A Predictive Model of the Start of Annual Influenza Epidemics

TL;DR

This paper presents a statistical model to predict the start of influenza epidemics in Catalonia, Spain, using clinical and meteorological data.

Contribution

A novel statistical model for predicting influenza epidemic onset using clinical diagnosis rates and meteorological variables.

Findings

The logistic regression model predicted influenza epidemic onset at least one week in advance.

The most important variables were bronchiolitis rates and mean temperature.

The model performed best for two of three performance metrics.

Abstract

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.

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