ABSTRACT
COVID-19 pandemic has paused many ongoing research projects and unified researchers' attention to focus on COVID-19 related issues. Our project traces 712,294 scientists' publications related to COVID-19 for two years, from January 2020 to December 2021, in order to detect the dynamic evolution patterns of COVID-19 collaboration network over time. By studying the collaboration network of COVID-19 scientists, we observe how a new scientific community has been built in preparation for a sudden shock. The number of newcomers grows incrementally, and the connectivity of the collaboration network shifts from loose to tight promptly. Even though every scientist has an equal opportunity to start a study, collaboration disparity still exists. Following the scale-free distribution, only a few top authors are highly connected with other authors. These top authors are more likely to attract newcomers and work with each other. As the collaboration network evolves, the increase rate in the probability of attracting newcomers for authors with higher degree increases, whereas the increase rates in the probability of forming new links among authors with higher degree decreases. This highlights the interesting trend that COVID pandemic alters the research collaboration trends that star scientists are starting to collaborate more with newcomers, but less with existing collaborators, which, in certain way, reduces the collaboration disparity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
COVID-19 pandemic has paused many ongoing research projects and unified researchers' attention to focus on COVID-19 related issues. Our project traces 712,294 scientists' publications related to COVID-19 for two years, from January 2020 to December 2021, in order to detect the dynamic evolution patterns of COVID-19 collaboration network over time. By studying the collaboration network of COVID-19 scientists, we observe how a new scientific community has been built in preparation for a sudden shock. The number of newcomers grows incrementally, and the connectivity of the collaboration network shifts from loose to tight promptly. Even though every scientist has an equal opportunity to start a study, collaboration disparity still exists. Following the scale-free distribution, only a few top authors are highly connected with other authors. These top authors are more likely to attract newcomers and work with each other. As the collaboration network evolves, the increase rate in the probability of attracting newcomers for authors with higher degree increases, whereas the increase rates in the probability of forming new links among authors with higher degree decreases. This highlights the interesting trend that COVID pandemic alters the research collaboration trends that star scientists are starting to collaborate more with newcomers, but less with existing collaborators, which, in certain way, reduces the collaboration disparity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The research uses the network collaborative construction theory to construct a national scientific cooperation network during the COVID-19 epidemic period, aiming to describe the evolution patterns of scientific research cooperation and the impact on the cooperation during the epidemic period. From the perspective of the cooperation network, the research calculates the network features to investigate the evolution patterns of the scientific research cooperation network. The present work also calculates the national cooperation freshness to investigate the dynamic evolution of the cooperation center. Finally, the regression discontinuity design is used to estimate the impact of the epidemic on national scientific research cooperation. The results show that the outbreak of the epidemic strengthens the scientific research cooperation. Furthermore, China played an important role in the epidemic, especially in the early stage. Although the cooperation center has gradually shifted with the change of the affected areas, China is still an important collaborator. Finally, our work studies the cooperative behavior of countries during the epidemic through regression discontinuity design and finds that the epidemic has been promoting people's ability to cooperate in fighting against disasters. © 2022, Editorial Board of Journal of the University of Electronic Science and Technology of China. All right reserved.
ABSTRACT
It is the cornerstone of precise and scientific prevention and control to understand the temporal evolution and spatial pattern of the COVID-19 epidemic. Based on the county-level COVID-19 case of the United States from January 22, 2020 to October 8, 2021, we explored and analyzed the epidemic by using time series analysis, spatial autocorrelation analysis and gravity center trajectory analysis. The results show that: (1) the epidemic in the United States experienced four stages of low incidence, growth, peak and rebound with June 15, September 30 and October 1, 2020 as the cut-off points. (2) The global Moran index experienced a process of 'increase-decrease-increase-stability', with the maximum value exceeding 0.6, indicating that the epidemic has obvious spatial aggregation;the epidemic is dominated by high-high clusters (over 150 counties) and low-low clusters (over 500 counties), presenting a pattern of 'three cores and multiple islands' and 'north-south belt'. (3) In 60% of states, the trajectory of the epidemic center of gravity is near-linear type. The epidemic hotspots in these states were relatively stable over time. In more than half of the states, the curve of the moving distance of the epidemic center of gravity is exponential. These states experienced a very rapid epidemic. This study is expected to provide a reference for evaluating the effectiveness of epidemic prevention measures and determining targeted epidemic prevention measures, as well as accumulate experience for future research on the spread of different infectious diseases in different regions. © 2022 IEEE.
ABSTRACT
Four GI-1/Massachusetts-type (GI-1/Mass-type) infectious bronchitis virus (IBV) strains were isolated and the complete genomes of these isolates, coupled with the Mass-type live-attenuated vaccine H120 and the Mass-type pathogenic M41 strains, were sequenced in the present study. Our results show that isolates LJL/140820 and I0306/17 may be derived from the Ma5 (another Mass-type live-attenuated vaccine strain) and H120 vaccine strains, respectively. The I1124/16 strain was found to be a M41 variant that likely resulted from nucleotide accumulated mutations in the genome. Consistently, the results of the virus neutralization test showed that isolate I1124/16 was antigenically related but slight different from the M41. Our results from the protection experiments pointed out that chickens immunized with H120 failed to eliminate viral shedding after infection with the isolate I1124/16, which was different from that of M41; this result was consistent to the field observation and further implicated that the variant IBV isolate I1124/16 was antigenic different from the M41 strain. Furthermore, the I1124/16 was found to have comparable but slightly lower pathogenicity with the M41 strain. More studies based on the reverse genetic techniques are needed to elucidate the amino acids in the S1 subunit of spike protein contributing to the altered antigenicity of the isolate I1124/16. In addition, an IBV isolate, LJL/130609, was found to be originated from recombination events between the I1124/16- and Connecticut-like strains. Our results from the virus neutralization test also showed that isolates LJL/130609 and I1124/16 were antigenic closely related. Hence, there are at least 3 different genetic evolution patterns for the circulation of the GI-1/Mass-type IBV field strains in China. The differences of vaccines used, the field conditions and genetic pressures between different flocks, likely account for the emergence, evolution patterns, and characteristics of the Mass-type IBV strains.