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

Aida Perramon-Malavez; Mario Bravo; Víctor López de Rioja; Martí Català; Sergio Alonso; Enrique Álvarez-Lacalle; Daniel López; Antoni Soriano-Arandes; Clara Prats

doi:10.3389/fpubh.2023.1307425

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

Front Public Health. 2024 Jan 8:11:1307425. doi: 10.3389/fpubh.2023.1307425. eCollection 2023.

Authors

Aida Perramon-Malavez¹, Mario Bravo¹, Víctor López de Rioja¹, Martí Català², Sergio Alonso¹, Enrique Álvarez-Lacalle¹, Daniel López¹, Antoni Soriano-Arandes^{3

4}, Clara Prats¹

Affiliations

¹ 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.
² Health Data Sciences, NDORMS, University of Oxford, Oxford, United Kingdom.
³ Paediatric Infectious Diseases and Immunodeficiencies Unit, Children's Hospital, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Catalonia, Spain.
⁴ Infection and Immunity in Paediatric Patients, Vall d'Hebron Research Institute, Barcelona, Catalonia, Spain.

Abstract

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.

Keywords: bronchiolitis; effective potential growth; epidemic; epidemic indicators; influenza; levels; respiratory infections; threshold.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Bronchiolitis*
Child
Delivery of Health Care
Humans
Influenza, Human* / epidemiology
Public Health
Respiratory Tract Infections*

Grants and funding

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.”