Journal Article

A Predictive Model of the Start of Annual Influenza Epidemics

Gonçalves, Alessandra Queiroga, Coma, Ermengol, Castro Blanco, Elisabet, Fernández-Sáez, José, Dalmau Llorca, Maria Rosa, Hernández Rojas, Zojaina, Aguilar Martín, Carina, Carrasco-Querol, Noèlia

2024

Overview

Influenza is a respiratory disease that causes annual epidemics during cold seasons. These epidemics increase pressure on healthcare systems, sometimes provoking their collapse. For this reason, a tool is needed to predict when an influenza epidemic will occur so that the healthcare system has time to prepare for it. This study therefore aims to develop a statistical model capable of predicting the onset of influenza epidemics in Catalonia, Spain. Influenza seasons from 2011 to 2017 were used for model training, and those from 2017 to 2018 were used for validation. Logistic regression, Support Vector Machine, and Random Forest models were used to predict the onset of the influenza epidemic. The logistic regression model was able to predict the start of influenza epidemics at least one week in advance, based on clinical diagnosis rates of various respiratory diseases and meteorological variables. This model achieved the best punctual estimates for two of three performance metrics. The most important variables in the model were the principal components of bronchiolitis rates and mean temperature. The onset of influenza epidemics can be predicted from clinical diagnosis rates of various respiratory diseases and meteorological variables. Future research should determine whether predictive models play a key role in preventing influenza.

• Publisher: MDPI AG
• Subject: Automation / bronchiolitis / Bronchopneumonia / cold / Cold season / Diagnosis / Epidemics / Generalized linear models / Health care / health services / Humidity / Influenza / Performance measurement / Performance prediction / Pneumonia / Prediction models / predictive modeling / Primary care / public health / Random variables / Regression analysis / Regression models / Respiratory diseases / Spain / Statistical analysis / Statistical models / Support vector machines / syndromic surveillance / Temperature / Variables