A semi-empirical risk panel to monitor epidemics: multi-faceted tool to assist healthcare and public health professionals

Otros/as autores/as

Institut Català de la Salut

[Perramon-Malavez A, Bravo M, López de Rioja V, Alonso S, Álvarez-Lacalle E] Department of Physics, Computational Biology and Complex Systems (BIOCOM-SC) group, Barcelona School of Agri-Food and Biosystems Engineering, Universitat Politècnica de Catalunya, Castelldefels, Spain. [Català M] Health Data Sciences, NDORMS, University of Oxford, Oxford, United Kingdom. [Soriano-Arandes A] Unitat de Patologia Infecciosa i Immunodeficiències de Pediatria, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Grup de Recerca d’Infecció i Immunitat al Pacient Pediàtric, Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain

Vall d'Hebron Barcelona Hospital Campus

Fecha de publicación

2024-02-16T08:08:21Z

2024-02-16T08:08:21Z

2024-01-08



Resumen

Bronchiolitis; Epidemic indicators; Threshold


Bronquiolitis; Indicadores epidémicos; Umbral


Bronquiolitis; Indicadors epidèmics; Llindar


Introduction: Bronchiolitis, mostly caused by Respiratory Syncytial Virus (RSV), and influenza among other respiratory infections, lead to seasonal saturation at healthcare centers in temperate areas. There is no gold standard to characterize the stages of epidemics, nor the risk of respiratory infections growing. We aimed to define a set of indicators to assess the risk level of respiratory viral epidemics, based on both incidence and their short-term dynamics, and considering epidemical thresholds. Methods: We used publicly available data on daily cases of influenza for the whole population and bronchiolitis in children <2 years from the Information System for Infection Surveillance in Catalonia (SIVIC). We included a Moving Epidemic Method (MEM) variation to define epidemic threshold and levels. We pre-processed the data with two different nowcasting approaches and performed a 7-day moving average. Weekly incidences (cases per 105 population) were computed and the 5-day growth rate was defined to create the effective potential growth (EPG) indicator. We performed a correlation analysis to define the forecasting ability of this index. Results: Our adaptation of the MEM method allowed us to define epidemic weekly incidence levels and epidemic thresholds for bronchiolitis and influenza. EPG was able to anticipate daily 7-day cumulative incidence by 4–5 (bronchiolitis) or 6–7 (influenza) days. Discussion: We developed a semi-empirical risk panel incorporating the EPG index, which effectively anticipates surpassing epidemic thresholds for bronchiolitis and influenza. This panel could serve as a robust surveillance tool, applicable to respiratory infectious diseases characterized by seasonal epidemics, easy to handle for individuals lacking a mathematical background.


The author(s) declare financial support was received for the research, authorship, and/or publication of this article. Grant number 202134-30-31, funded by “La Fundació La Marató de TV3.” Grant PDI2022-139215NB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe.”

Tipo de documento

Artículo


Versión publicada

Lengua

Inglés

Publicado por

Frontiers Media

Documentos relacionados

Frontiers in Public Health;11

https://doi.org/10.3389/fpubh.2023.1307425

Citación recomendada

Esta citación se ha generado automáticamente.

Derechos

Attribution 4.0 International

http://creativecommons.org/licenses/by/4.0/

Este ítem aparece en la(s) siguiente(s) colección(ones)