (SEM) was developed. Results: Fourteen indicators define three latent variables. Cycling behaviour is defined by these three latent factors and three indicators. This paper goes through each of the indicators and their impact on latent variables. The findings show that latent factors have a direct impact on cycling behaviour. Conclusion: Structural equation modeling (SEM) is a great tool for defining cyclist behaviour analysis that shows the positive and negative influence of variables on cycling rate during a covid-19 pandemic. There are some limitations in the area of this study in developing countries discussed in the paper. © 2022 Rashidi et al.

COVID-19 and food security in Malaysia: implications on smallholding agriculture

Over the last two decades, the world has been confronted with several outbreaks of infectious diseases. Ebola, influenza A (H1N1), SARS, MERS, Zika and most recently, COVID-19, have had a serious global impact on economic instability, local and global public resources and above all, people's health. As the effect of COVID-19 is significant to the world market, it has also indirectly impacted the agricultural sector and hence has significantly exposed the strengths and limitations of the farming systems in terms of resiliency, production and food security which offers important lessons for policymakers. Hence, this study was conducted to analyse the impact of COVID-19 pandemics on the agricultural industry especially the smallholders who are more vulnerable during or post-pandemic due to the lack of resilient supply networks, low periphery of market access and limited production. In doing so, a survey was conducted, which began in May 2020, consisting of online questions that contain information about the impact of COVID-19 on the smallholding sector and the challenges faced by smallholders including crops, livestock, fisheries and aquaculture in Malaysia. A total sample of 217 smallholders or farmers from several states in Peninsular Malaysia was obtained within the Movement Control Order (MCO) period and later extended to the Conditional MCO (CMCO). Almost 86% of respondents answered that COVID-19 had heavily affected agriculture-related activities in Malaysia. Using a principal component analysis, our findings reveal four major issues or challenges those farmers faced during the MCO/CMCO namely income, uncertainties of supply and price, logistics and marketing activities.

Assessment of IgM/IgG Antibody Detection in Comparison with RT-PCR Technique for Diagnostic Purposes in Patients with COVID-19

Background and Aim: The diagnosis of COVID-19 is an essential step toward controlling the pandemic. For this purpose, a series of laboratory methods have been developed. This study evaluated the efficiency of the ELISA method for COVID-19 IgM and IgG detection. Materials and Methods In this case-control study, 46 blood samples from PCR positive COVID-19 patients and 49 samples from PCR negative COVID-19 subjects were collected. Subsequently, the presence of IgM and IgG in all blood samples was assessed using the Pishtaz Teb ELISA kit. Results: The data demonstrated that among 49 PCR negative, 40 (81.6%) were IgM negative and 9 (19.1%) were IgM positive. Besides, out of the 49 PCR-negative patients, 42 (85.7%) and 7 (14.3%) were IgG negative and positive, respectively. In 46 PCR-positive individuals, 40 (87%) were IgM negative, while 6 (13 %) were IgM positive. Of the 46 PCR-positive patients, 24 (52.2%) were IgG negative, and 22(47.8%) were IgG positive. Conclusion: Our results showed that detection of SARS-CoV-2 IgG and IgM using Pishtaz Teb ELISA kit is not enough for COVID diagnosis, but it can serve as a diagnostic RNA supplement to confirm infection with SARS-CoV-2 in approved clinics and other scientific communities, owing to its easy, rapid, and inexpensive availability. © 2021. All Rights Reserved.

Dynamic diversification benefits of Sukuk and conventional bonds for the financial performance of MENA region companies: empirical evidence from COVID-19 pandemic period

Purpose This study aims to investigate the spillover between the Middle East and North Africa (MENA) stock index and several security indices, including Sukuk and conventional bond, and ultimately compare the hedge effectiveness of Sukuk and conventional bond. Design/methodology/approach The study uses VAR (1)-asymmetric Baba, Engle, Kraft and Kroner-multivariate generalized autoregressive conditional heteroskedasticity (1,1) model to analyze the volatility and shock and asymmetric shock spillover between Sukuk index and several bond indices in the MENA region including, Bond, All Bond, High Yield Bond and Bond and Sukuk and MENA stock market index and ultimately compare the hedging capabilities of Sukuk and conventional bonds by calculating the optimal portfolio weights for securities indices and stock portfolios and hedge effectiveness of security indices. Findings Results indicate that there is no shock, volatility and asymmetric shock spillover between the Sukuk index and MENA stock index, implying that Sukuk indices behave independently from MENA stock indices;however, there is shock and asymmetric shock spillover between MENA stock indices and security indices that include conventional bonds. The result of optimal portfolio weights and corresponding hedge effectiveness indicate that Sukuk is the most significant asset among other security indices in diversifying and hedging stock MENA portfolios. Moreover, the hedge effectiveness of Sukuk shows persistent trends during both the normal and crisis periods. Practical implications The study suggests that MENA stock market investors and investment managers should add Sukuk instead of the conventional bond to their portfolio to hedge their portfolio against investment risks during both normal and crisis periods. Originality/value Although many studies compare many aspects of Sukuk and conventional bonds, this is the first study that compares the hedge effectiveness of Sukuk and conventional bond based on the time-varying optimal portfolio weights strategy.