Risk management is a comparatively new field and there is no core system of risk management in the construction industries of developing countries. In Pakistan, construction is an extremely risk-seeking industry lacking a good reputation for handling risk. However, it is gradually giving it more importance as a result of increased competition and construction activities. For this purpose, a survey-based study has been conducted which aims to investigate the risk management practices used in construction projects in Pakistan. To achieve the objective, data was collected from 22 contractor firms working on 100 diverse projects. The analysis indicates that risk management has been implemented at a low level in the local environment. The results also disclose that there is a higher degree of correlation between effective risk management and project success. The findings reveal the importance of risk management techniques, their usage, implication, and the effect of these techniques on the success of construction projects from the contractor’s perspective, thus convincing the key participants of projects about the use of risk management.

The coronavirus disease (COVID-19) was first reported in China (Wuhan) at the end of 2019. It has rapidly spread over 216 countries, including the USA, UK, Europe, Russia, and many Asian countries. It has affected more than 4.5 million people, and around 0.3 million deaths have been reported globally. Many preventive measures have been adopted worldwide to mitigate its spread. The government of Pakistan has also taken many preventive measures to combat the COVID-19 outbreak, such as rapid response by governance, continuous monitoring of the pandemic spread in the affected areas, and integration of resources from multiple sectors, including health, education, defense, and media. According to global statistics, the number of COVID-19 cases in the country remained remarkably lower than the expected number for the first 169 days, as compared to other countries. A total of 286,674 confirmed cases, including 16,475 active, 6,139 deaths, and 264,060 (92%) recoveries were reported. The study finds that strict adherence to national policies, effective governance, and unity at the national level resulted in better outcomes. Hence, the preventive measures, rapid responses, and strategies adopted for combating the challenges could be adopted as a learning tool for other countries having similar work environments and financial constraints. This paper can help and guide governance/public actions in response to the possible rebound of coronavirus this fall/winter.

Urbanization is playing a key role in big cities of developing countries, which, in effect, is increasing the population. This study takes care of the mega infrastructure project (Orange Line Metro Train (OLMT)) to explore and identify the H&S (Health and Safety) factors that affect the local residents and the main key stakeholders working on the project. A Sequential Mixed-Method approach of the OLMT-project includes qualitative and quantitative methods were adopted. The data have been collected from the targeted population working on the OLMT-project through a questionnaire. The main key finding of the study indicates that poor planning and a lack of communication between the public and government led to frustration. The most significant factors that identified in the study were unsafe to work practice, project scope constraints, lack in technical and material support, unsafe/bad condition, health/environment degradation, declination and loss of resources and time, no proper emergency system, and negligence in adopting safety rules and laws. The study also revealed that the consensus should also be noticed between the key stakeholders (e.g., contractors, clients, safety officials, academia) in the second round of the Delphi survey of the project. The study findings will help the key stakeholders to prioritize their energies towards attaining zero levels of inadequate health and safety practices in infrastructure projects. The study outcomes can also be generalized for the other developing countries having a similar work scenario.

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Identification of the most significant factors for evaluating road risk level is an important question in road safety research, predominantly for decision-making processes. However, model selection for this specific purpose is the most relevant focus in current research. In this paper, we proposed a new methodological approach for road safety risk evaluation, which is a two-stage framework consisting of data envelopment analysis (DEA) in combination with artificial neural networks (ANNs). In the first phase, the risk level of the road segments under study was calculated by applying DEA, and high-risk segments were identified. Then, the ANNs technique was adopted in the second phase, which appears to be a valuable analytical tool for risk prediction. The practical application of DEA-ANN approach within the Geographical Information System (GIS) environment will be an efficient approach for road safety risk analysis.

Abstract:Road safety assessment has played a crucial role in the theory and practice of transport management systems. This paper focuses on risk evaluation in the Asian region by exploring the interaction between road safety risk and influencing factors. In the first stage, a data envelopment analysis (DEA) method is applied to calculate and rank the road safety risk levels of Asian countries. In the second stage, a structural equation model (SEM) with latent variables is applied to analyze the interaction between the road safety risk level and the latent variables, measured by six observed performance indicators, i.e., financial impact, institutional framework, infrastructure and mobility, legislation and policy, vehicular road users, and trauma management. Finally, this paper illustrates the applicability of this DEA-SEM approach for road safety performance analysis.

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During the outbreak of an epidemic, it becomes significantly essential to monitor the effects of containment measures and forecast the outbreak, including the epidemic peak. Many countries have either implemented strict lockdown to counter the spread of coronavirus disease or taken necessary preventive measures across the world to reduce the outbreak of this epidemic war. Several epidemic models have been presented across the world to examine the effects of public health-related strategies on mitigating the spread of current infectious disease, yet no reputable model has been presented for Pakistan as well as other South-Asian developing countries as per the authors’ knowledge. In this research, an actual coronavirus prediction in Pakistan is presented, which may guide the decision-makers as to how this pandemic has spread across the country and how it can be controlled. Furthermore, in the absence of targeted medicines, the analysis helps to develop a precise plan for the eradication of the outbreak by adopting the calculated steps at the right time. The mathematical phenomenological models have been adopted in this study to predict, project, and simulate the overall affected cases reflected due to the recent outbreak in Pakistan. These models predict the expected growth, and the estimated results are almost well matched with the real cases. Through the calibration of parameters and analyzing the current situation, forecast for the appearance of new cases in Pakistan is reported till the end of this year. The constant level of number of patients and time to reach specific levels are also reported through the simulations. The drastic conditions are also discussed which may occur if all the preventive restraints are removed. This research quantitatively describes the significant characteristics of the spread of corona cases. It acknowledges and provides an understanding of a short-term and long-term transmission of coronavirus outbreak in the country as three evolutionary phases. Therefore, this research provides a pathway to cope with the emerging threat of a severe outbreak in developing and nondeveloping countries.