A PHP Error was encountered

Severity: Warning

Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests

Filename: helpers/my_audit_helper.php

Line Number: 176

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016

File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 316
Function: require_once

Future worldwide coronavirus disease 2019 epidemic predictions by Gaidai multivariate risk evaluation method. | LitMetric

AI Article Synopsis

  • This research introduces a new method for estimating the risk of pandemic outbreaks across different U.S. states, using real-time clinical data and patient counts.
  • It highlights challenges in applying traditional extreme value statistics to higher dimensional data while aiming to improve how we assess epidemic risks.
  • The Gaidai multivariate hazard assessment technique is proposed as a solution for analyzing public health risks, offering insights based on raw medical survey data.

Article Abstract

Accurate estimation of pandemic likelihood in every US state of interest and at any time. Coronavirus disease 2019 (COVID-19) is an infectious illness with a high potential for global dissemination and low rates of fatality and morbidity, placing some strains on national public health systems. This research intends to benchmark a novel technique, that enables hazard assessment, based on available clinical data, and dynamically observed patient numbers while taking into account pertinent territorial and temporal mapping. Multicentre, population-based, and biostatistical strategies have been utilized to process raw/unfiltered medical survey data. The expansion of extreme value statistics from the univariate to the bivariate situation meets with numerous challenges. First, the univariate extreme value types theorem cannot be directly extended to the bivariate (2D) case,-not to mention challenges with system dimensionality higher than 2D. Assessing outbreak risks of future outbreaks in any nation/region of interest. Existing bio-statistical approaches do not always have the benefits of effectively handling large regional dimensionality and cross-correlation between various regional observations. These methods deal with temporal observations of multi-regional phenomena. Apply contemporary, novel statistical/reliability techniques directly to raw/unfiltered clinical data. The current study outlines a novel bio-system hazard assessment technique that is particularly suited for multi-regional environmental, bio, and public health systems, observed over a representative period. With the use of the Gaidai multivariate hazard assessment approach, epidemic outbreak spatiotemporal risks may be properly assessed. Based on raw/unfiltered clinical survey data, the Gaidai multivariate hazard assessment approach may be applied to a variety of public health applications. The study's primary finding was an assessment of the risks of epidemic outbreaks, along with a matching confidence range. Future global COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-COV2) epidemic risks have been examined in the current study; however, COVID-19/SARS-COV2 infection transmission mechanisms have not been discussed.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11361367PMC
http://dx.doi.org/10.1002/ansa.202400027DOI Listing

Publication Analysis

Top Keywords

hazard assessment
16
gaidai multivariate
12
public health
12
coronavirus disease
8
disease 2019
8
health systems
8
clinical data
8
survey data
8
raw/unfiltered clinical
8
current study
8

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!