Qiang Sun’s research while affiliated with University of International Business and Economics and other places

As the economic environment becomes increasingly complex, enhancing supply chain resilience is crucial for the operations and long-term development of enterprises. Real-world supply chains, encompassing components such as goods, warehouses, and plants, often contain complex network structures, making resilience analysis a challenging task. This paper addresses this challenge from a network analysis perspective. We project the complex supply chain network into single-mode, multi-layer networks focusing on plants and warehouses. Utilizing a multi-layer community detection method, we identify local clusters within these networks. By uncovering closely connected clusters, we reveal the flexibility and redundancy in production capabilities among different plants and warehouses. An empirical study using real-world data demonstrates that multi-layer network clustering effectively uncovers indirect capacity linkages between plants and warehouses. The findings from this community detection are beneficial for strategic capacity management, aiding enterprises in managing supply shortages or sudden demand spikes.

The effects of temperature and relative humidity on the growth of coronavirus disease 2019 (COVID-19) remain unclear. Data on the COVID-19 epidemic that were analyzed in this study were obtained from the official websites of the National Health Commission of China and the Health Commissions of 31 provinces in China. From January 26 to February 25, 2020, the cumulative number of confirmed COVID-19 cases in each region was counted daily using data from our database. Curve fitting of daily scatter plots of the relationship between epidemic growth rate (GR) with average temperature (AT) and average relative humidity (ARH) was conducted using the loess method. The heterogeneity across days and provinces was calculated to assess the necessity of using a longitudinal model. Fixed-effect models with polynomial terms were developed to quantify the relationship between variations in the GR and AT or ARH. An increased AT markedly reduced the GR when the AT was lower than -5°C, the GR was moderately reduced when the AT ranged from -5°C to 15°C, and the GR increased when the AT exceeded 15°C. ARH increased the GR when it was less than 72% and reduced the GR when it exceeded 72%. The temperature and relative humidity curves were not linearly associated with the GR of COVID-19. The GR was moderately reduced when the AT ranged from -5°C to 15°C. When the AT was lower or higher than -5°C to 15°C, the GR of COVID-19 increased. An increased ARH increased the GR when the ARH was lower than 72% and reduced the GR when the ARH exceeded 72%.

Background Since the outbreak of COVID-19 in December 2019 in Wuhan, Hubei Province, China, frequent interregional contacts and the high rate of infection spread have catalyzed the formation of an epidemic network. Objective The aim of this study was to identify influential nodes and highlight the hidden structural properties of the COVID-19 epidemic network, which we believe is central to prevention and control of the epidemic. Methods We first constructed a network of the COVID-19 epidemic among 31 provinces in mainland China; after some basic characteristics were revealed by the degree distribution, the k-core decomposition method was employed to provide static and dynamic evidence to determine the influential nodes and hierarchical structure. We then exhibited the influence power of the above nodes and the evolution of this power. Results Only a small fraction of the provinces studied showed relatively strong outward or inward epidemic transmission effects. The three provinces of Hubei, Beijing, and Guangzhou showed the highest out-degrees, and the three highest in-degrees were observed for the provinces of Beijing, Henan, and Liaoning. In terms of the hierarchical structure of the COVID-19 epidemic network over the whole period, more than half of the 31 provinces were located in the innermost core. Considering the correlation of the characteristics and coreness of each province, we identified some significant negative and positive factors. Specific to the dynamic transmission process of the COVID-19 epidemic, three provinces of Anhui, Beijing, and Guangdong always showed the highest coreness from the third to the sixth week; meanwhile, Hubei Province maintained the highest coreness until the fifth week and then suddenly dropped to the lowest in the sixth week. We also found that the out-strengths of the innermost nodes were greater than their in-strengths before January 27, 2020, at which point a reversal occurred. Conclusions Increasing our understanding of how epidemic networks form and function may help reduce the damaging effects of COVID-19 in China as well as in other countries and territories worldwide.

UNSTRUCTURED Frequent interregional contacts and the high rate of infection spread catalyzed the formation of 2019-nCoV epidemic network. Identifying influential nodes and highlighting the hidden structural properties of the network is central for epidemic prevention and control. In this paper, we first construct the 2019-nCoV epidemic network among provinces in mainland China, after using the degree distribution to reveal some basic characteristics, the k-core decomposition method is employed to provide some static and dynamic evidence of figuring out the influential nodes and hierarchical structure, and then we exhibit the influence power of the above nodes and its evolution. Results yield unexpected information on which are influential nodes and how important they are, as well as their geographic distribution and dynamic modes. Such a better understanding of how epidemic network form and function may help reduce the damaging effects of 2019-nCoV.

Background: The effects of temperature and humidity on the epidemic growth of coronavirus disease 2019 (COVID-19)remains unclear. Methods: Daily scatter plots between the epidemic growth rate (GR) and average temperature (AT) or average relative humidity (ARH) were presented with curve fitting through the “loess” method. The heterogeneity across days and provinces were calculated to assess the necessity of using a longitudinal model. Fixed effect models with polynomial terms were developed to quantify the relationship between variations in the GR and AT or ARH. Results: An increased AT dramatically reduced the GR when the AT was lower than −5°C, the GR was moderately reduced when the AT ranged from −5°C to 15°C, and the GR increased when the AT exceeded 15°C. An increasedARH increased theGR when the ARH was lower than 72% and reduced theGR when the ARH exceeded 72%. Conclusions: High temperatures and low humidity may reduce the GR of the COVID-19 epidemic. The temperature and humidity curves were not linearly associated with the COVID-19 GR.

Predicting the number of new suspected or confirmed cases of novel coronavirus disease 2019 (COVID-19) is crucial in the prevention and control of the COVID-19 outbreak. Social media search indexes (SMSI) for dry cough, fever, chest distress, coronavirus, and pneumonia were collected from 31 December 2019 to 9 February 2020. The new suspected cases of COVID-19 data were collected from 20 January 2020 to 9 February 2020. We used the lagged series of SMSI to predict new suspected COVID-19 case numbers during this period. To avoid overfitting, five methods, namely subset selection, forward selection, lasso regression, ridge regression, and elastic net, were used to estimate coefficients. We selected the optimal method to predict new suspected COVID-19 case numbers from 20 January 2020 to 9 February 2020. We further validated the optimal method for new confirmed cases of COVID-19 from 31 December 2019 to 17 February 2020. The new suspected COVID-19 case numbers correlated significantly with the lagged series of SMSI. SMSI could be detected 6–9 days earlier than new suspected cases of COVID-19. The optimal method was the subset selection method, which had the lowest estimation error and a moderate number of predictors. The subset selection method also significantly correlated with the new confirmed COVID-19 cases after validation. SMSI findings on lag day 10 were significantly correlated with new confirmed COVID-19 cases. SMSI could be a significant predictor of the number of COVID-19 infections. SMSI could be an effective early predictor, which would enable governments’ health departments to locate potential and high-risk outbreak areas.