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In this study, we present a hybrid agent-based model (ABM) and discrete event simulation (DES) framework where ABM captures the spread dynamics of COVID-19 via asymptomatic passengers and DES captures the impacts of environmental variables, such as service process capacity, on the results of different containment measures in a typical high-speed train station in China. The containment and control measures simulated include as-is (nothing changed) passenger flow control, enforcing social distancing, adherence level in face mask-wearing, and adding capacity to current service stations. These measures are evaluated individually and then jointly under a different initial number of asymptomatic passengers. The results show how some measures can consolidate the outcomes for each other, while combinations of certain measures could compromise the outcomes for one or the other due to unbalanced service process configurations. The hybrid ABM and DES models offer a useful multi-function simulation tool to help inform decision/policy makers of intervention designs and implementations for addressing issues like public health emergencies and emergency evacuations. Challenges still exist for the hybrid model due to the limited availability of simulation platforms, extensive consumption of computing resources, and difficulties in validation and optimisation. © 2023 The Operational Research Society.
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It is necessary to accurately calculate ship carbon emissions for shipping suitability. The state-of-the-art approaches could arguably not be able to estimate ship carbon emissions accurately due to the uncertainties of Ship Technical Specification Database (STSD) and the geographical and temporal breakpoints in Automatic Identification System (AIS) data, hence requiring a new methodology to be developed to address such defects and further improve the accuracy of emission estimation. Firstly, a novel STSD iterative repair model is proposed based on the random forest algorithm by the incorporation of13 ship technical parameters. The repair model is scalable and can substantially improve the quality of STSD. Secondly, a new ship AIS trajectory segmentation algorithm based on ST-DBSCAN is developed, which effectively eliminates the impact of geographical and temporal AIS breakpoints on emission estimation. It can accurately identify the ships' berthing and anchoring trajectories and reasonably segment the trajectories. Finally, based on this proposed framework, the ship carbon dioxide emissions within the scope of domestic emission control areas (DECA) along the coast of China are estimated. The experiment results indicate that the proposed STSD repair model is highly credible due to the significant connections between ship technical parameters. In addition, the emission analysis shows that, within the scope of China's DECA, the berthing period of ships is longer owing to the joint effects of coastal operation features and the strict quarantine measures under the COVID-19 pandemic, which highlights the emissions produced by ship auxiliary engines and boilers. The carbon intensity of most coastal provinces in China is relatively high, reflecting the urgent demand for the transformation and updates of the economic development models. Based on the theoretical models and results, this study recommends a five-stage decarbonization scheme for China's DECA to advance its decarbonization process. © 2022 Elsevier Ltd
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Objective To assess imported risk of COVID-19 in Hainan province from January 10 to March 7 in 2020, and to assess the effect of "The Normalization Prevention and Control" (measures during the Spring Festival Travel Rush (SFTR) in Hainan in 2021. Methods The daily reported imported cases in Hainan province, the daily reported cases in other 30 province of China, and the Baidu Migration Index were collected to calculated into the Imported Risk Index (IRI) to quantitatively assess the imported risk of Hainan province. Based on the analysis of the relationship between the imported risk index and imported cases, an imported case prediction model was constructed to fit the number of imported cases in "emergency containment" stage in Hainan. And number of imported cases during the Spring Festival Travel rush in 2021 was predicted by this model to compared with the actual number, which was to evaluate the "Normalization Prevention and Control" measures in this model was also used to assess the effect of "Normalization Prevention and Control" measures during the SFTR in 2021. Results Totally 112 imported cases were reported in Hainan. The average IRI was 0.98. Haikou, Sanya and Danzhou have the highest imported risk. Except Haikou, the imported risk index of all cities and counties reached the maximum value around January 24th. The generalized additive model based on the lag 4 days and lag 5 days was best fitted with the actual imported cases number (R2adjust1=83.50%, R2adjust2=82.00%, MRE=17.61%). If "Emergency Containment" strategy was still adopted, there were 10 COVID-19 cases imported into Hainan during the SFTR in 2021. Under the "Normalization Prevention and Control" strategy, virtually no imported cases were found in Hainan. Conclusions Tourism cities such as Haikou and Sanya have high imported risks. Hubei and Guangdong provinces are the main imported provinces. The Generalized Additive Model based on the Imported Risk Index can better fit with the imported cases number of COVID-19 in Hainan Province in "emergency containment". Compared with the "Emergency Containment" strategy, the "Normalization Prevention and Control" strategy adopted during the SFTR in 2021 reduced imported cases in Hainan by about 10. © 2022. China Tropical Medicine. All rights reserved.
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It is important for social public security and urban management to explore the spread of infectious diseases. A city-level structured prediction and simulation model for COVID-19 is proposed. This model is consisted of SEIR and social network model on the basis of latest infectious disease dynamics theory and real geographic networks. The prediction region is divided into multiple levels. Specifically, a bipartite network is applied to simulate the relationship between public facilities and community nodes at the macro level, and a modified SEIR is applied to simulate the infection within nodes at the micro level. Besides, intelligent agent is applied to track the individual transmission process. The contrast experimental results based on the confirmed and cursed cases of Wuhan and Beijing in 2020 published by National Health Commission, show that the proposed model has better flexibility and higher accuracy, and reflects the distribution and movement of people more directly. © 2022 Institute of Computing Technology. All rights reserved.
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With the increase in inevitable large-scale crowd aggregation, disastrous pedestrian stampedes occurred with increasing frequency over the past decade. To prevent these tragedies, it is significant to assess crowd accident-risk (CAR) and identify high-risk areas to control crowd flow dynamically. The cost function of a conventional fluid dynamics model is improved with new items of Gaussian white noise and protection factor, considering both the abnormal pedestrian movements and social distance control due to epidemic, thereby to establish an improved crowd flow model comprehensively. Different from conventional density-based pedestrian aggregation-risk models, this study proposes a hybrid crowd accident-risk assessment (HCRA) model based on internal energy and information entropy. Using the HCRA model, we can consider not only crowd density but also the modulus and direction of a crowd velocity vector simultaneously. Then this study designs a framework to realize crowd accident risk assessment based on the improved crowd-flow model and HCRA model. To validate the proposed models, case studies of CAR assessment in the large-scale waiting hall of the Shanghai Hongqiao railway station are conducted. The pedestrian social control distance-range of 1.0 m-2.0 m under the COVID-19 epidemic situation is verified numerically. Moreover, a valuable result is that this social control distance-range can be shortened to 1.0 m-1.9 m without increase of crow accident-risk. Subsequently, the down-limit of accommodation-capacity of this large waiting hall can be enhanced to 10.54%under this epidemic. IEEE
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This paper aims to analyze the impact of COVID-19 on the sustainability of the banking sector and the fintech sector. In China, where banks’ revenue mainly comes from branches, we collect relevant data manually and use the OLS model for empirical analysis. The results show that as the COVID-19 infection rate increases, the number of bank branches decreases significantly, which threatens the banking sector’s sustainability. The fintech sector acts as a competitor to the banking sector. With the increase of COVID-19 infection rate, the public pays more attention to fintech, promoting the development of the fintech sector. Moreover, the impact of COVID-19 on these two sectors will diminish over time. In addition, this paper finds that COVID-19 further weakens the number of bank branches during the epidemic through the mediating effect of fintech. The findings of this paper help to assess the sustainability of the different financial sectors during the epidemic, which is essential for financial stability. Copyright © 2022 Yan and Jia.
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It is essential to unravel the spatial and temporal patterns of the spread of the epidemic in China during the backdrop of the global coronavirus disease 2019 (COVID-19) outbreak in 2020, as the underlying drivers are crucial for scientific formulation of epidemy-preventing strategies. A discriminant model for the spatio-temporal pattern of epidemic spread was developed for 317 prefecture-level cities using accumulated data on confirmed cases. The model was introduced for the real-time evolution of the outbreak starting from the rapid spread of COVID-19 on January 24, 2020, until the control on March 18, 2020. The model was used to analyze the basic characteristics of the spatio-temporal patterns of the epidemic spread by combining parameters such as peak position, full width at half maximum, kurtosis, and skewness. A multivariate logistic regression model was developed to unravel the key drivers of the spatio-temporal patterns based on traffic accessibility, urban connectivity, and population flow. The results of the study are as follows. (1) The straight-line distance of 588 km from Wuhan was used as the effective boundary to identify the four spatial patterns of epidemic spread, and 13 types of spatio-temporal patterns were obtained by combining the time-course categories of the same spatial pattern. (2) The spread of the epidemic was relatively severe in the leapfrogging model. Besides the short-distance leapfrogging model, significant differences emerged in the spatial patterns of the time course of epidemic spread. The peaks of the new confirmed cases in various spatio-temporal patterns were mostly observed on February 3, 2020. The average full widths at the half maximum of all ordinary cities were approximately 14 days, thus, resonating with the incubation period of the COVID-19 virus. (3) The degree of the population correlation with Wuhan city has mainly influenced the spreading and the short-distance leapfrogging spatial patterns. The existence of direct flight from Wuhan city exhibited a positive effect on the long-distance leapfrogging spatial pattern. The number of population outflows has significantly affected the leapfrogging spatial pattern. The integrated spatial pattern was influenced by both primary and secondary epidemic outbreak sites. Thus, cities should pay great attention to traffic control during the epidemic as analysis has shown that the spatio-temporal patterns of epidemic spread in the respective cities can curb the spread of the epidemic from key links. © 2022, Science Press. All right reserved.
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Pedestrian merging flow in the crowd gathering public places are the common movement nodes of crowd kinematics merging and psychological panic transmission. There are stochastic turbulences, disturbances and density fluctuations in the crowd merging area, with high risk of pedestrian stampede events. Based on the dynamics model of crowd merging, this study considers the psychological characteristics of the escape panic in the normal disaster conditions of the crowd in the cross-passages and the epidemic panic psychological characteristics under the public health events. With the introduction of Shanoon's information entropy theory, panic entropy is applied to measure the degrees of transient panic in the fluid grid area of the crowd, the overall transient disorder of the crowd, and the dynamic relationship with time and space changes. This study comprehensively considers the characteristics of conventional escape panic and epidemic panic, defines protective relaxation factors, forms a dynamic model of escape panic propagation, and provides a scientific theoretical basis for crowd evacuation guidance under COVID-19 epidemic situation. © 2021 IEEE.
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BACKGROUND: Cancer patients are regarded as a highly vulnerable group in the current Coronavirus Disease 2019 (COVID-19) pandemic. To date, the clinical characteristics of COVID-19-infected cancer patients remain largely unknown. PATIENTS AND METHODS: In this retrospective cohort study, we included cancer patients with laboratory-confirmed COVID-19 from three designated hospitals in Wuhan, China. Clinical data were collected from medical records from 13 January 2020 to 26 February 2020. Univariate and multivariate analyses were carried out to assess the risk factors associated with severe events defined as a condition requiring admission to an intensive care unit, the use of mechanical ventilation, or death. RESULTS: A total of 28 COVID-19-infected cancer patients were included; 17 (60.7%) patients were male. Median (interquartile range) age was 65.0 (56.0-70.0) years. Lung cancer was the most frequent cancer type (n = 7; 25.0%). Eight (28.6%) patients were suspected to have hospital-associated transmission. The following clinical features were shown in our cohort: fever (n = 23, 82.1%), dry cough (n = 22, 81%), and dyspnoea (n = 14, 50.0%), along with lymphopaenia (n = 23, 82.1%), high level of high-sensitivity C-reactive protein (n = 23, 82.1%), anaemia (n = 21, 75.0%), and hypoproteinaemia (n = 25, 89.3%). The common chest computed tomography (CT) findings were ground-glass opacity (n = 21, 75.0%) and patchy consolidation (n = 13, 46.3%). A total of 15 (53.6%) patients had severe events and the mortality rate was 28.6%. If the last antitumour treatment was within 14 days, it significantly increased the risk of developing severe events [hazard ratio (HR) = 4.079, 95% confidence interval (CI) 1.086-15.322, P = 0.037]. Furthermore, patchy consolidation on CT on admission was associated with a higher risk of developing severe events (HR = 5.438, 95% CI 1.498-19.748, P = 0.010). CONCLUSIONS: Cancer patients show deteriorating conditions and poor outcomes from the COVID-19 infection. It is recommended that cancer patients receiving antitumour treatments should have vigorous screening for COVID-19 infection and should avoid treatments causing immunosuppression or have their dosages decreased in case of COVID-19 coinfection.