Early warning of potential epidemics: A pilot application of an early warning tool to data from the pulmonary clinic of the university hospital of Thessaly, Greece.

Early warning Epidemics Public health Respiratory infections Syndromic surveillance

Journal

Journal of infection and public health
ISSN: 1876-035X
Titre abrégé: J Infect Public Health
Pays: England
ID NLM: 101487384

Informations de publication

Date de publication:
11 Jan 2024
Historique:
received: 18 09 2023
revised: 02 01 2024
accepted: 08 01 2024
medline: 23 1 2024
pubmed: 23 1 2024
entrez: 23 1 2024
Statut: aheadofprint

Résumé

This paper describes a pilot application of the Epidemic Volatility Index (EVI) to data from the pulmonary clinic of the University Hospital of Thessaly, Greece, for monitoring respiratory infections, COVID-19, and flu cases. EVI, a simple and easily implemented early warning method based on the volatility of newly reported cases, exhibited consistent and stable performance in detecting new waves of epidemics. The study highlights the importance of implementing early warning tools to address the effects of epidemics, including containment of outbreaks, timely intervention strategies, and resource allocation within real-world clinical settings as part of a broader public health strategy. The results presented in the figures demonstrate the association between successive early warnings and the onset of new waves, providing valuable insights for proactive decision-making. A web-based application enabling real-time monitoring and informed decision-making by healthcare professionals, public health officials, and policymakers was developed. This study emphasizes the significant role of early warning methods in managing epidemics and safeguarding public health. Future research may explore extensions and combinations of multiple warning systems for optimal outbreak interventions and application of the methods in the context of personalized medicine.

Sections du résumé

BACKGROUND & METHODS METHODS
This paper describes a pilot application of the Epidemic Volatility Index (EVI) to data from the pulmonary clinic of the University Hospital of Thessaly, Greece, for monitoring respiratory infections, COVID-19, and flu cases. EVI, a simple and easily implemented early warning method based on the volatility of newly reported cases, exhibited consistent and stable performance in detecting new waves of epidemics. The study highlights the importance of implementing early warning tools to address the effects of epidemics, including containment of outbreaks, timely intervention strategies, and resource allocation within real-world clinical settings as part of a broader public health strategy.
RESULTS RESULTS
The results presented in the figures demonstrate the association between successive early warnings and the onset of new waves, providing valuable insights for proactive decision-making. A web-based application enabling real-time monitoring and informed decision-making by healthcare professionals, public health officials, and policymakers was developed.
CONCLUSIONS CONCLUSIONS
This study emphasizes the significant role of early warning methods in managing epidemics and safeguarding public health. Future research may explore extensions and combinations of multiple warning systems for optimal outbreak interventions and application of the methods in the context of personalized medicine.

Identifiants

pubmed: 38262075
pii: S1876-0341(24)00009-1
doi: 10.1016/j.jiph.2024.01.008
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

401-405

Informations de copyright

Copyright © 2024. Published by Elsevier Ltd.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors have no competing or any other interests that might be perceived to influence the results and/or discussion reported in this paper.

Auteurs

Eleftherios Meletis (E)

Faculty of Public and One Health, University of Thessaly, Karditsa, Greece. Electronic address: elmeletis@uth.gr.

Irene Poulakida (I)

Respiratory Medicine Department, University of Thessaly, School of Medicine, University Hospital of Larissa, Larissa, Greece.

Garyfallia Perlepe (G)

Respiratory Medicine Department, University of Thessaly, School of Medicine, University Hospital of Larissa, Larissa, Greece.

Asimina Katsea (A)

Respiratory Medicine Department, University of Thessaly, School of Medicine, University Hospital of Larissa, Larissa, Greece.

Konstantinos Pateras (K)

Faculty of Public and One Health, University of Thessaly, Karditsa, Greece; Department of Data Science and Biostatistics, University of Utrecht, Utrecht 3508, the Netherlands.

Stylianos Boutlas (S)

Respiratory Medicine Department, University of Thessaly, School of Medicine, University Hospital of Larissa, Larissa, Greece.

Georgia Papadamou (G)

Respiratory Medicine Department, University of Thessaly, School of Medicine, University Hospital of Larissa, Larissa, Greece.

Konstantinos Gourgoulianis (K)

Respiratory Medicine Department, University of Thessaly, School of Medicine, University Hospital of Larissa, Larissa, Greece.

Polychronis Kostoulas (P)

Faculty of Public and One Health, University of Thessaly, Karditsa, Greece.

Classifications MeSH