No abstract

No abstract

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.

No abstract

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.