Corpus overview


Overview

MeSH Disease

HGNC Genes

There are no HGNC terms in the subcorpus


SARS-CoV-2 proteins

There are no SARS-CoV-2 protein terms in the subcorpus


Filter

Genes
Diseases
SARS-CoV-2 Proteins
    displaying 1 - 2 records in total 2
    records per page




    Prevalence Estimation from Random Samples and Census Data with Participation Bias MESHD

    Authors: Stéphane Guerrier; Christoph Kuzmics; Maria-Pia Victoria-Feser

    id:2012.10745v2 Date: 2020-12-19 Source: arXiv

    Countries officially record the number of COVID-19 MESHD cases based on medical tests of a subset of the population with unknown participation bias MESHD. For prevalence estimation, the official information is typically discarded and, instead, small random survey samples are taken. We derive (maximum likelihood and method of moment) prevalence estimators, based on a survey sample, that additionally utilize the official information, and that are substantially more accurate than the simple sample proportion of positive cases. Put differently, using our estimators, the same level of precision can be obtained with substantially smaller survey samples. We take into account the possibility of measurement errors due to the sensitivity and specificity of the medical testing procedure. The proposed estimators and associated confidence intervals are implemented in the companion open source R package cape.

    Surveillance by age-class and prefecture for emerging infectious febrile diseases with respiratory symptoms, including COVID-19 MESHD

    Authors: Tomoaki Ueno; Junko Kurita; Tamie Sugawara; Yoshiyuki Sugishita; Yasushi Ohkusa; Hirokazu Kawanohara; Miwako Kamei

    doi:10.1101/2020.04.11.20061697 Date: 2020-04-15 Source: medRxiv

    Object The COVID-19 MESHD outbreak emerged in late 2019 in China, expanding rapidly thereafter. Even in Japan, epidemiological linkage of transmission was probably lost already by February 18, 2020. From that time, it has been necessary to detect clusters using syndromic surveillance. Method We identified common symptoms of COVID-19 MESHD as fever MESHD and respiratory symptoms. Therefore, we constructed a model to predict the number of patients with antipyretic analgesics (AP) and multi-ingredient cold medications (MIC) controlling well-known pediatric infectious diseases including influenza or RS virus infection MESHD. To do so, we used the National Official Sentinel Surveillance for Infectious Diseases MESHD (NOSSID), even though NOSSID data are weekly data with 10 day delays, on average. The probability of a cluster with unknown febrile disease MESHD with respiratory symptoms MESHD is a product of the probabilities of aberrations in AP and MIC, which is defined as one minus the probability of the number of patients prescribed a certain type of drug in PS compared to the number predicted using a model. This analysis was conducted prospectively in 2020 using data from October 1, 2010 through 2019 by prefecture and by age-class. Results The probability of unknown febrile disease MESHD with respiratory symptom cluster was estimated as less than 60% in 2020. Discussion The most severe limitation of the present study is that the proposed model cannot be validated. A large outbreak of an unknown febrile disease MESHD with respiratory symptoms MESHD must be experienced, at which time, practitioners will have to wing it. We expect that no actual cluster of unknown febrile disease MESHD with respiratory symptoms MESHD will occur, but if it should occur, we hope to detect it.

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.