Corpus overview


Overview

MeSH Disease

HGNC Genes

destrin (1)


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




    Generalized k-Means in GLMs with Applications to the Outbreak of COVID-19 MESHD in the United States

    Authors: Tonglin Zhang; Ge Lin

    id:2008.03838v1 Date: 2020-08-09 Source: arXiv

    Generalized $k$-means can be incorporated with any similarity or dissimilarity measure for clustering. By choosing the dissimilarity measure as the well known likelihood ratio or $F$-statistic, this work proposes a method based on generalized $k$-means to group statistical models. Given the number of clusters $k$, the method is established under hypothesis tests between statistical models. If $k$ is unknown, then the method can be combined with GIC to automatically select the best $k$ for clustering. The article investigates both AIC MESHD and BIC as the special cases. Theoretical and simulation results show that the number of clusters can be identified by BIC but not AIC. The resulting method for GLMs is used to group the state-level time series patterns for the outbreak of COVID-19 MESHD in the United States. A further study shows that the statistical models between the clusters are significantly different from each other. This study confirms the result given by the proposed method based on generalized $k$-means.

    Application of ARIMA and Holt-Winters forecasting model to predict the spreading of COVID-19 MESHD for India and its states

    Authors: Mrutyunjaya Panda

    doi:10.1101/2020.07.14.20153908 Date: 2020-07-16 Source: medRxiv

    The novel Corona-virus (COVID-2019) epidemic has posed a global threat to human life and society. The whole world is working relentlessly to find some solutions to fight against this deadly virus to reduce the number of deaths. Strategic planning with predictive modelling and short term forecasting for analyzing the situations based on the worldwide available data allow us to realize the future exponential behaviour of the COVID-19 MESHD disease. Time series forecasting plays a vital role in developing an efficient forecasting model for a future prediction about the spread of this contagious disease. In this paper, the ARIMA (Auto regressive integrated moving average) and Holt-Winters time series exponential smoothing are used to develop an efficient 20- days ahead short-term forecast model to predict the effect of COVID-19 MESHD epidemic. The modelling and forecasting are done with the publicly available dataset from Kaggle as a perspective to India and its five states such as Odisha, Delhi, Maharashtra, Andhra Pradesh and West Bengal. The model is assessed with correlogram, ADF HGNC test, AIC MESHD and RMSE to understand the accuracy of the proposed forecasting model.

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.