Coxsackievirus A6 (CV-A6) and Coxsackievirus A10 (CV-A10) have been emerging as the prevailing serotypes and overtaking Enterovirus A71 (EV-A71) and Coxsackievirus A16 (CV-A16) in most areas as main pathogens of hand, foot and mouth disease (HFMD) in China since 2013. To investigate whole etiological spectrum following EV-A71 vaccination of approximate 40,000 infants and young children in Xiangyang, enteroviruses were serotyped in 4415 HFMD cases from October 2016 to December 2017 using Real Time and conventional PCR and cell cultures. Of the typeable 3201 specimen, CV-A6 was the predominant serotype followed by CV-A16, CV-A10, CV-A5, CV-A2 and EV-A71 with proportions of 59.54%, 15.31%, 11.56%, 4.56%, 3.78% and 3.03%, respectively. Other 12 minor serotypes were also detected. The results demonstrated that six major serotypes of enteroviruses were co-circulating, including newly emerged CV-A2 and CV-A5. A dramatic decrease of EV-A71 cases was observed, whereas the total cases remained high. Multivalent vaccines against major serotypes are urgently needed for control of HFMD.
A social event is an occurrence that involves lots of people and is accompanied by an obvious rise in human flow. Analysis of social events has real-world importance because events bring about impacts on many aspects of city life. Traditionally, detection and impact measurement of social events rely on social investigation, which involves considerable human effort. Recently, by analyzing messages in social networks, researchers can also detect and evaluate country-scale events. Nevertheless, the analysis of city-scale events has not been explored. In this article, we use human flow dynamics, which reflect the social activeness of a region, to detect social events and measure their impacts. We first extract human flow dynamics from taxi traces. Second, we propose a method that can not only discover the happening time and venue of events from abnormal social activeness, but also measure the scale of events through changes in such activeness. Third, we extract traffic congestion information from traces and use its change during social events to measure their impact. The results of experiments validate the effectiveness of both the event detection and impact measurement methods.
Background
Evaluation of a licensed inactivated EV71 vaccine is needed in a phase IV study with a large population to identify its effectiveness and safety for further application.
Methods
An open-label and controlled trial involving a large population of 155,995 children aged 6-71 months is performed; 40,724 were enrolled in the vaccine group and received 2 doses of inactivated EV71 vaccine at an interval of 1 month, and the remaining children were used as the control group. The EV71-infected hand, foot and mouth disease (HFMD) cases were monitored in the vaccine and control groups during a follow-up period of 14 months since the 28th days post inoculation through the local database of Notifiable Infectious Diseases Network. The effectiveness of the vaccine was estimated by comparing the incidence density in the vaccine group versus that in the control group based upon EV71 infected patients identified via laboratory testing. In parallel, the active and passive surveillance for safety of the vaccine was conducted by home or telephone visits and by using the Adverse Event Following Immunization (AEFI) system, respectively.
Results
An overall level of 89.7% (95% confidence interval [CI], 24.0 to 98.6) vaccine effectiveness (VE) against EV71 infection and a 4.58% rate of reported AEs were observed. Passive surveillance demonstrated a 0.31% rate of reported common minor reactions.
Conclusions
The clinical protection and safety of the EV71 vaccine were demonstrated in the immunization of a large population.
Clinical trials registration
NCT03001986.
Complex networks describe a wide range of systems in nature and society. To understand complex networks, it is crucial to investigate their community structure. In this paper, we develop an online community detection algorithm with linear time complexity for large complex networks. Our algorithm processes a network edge by edge in the order that the network is fed to the algorithm. If a new edge is added, it just updates the existing community structure in constant time, and does not need to re-compute the whole network. Therefore, it can efficiently process large networks in real time. Our algorithm optimizes expected modularity instead of modularity at each step to avoid poor performance. The experiments are carried out using 11 public data sets, and are measured by two criteria, modularity and NMI (Normalized Mutual Information). The results show that our algorithm's running time is less than the commonly used Louvain algorithm while it gives competitive performance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.