This survey paper comprehensively reviews Digital Twin (DT) technology, a virtual representation of a physical object or system, pivotal in Smart Cities for enhanced urban management. It explores DT's integration with Machine Learning for predictive analysis, IoT for real-time data, and its significant role in Smart City development. Addressing the gap in existing literature, this survey analyzes over 4,220 articles from the Web of Science, focusing on unique aspects like datasets, platforms, and performance metrics. Unlike other studies in the field, this research paper distinguishes itself through its comprehensive and bibliometric approach, analyzing over 4,220 articles and focusing on unique aspects like datasets, platforms, and performance metrics. This approach offers an unparalleled depth of analysis, enhancing the understanding of Digital Twin technology in Smart City development and setting a new benchmark in scholarly research in this domain. The study systematically identifies emerging trends and thematic topics, utilizing tools like VOSviewer for data visualization. Key findings include publication trends, prolific authors, and thematic clusters in research. The paper highlights the importance of DT in various urban applications, discusses challenges and limitations, and presents case studies showcasing successful implementations. Distinguishing from prior studies, it offers detailed insights into emerging trends, future research directions, and the evolving role of policy and governance in DT development, thereby making a substantial contribution to the field.

Globally, many research works are going on to study the infectious nature of COVID-19 and every day we learn something new about it through the flooding of the huge data that are accumulating hourly rather than daily which instantly opens hot research avenues for artificial intelligence researchers. However, the public’s concern by now is to find answers for two questions; (1) When this COVID-19 pandemic will be over? and (2) After coming to its end, will COVID-19 return again in what is known as a second rebound of the pandemic? In this work, we developed a predictive model that can estimate the expected period that the virus can be stopped and the risk of the second rebound of COVID-19 pandemic. Therefore, we have considered the SARIMA model to predict the spread of the virus on several selected countries and used it for predicting the COVID-19 pandemic life cycle and its end. The study can be applied to predict the same for other countries as the nature of the virus is the same everywhere. The proposed model investigates the statistical estimation of the slowdown period of the pandemic which is extracted based on the concept of normal distribution. The advantages of this study are that it can help governments to act and make sound decisions and plan for future so that the anxiety of the people can be minimized and prepare the mentality of people for the next phases of the pandemic. Based on the experimental results and simulation, the most striking finding is that the proposed algorithm shows the expected COVID-19 infections for the top countries of the highest number of confirmed cases will be manifested between Dec-2020 and Apr-2021. Moreover, our study forecasts that there may be a second rebound of the pandemic in a year time if the currently taken precautions are eased completely. We have to consider the uncertain nature of the current COVID-19 pandemic and the growing inter-connected and complex world, that are ultimately demanding flexibility, robustness and resilience to cope with the unexpected future events and scenarios.