Corpus overview


Overview

MeSH Disease

HGNC Genes

SARS-CoV-2 proteins

ProteinS (56)

ProteinN (21)

NSP5 (11)

ComplexRdRp (9)

ProteinE (6)


Filter

Genes
Diseases
SARS-CoV-2 Proteins
    displaying 1341 - 1346 records in total 1346
    records per page




    Epidemiological identification of a novel infectious disease in real time: Analysis of the atypical pneumonia outbreak in Wuhan, China, 2019-20

    Authors: Sung-mok Jung; Ryo Kinoshita; Robin N. Thompson; Katsuma Hayashi; Natalie M. Linton; Yichi Yang; Andrei R. Akhmetzhanov; Hiroshi Nishiura

    doi:10.1101/2020.01.26.20018887 Date: 2020-01-28 Source: medRxiv

    Objective: Virological tests indicate that a novel coronavirus is the most likely explanation for the 2019-20 pneumonia MESHD outbreak in Wuhan, China. We demonstrate that non-virological descriptive characteristics could have determined that the outbreak is caused by a novel pathogen in advance of virological testing. Methods: Characteristics of the ongoing outbreak were collected in real time from two medical social media sites. These were compared against characteristics of ten existing pathogens that can induce atypical pneumonia MESHD. The probability that the current outbreak is due to "Disease MESHD X" (i.e., previously unknown etiology) as opposed to one of the known pathogens was inferred, and this estimate was updated as the outbreak continued. Results: The probability that Disease X is driving the outbreak was assessed as over 32% on 31 December 2019, one week before virus identification. After some specific pathogens were ruled out by laboratory tests on 5 Jan 2020, the inferred probability of Disease X was over 59%. Conclusions: We showed quantitatively that the emerging outbreak of atypical pneumonia MESHD cases is consistent with causation by a novel pathogen. The proposed approach, that uses only routinely-observed non-virological data, can aid ongoing risk assessments even before virological test results become available. Keywords: Epidemic; Causation; Bayes' theorem; Diagnosis; Prediction; Statistical model

    2019-20 Wuhan coronavirus outbreak: Intense surveillance is vital for preventing sustained transmission in new locations

    Authors: Robin Nicholas Thompson

    doi:10.1101/2020.01.24.919159 Date: 2020-01-25 Source: bioRxiv

    The outbreak of pneumonia MESHD originating in Wuhan, China, has generated 830 confirmed cases, including 26 deaths MESHD, as of 24 January 2020. The virus (2019-nCoV) has spread elsewhere in China and to other countries, including South Korea, Thailand, Japan and USA. Fortunately, there has not yet been evidence of sustained human-to-human transmission outside of China. Here we assess the risk of sustained transmission whenever the coronavirus arrives in other countries. Data describing the times from symptom onset to hospitalisation for 47 patients infected in the current outbreak are used to generate an estimate for the probability that an imported case is followed by sustained human-to-human transmission. Under the assumptions that the imported case is representative of the patients in China, and that the 2019-nCoV is similarly transmissible to the SARS coronavirus, the probability that an imported case is followed by sustained human-to-human transmission is 0.37. However, if the mean time from symptom onset to hospitalisation can be halved by intense surveillance, then the probability that an imported case leads to sustained transmission is only 0.005. This emphasises the importance of current surveillance efforts in countries around the world, to ensure that the ongoing outbreak will not become a large global epidemic.

    Pattern of early human-to-human transmission of Wuhan 2019-nCoV

    Authors: Julien Riou; Christian L Althaus

    doi:10.1101/2020.01.23.917351 Date: 2020-01-24 Source: bioRxiv

    On December 31, 2019, the World Health Organization was notified about a cluster of pneumonia MESHD of unknown aetiology in the city of Wuhan, China. Chinese authorities later identified a new coronavirus (2019-nCoV) as the causative agent of the outbreak. As of January 23, 2020, 655 cases have been confirmed in China and several other countries. Understanding the transmission characteristics and the potential for sustained human-to-human transmission of 2019-nCoV is critically important for coordinating current screening and containment strategies, and determining whether the outbreak constitutes a public health emergency of international concern (PHEIC). We performed stochastic simulations of early outbreak trajectories that are consistent with the epidemiological findings to date. We found the basic reproduction number, R0, to be around 2.2 (90% high density interval 1.4--3.8), indicating the potential for sustained human-to-human transmission. Transmission characteristics appear to be of a similar magnitude to severe acute respiratory syndrome MESHD-related coronavirus ( SARS-CoV MESHD) and the 1918 pandemic influenza. These findings underline the importance of heightened screening, surveillance and control efforts, particularly at airports and other travel hubs, in order to prevent further international spread of 2019-nCoV.

    Host and infectivity prediction of Wuhan 2019 novel coronavirus using deep learning algorithm

    Authors: Qian Guo; Mo Li; Chunhui Wang; Zhengcheng Fang; Peihong Wang; Jie Tan; Shufang Wu; Yonghong Xiao; Huaiqiu Zhu

    doi:10.1101/2020.01.21.914044 Date: 2020-01-24 Source: bioRxiv

    The recent outbreak of pneumonia MESHD in Wuhan, China caused by the 2019 Novel Coronavirus (2019-nCoV) emphasizes the importance of detecting novel viruses and predicting their risks of infecting people. In this report, we introduced the VHP (Virus Host Prediction) to predict the potential hosts of viruses using deep learning algorithm. Our prediction suggests that 2019-nCoV has close infectivity with other human coronaviruses, especially the severe acute respiratory syndrome coronavirus (SARS-CoV) MESHD, Bat SARS-like Coronaviruses and the Middle East respiratory syndrome coronavirus (MERS-CoV) MESHD. Based on our prediction, compared to the Coronaviruses infecting other vertebrates, bat coronaviruses are assigned with more similar infectivity patterns with 2019-nCoVs. Furthermore, by comparing the infectivity patterns of all viruses hosted on vertebrates, we found mink viruses show a closer infectivity pattern to 2019-nCov. These consequences of infectivity pattern analysis illustrate that bat and mink may be two candidate reservoirs of 2019-nCov.These results warn us to beware of 2019-nCoV and guide us to further explore the properties and reservoir of it. One Sentence SummaryIt is of great value to identify whether a newly discovered virus has the risk of infecting human. Guo et al. proposed a virus host prediction method based on deep learning to detect what kind of host a virus can infect with DNA sequence as input. Applied to the Wuhan 2019 Novel Coronavirus, our prediction demonstrated that several vertebrate-infectious coronaviruses have strong potential to infect human. This method will be helpful in future viral analysis and early prevention and control of viral pathogens.

    Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak

    Authors: Shi Zhao; Qianying Lin; Jinjun Ran; Salihu Sabiu MUSA; Guangpu Yang; Weiming Wang; Yijun Lou; Daozhou Gao; Lin Yang; Daihai He; Maggie H Wang

    doi:10.1101/2020.01.23.916395 Date: 2020-01-24 Source: bioRxiv

    BackgroundsAn ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia MESHD hit a major city of China, Wuhan, December 2019 and subsequently reached other provinces/regions of China and countries. We present estimates of the basic reproduction number, R0, of 2019-nCoV in the early phase of the outbreak. MethodsAccounting for the impact of the variations in disease reporting rate, we modelled the epidemic curve of 2019-nCoV cases time series, in mainland China from January 10 to January 24, 2020, through the exponential growth. With the estimated intrinsic growth rate ({gamma}), we estimated R0 by using the serial intervals (SI) of two other well-known coronavirus diseases, MERS and SARS, as approximations for the true unknown SI. FindingsThe early outbreak data largely follows the exponential growth. We estimated that the mean R0 ranges from 2.24 (95%CI: 1.96-2.55) to 3.58 (95%CI: 2.89-4.39) associated with 8-fold to 2-fold increase in the reporting rate. We demonstrated that changes in reporting rate substantially affect estimates of R0. ConclusionThe mean estimate of R0 for the 2019-nCoV ranges from 2.24 to 3.58, and significantly larger than 1. Our findings indicate the potential of 2019-nCoV to cause outbreaks.

    A mathematical model for simulating the transmission of Wuhan novel Coronavirus

    Authors: Tianmu Chen; Jia Rui; Qiupeng Wang; Zeyu Zhao; Jing-An Cui; Ling Yin

    doi:10.1101/2020.01.19.911669 Date: 2020-01-19 Source: bioRxiv

    As reported by the World Health Organization, a novel coronavirus (2019-nCoV) was identified as the causative virus of Wuhan pneumonia MESHD of unknown etiology by Chinese authorities on 7 January, 2020. In this study, we developed a Bats-Hosts-Reservoir-People transmission network model for simulating the potential transmission from the infection source (probable be bats) to the human infection. Since the Bats-Hosts-Reservoir network was hard to explore clearly and public concerns were focusing on the transmission from a seafood market (reservoir) to people, we simplified the model as Reservoir-People transmission network model. The basic reproduction number (R0) was calculated from the RP MESHD model to assess the transmissibility of the 2019-nCoV.

The ZB MED preprint Viewer preVIEW includes all COVID-19 related preprints from medRxiv and bioRxiv, from ChemRxiv, from ResearchSquare, from arXiv and from Preprints.org and is updated on a daily basis (7am CET/CEST).
The web page can also be accessed via API.

Sources


Annotations

All
None
MeSH Disease
HGNC Genes
SARS-CoV-2 Proteins


Export subcorpus as...

This service is developed in the project nfdi4health task force covid-19 which is a part of nfdi4health.

nfdi4health is one of the funded consortia of the National Research Data Infrastructure programme of the DFG.