Modeling and Forecasting COVID-19 Incidence Rates: A Time Series Analysis of Acute Respiratory Infections (ARI) in France Since Surveillance Initiation

Abstract

Objective: This study aims to address the challenges of planning and managing the trajectory of the COVID-19 pandemic by evaluating the predictive abilities of three distinct forecasting models. The primary focus is on the ATA univariate forecasting method, ARIMA (AutoRegressive Integrated Moving Average), and ETS (Error-Trend-Seasonality) models. These models are applied to a meticulously collected dataset comprising Acute Respiratory Infections (ARI) incidence rates in France, systematically collected since the initiation of surveillance. Methods: The purpose of the study was to conduct a comprehensive evaluation of forecasting models using the selected dataset to achieve its objective. The focus was on comparing the accuracy and performance of ATA univariate forecasting, ARIMA, and ETS models in predicting COVID-19 incidence rates. Additionally, the study incorporated a combination approach proven to be effective in enhancing forecasting performance. Results: According to the results obtained regarding forecast performance, the univariate models indicate that the ATA method exhibits the highest performance, while observations reveal that combinations of ATA and ARIMA methods enhance forecast accuracy. Conclusions: In summary, the most accurate approach for forecasting future Covid-19 incidence rates, specifically those derived from Acute Respiratory Infections (ARI), has been a combination of the high-accuracy methods ATA and ARIMA. These findings enhance our understanding of the trajectory of the pandemic, providing a foundation for strategic planning and effective management.