Guest Editorial Introduction to the Special Issue on Intelligent Transportation Systems in Epidemic Areas

The COVID-19 pandemic has posed significant challenges to transportation systems in various aspects, such as transferring patients and medical resources, enforcing physical distancing in public transportation, and controlling virus transmission through transportation networks. To address these challenges, a variety of artificial intelligence technologies, such as autonomous driving, big data analytics, intelligent vehicle routing and scheduling, and intelligent traffic control, have been employed in the design of intelligent transportation systems. This Special Issue provides a forum for researchers and practitioners to present the most recent advances in presenting and applying intelligent technologies to promote transportation systems in large-scale epidemics.

Evolutionary Optimization of COVID-19 Vaccine Distribution With Evolutionary Demands

Vaccination uptake has become the key factor that will determine our success in containing the coronavirus pneumonia (COVID-19) pandemic. Efficient distribution of vaccines to inoculation spots is crucial to curtailing the spread of the novel COVID-19 pandemic. Normally, in a big city, a huge number of vaccines need to be transported from central depot(s) through a set of satellites to widely scattered inoculation spots by special-purpose vehicles every day. Such a large two-echelon vehicle routing problem is computationally difficult. Moreover, the demands for vaccines evolve with the epidemic spread over time, and the actual demands are hard to determine early and exactly, which not only increases the problem difficulty but also prolongs the distribution time. Based on our practical experience of COVID-19 vaccine distribution in China, we present a hybrid machine learning and evolutionary computation method, which first uses a fuzzy deep learning model to forecast the demands for vaccines for each next day, such that we can predistribute the forecasted number of vaccines to the satellites in advance;after obtaining the actual demands, it uses an evolutionary algorithm (EA) to route vehicles to distribute vaccines from the satellites/depots to the inoculation spots on each day. The EA saves historical problem instances and their high-quality solutions in a knowledge base, so as to capture inherent relationship between evolving problem inputs to solutions;when solving a new problem instance on each day, the EA utilizes historical solutions that perform well on the similar instances to improve initial solution quality and, hence, accelerate convergence. Computational results on real-world instances of vaccine distribution demonstrate that the proposed method can produce solutions with significantly shorter distribution time compared to state-of-the-arts and, hence, contribute to accelerating the achievement of herd immunity.

[Analysis of CT features of 15 children with 2019 novel coronavirus infection].

Objective: To explore imaging characteristics of children with 2019 novel coronavirus (2019-nCoV) infection. Methods: A retrospective analysis was performed on clinical data and chest CT images of 15 children diagnosed with 2019-nCoV infection. They were admitted to the Third People's Hospital of Shenzhen from January 16 to February 6, 2020. The distribution and morphology of pulmonary lesions on chest CT images were analyzed. Results: Among the 15 children, 5 were males and 10 females, aged from 4 to 14 years. Five of the 15 children were febrile and 10 were asymptomatic on the first visit. The first nasal or pharyngeal swab samples in all the 15 cases were positive for 2019-nCoV nucleic acid. For their first chest CT images, 6 patients had no lesions, while 9 patients had pulmonary inflammatory lesions. Seven cases had small nodular ground glass opacities and 2 cases had speckled ground glass opacities. After 3 to 5 days of treatment, 2019-nCoV nucleic acid in a second respiratory sample turned negative in 6 cases. Among them, chest CT images showed less lesions in 2 cases, no lesion in 3 cases, and no improvement in 1 case. The remaining 9 cases were still positive in a second nucleic acid test. Six patients showed similar chest CT inflammation, while 3 patients had new lesions, which were all small nodular ground glass opacities. Conclusions: The early chest CT images of children with 2019-nCoV infection are mostly small nodular ground glass opacities. The clinical symptoms of children with 2019-nCoV infection are nonspecific. Dynamic reexamination of chest CT and nucleic acid are important.

[Analysis of CT features of 15 Children with 2019 novel coronavirus infection].

Objective: To explore imaging characteristics of children with 2019 novel coronavirus (2019-nCoV) infection. Methods: A retrospective analysis was performed on clinical data and chest CT images of 15 children diagnosed with 2019-nCoV. They were admitted to the third people's Hospital of Shenzhen from January 16 to February 6, 2020. The distribution and morphology of pulmonary lesions on chest CT images were analyzed. Results: Among the 15 children, there were 5 males and 10 females, aged from 4 to 14 years old. Five of the 15 children were febrile and 10 were asymptomatic on first visit. The first nasal or pharyngeal swab samples in all the 15 cases were positive for 2019-nCoV nucleic acid. For their first chest CT images, 6 patients had no lesions, while 9 patients had pulmonary inflammation lesions. Seven cases of small nodular ground glass opacities and 2 cases of speckled ground glass opacities were found. After 3 to 5 days of treatment, 2019-nCoV nucleic acid in a second respiratory sample turned negative in 6 cases. Among them, chest CT images showed less lesions in 2 cases, no lesion in 3 cases, and no improvement in 1 case. Other 9 cases were still positive in a second nucleic acid test. Six patients showed similar chest CT inflammation, while 3 patients had new lesions, which were all small nodular ground glass opacities. Conclusions: The early chest CT images of children with 2019-nCoV infection are mostly small nodular ground glass opacities. The clinical symptoms of children with 2019-nCoV infection are nonspecific. Dynamic reexamination of chest CT and nucleic acid are important.