Smart Cities after COVID-19: Building a conceptual framework through a multidisciplinary perspective.

This study provides theoretical grounds for planning smart cities using multidisciplinary approaches, offering insightful suggestions to researchers and policy- and decision-makers. Its main purpose is to contribute to the debate on the new connotations of the smart city paradigm in the context of the COVID-19 pandemic. It will emphasize how the Internet of Things and related technologies will collaborate to develop an antivirus-built environment against future pandemics. In this context, the study proposes a conceptual framework that provides a futuristic vision of prevention control, contingency planning, and measures against future risks. Although a smart city ecosystem improves citizens' lives, building it may involve design, implementation, and operational challenges that must be addressed.

Smart Data Analytics on COVID-19 Data

Advances in computers, information and networks has brought a digital cyber world to our daily lives. They have generated numerous digital things (or cyber entities), which have resided in the cyber world. Meanwhile, countless real things in the conventional physical, social and mental worlds have possessed cyber mappings (or cyber components) to have a cyber existence in cyber world. Consequently, cyberization has been an emerging trend forming the new cyber world and reforming conventional worlds towards cyber-enabled hyper-worlds. As such, cybermatics helps build systematic knowledge about new phenomena, behaviors, properties and practices in the cyberspace, cyberization and cyber-enabled hyper-worlds. Cybermatics is characterized by catching up with the human intelligence (e.g. intelligent sensing, making decision and control, etc.), as well as learning from the nature-inspired attributes (e.g., dynamics, self-adaptability, energy saving). As a cybermatics technique, smart data analytics helps filter out the noise data and produce valuable data. In this paper, we focus on smart data analytics on health data related to coronavirus disease 2019 (COVID-19). It builds temporal and demographic hierarchies, which capture characteristics of COVID-19 patients, to discover valuable knowledge and information about temporal-demographic characteristics of these patients. Evaluation on real-life COVID-19 epidemiological data demonstrates the practicality of our solution in conducting smart data analytics on COVID-19 data. © 2021 IEEE.

Smarter Open Government Data for Society 5.0: Are Your Open Data Smart Enough?

Nowadays, governments launch open government data (OGD) portals that provide data that can be accessed and used by everyone for their own needs. Although the potential economic value of open (government) data is assessed in millions and billions, not all open data are reused. Moreover, the open (government) data initiative as well as users' intent for open (government) data are changing continuously and today, in line with IoT and smart city trends, real-time data and sensor-generated data have higher interest for users. These "smarter" open (government) data are also considered to be one of the crucial drivers for the sustainable economy, and might have an impact on information and communication technology (ICT) innovation and become a creativity bridge in developing a new ecosystem in Industry 4.0 and Society 5.0. The paper inspects OGD portals of 60 countries in order to understand the correspondence of their content to the Society 5.0 expectations. The paper provides a report on how much countries provide these data, focusing on some open (government) data success facilitating factors for both the portal in general and data sets of interest in particular. The presence of "smarter" data, their level of accessibility, availability, currency and timeliness, as well as support for users, are analyzed. The list of most competitive countries by data category are provided. This makes it possible to understand which OGD portals react to users' needs, Industry 4.0 and Society 5.0 request the opening and updating of data for their further potential reuse, which is essential in the digital data-driven world.

COVID-19 Symptom-Related Google Searches and Local COVID-19 Incidence in Spain: Correlational Study.

BACKGROUND: COVID-19 is one of the biggest pandemics in human history, along with other disease pandemics, such as the H1N1 influenza A, bubonic plague, and smallpox pandemics. This study is a small contribution that tries to find contrasted formulas to alleviate global suffering and guarantee a more manageable future. OBJECTIVE: In this study, a statistical approach was proposed to study the correlation between the incidence of COVID-19 in Spain and search data provided by Google Trends. METHODS: We assessed the linear correlation between Google Trends search data and the data provided by the National Center of Epidemiology in Spain-which is dependent on the Instituto de Salud Carlos III-regarding the number of COVID-19 cases reported with a certain time lag. These data enabled the identification of anticipatory patterns. RESULTS: In response to the ongoing outbreak, our results demonstrate that by using our correlation test, the evolution of the COVID-19 pandemic can be predicted in Spain up to 11 days in advance. CONCLUSIONS: During the epidemic, Google Trends offers the possibility to preempt health care decisions in real time by tracking people's concerns through their search patterns. This can be of great help given the critical, if not dramatic need for complementary monitoring approaches that work on a population level and inform public health decisions in real time. This study of Google search patterns, which was motivated by the fears of individuals in the face of a pandemic, can be useful in anticipating the development of the pandemic.