Dataset Information

Distribution and characteristics of rodent picornaviruses in China.

ABSTRACT: Rodents are important reservoir hosts of many important zoonotic viruses. The family Picornaviridae contains clinically important pathogens that infect humans and animals, and increasing numbers of rodent picornaviruses have recently been associated with zoonoses. We collected 574 pharyngeal and anal swab specimens from 287 rodents of 10 different species from eight representative regions of China from October 2013 to July 2015. Seven representative sequences identified from six rodent species were amplified as full genomes and classified into four lineages. Three lineage 1 viruses belonged to a novel genus of picornaviruses and was more closely related to Hepatovirus than to others genera of picornaviruses based on aa homology. Lineage 2, lineage 3, and lineage 4 viruses belonged to the genera Rosavirus, Hunnivirus, and Enterovirus, respectively, representing new species. According to both phylogenetic and identity analyses, Lineage 2 viruses had a close relationship with rosavirus 2 which was recovered from the feces of a child in Gambia and Lineage 3 viruses had a close relationship with domestic animal Hunnivirus. Lineage 4 viruses provide the first evidence of these enteroviruses and their evolution in rodent hosts in China.

SUBMITTER: Du J

PROVIDER: S-EPMC5041129 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Distribution and characteristics of rodent picornaviruses in China.

Du Jiang J Lu Liang L Liu Feng F Su Haoxiang H Dong Jie J Sun Lilian L Zhu Yafang Y Ren Xianwen X Yang Fan F Guo Fei F Liu Qiyong Q Wu Zhiqiang Z Jin Qi Q

Scientific reports 20160929

Rodents are important reservoir hosts of many important zoonotic viruses. The family Picornaviridae contains clinically important pathogens that infect humans and animals, and increasing numbers of rodent picornaviruses have recently been associated with zoonoses. We collected 574 pharyngeal and anal swab specimens from 287 rodents of 10 different species from eight representative regions of China from October 2013 to July 2015. Seven representative sequences identified from six rodent species w ...[more]

PMID: 27682620

Similar Datasets

Project description:BackgroundEchinococcosis is a zoonotic parasitic disease caused by larval stages of cestodes belonging to the genus Echinococcus. The infection affects people's health and safety as well as agropastoral sector. In China, human echinococcosis is a major public health burden, especially in western China. Echinococcosis affects people health as well as agricultural and pastoral economy. Therefore, it is important to understand the prevalence status and spatial distribution of human echinococcosis in order to advance our knowledge of basic information for prevention and control measures reinforcement.MethodsReport data on echinococcosis were collected in 370 counties in China in 2018 and were used to assess prevalence and spatial distribution. SPSS 21.0 was used to obtain the prevalence rate for CE and AE. For statistical analyses and mapping, all data were processed using SPSS 21.0 and ArcGIS 10.4, respectively. Chi-square test and Exact probability method were used to assess spatial autocorrelation and spatial clustering.ResultsA total of 47,278 cases of echinococcosis were recorded in 2018 in 370 endemic counties in China. The prevalence rate of human echinococcosis was 10.57 per 10,000. Analysis of the disease prevalence showed obvious spatial positive autocorrelation in globle spatial autocorrelation with two aggregation modes in local spatial autocorrelation, namely high-high and low-high aggregation areas. The high-high gathering areas were mainly concentrated in northern Tibet, western Qinghai, and Ganzi in the Tibetan Autonomous Region and in Sichuan. The low-high clusters were concentrated in Gamba, Kangma and Yadong counties of Tibet. In addition, spatial scanning analysis revealed two spatial clusters. One type of spatial clusters included 71 counties in Tibet Autonomous Region, 22 counties in Qinghai, 11 counties in Sichuan, three counties in Xinjiang Uygur Autonomous Region, two counties in Yunnan, and one county in Gansu. In the second category, six types of spatial clusters were observed in the counties of Xinjiang Uygur Autonomous Region, and the Qinghai, Gansu, and Sichuan Provinces.ConclusionThis study showed a serious prevalence of human echinococcosis with obvious spatial aggregation of the disease prevalence in China. The Qinghai-Tibet Plateau is the "hot spot" area of human echinococcosis in China. Findings from this study indicate that there is an urgent need of joint strategies to strengthen efforts for the prevention and control of echinococcosis in China, especially in the Qinghai-Tibet Plateau.

Project description:BackgroundThe coronavirus disease 2019 (COVID-19) has become a pandemic. Few studies have been conducted to investigate the spatio-temporal distribution of COVID-19 on nationwide city-level in China.ObjectiveTo analyze and visualize the spatiotemporal distribution characteristics and clustering pattern of COVID-19 cases from 362 cities of 31 provinces, municipalities and autonomous regions in mainland China.MethodsA spatiotemporal statistical analysis of COVID-19 cases was carried out by collecting the confirmed COVID-19 cases in mainland China from January 10, 2020 to October 5, 2020. Methods including statistical charts, hotspot analysis, spatial autocorrelation, and Poisson space-time scan statistic were conducted.ResultsThe high incidence stage of China's COVID-19 epidemic was from January 17 to February 9, 2020 with daily increase rate greater than 7.5%. The hot spot analysis suggested that the cities including Wuhan, Huangshi, Ezhou, Xiaogan, Jingzhou, Huanggang, Xianning, and Xiantao, were the hot spots with statistical significance. Spatial autocorrelation analysis indicated a moderately correlated pattern of spatial clustering of COVID-19 cases across China in the early phase, with Moran's I statistic reaching maximum value on January 31, at 0.235 (Z = 12.344, P = 0.001), but the spatial correlation gradually decreased later and showed a discrete trend to a random distribution. Considering both space and time, 19 statistically significant clusters were identified. 63.16% of the clusters occurred from January to February. Larger clusters were located in central and southern China. The most likely cluster (RR = 845.01, P < 0.01) included 6 cities in Hubei province with Wuhan as the centre. Overall, the clusters with larger coverage were in the early stage of the epidemic, while it changed to only gather in a specific city in the later period. The pattern and scope of clusters changed and reduced over time in China.ConclusionsSpatio-temporal cluster detection plays a vital role in the exploration of epidemic evolution and early warning of disease outbreaks and recurrences. This study can provide scientific reference for the allocation of medical resources and monitoring potential rebound of the COVID-19 epidemic in China.

Dataset Information

Distribution and characteristics of rodent picornaviruses in China.

Publications

Distribution and characteristics of rodent picornaviruses in China.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets