Comparative analysis of ARIMA and Holt-Winter’s additive models for describing human respiratory syncytial virus activity in Yaoundé, Cameroon

Abstract

Objectives Human respiratory syncytial virus (HRSV) is a major cause of respiratory infections in children and older adults. This study compared the Autoregressive Integrative Moving Average (ARIMA) and Holt-Winter’s Additive models to describe HRSV activity in Yaoundé, Cameroon. Methods In a three-year retrospective study (July 2020–December 2022), analyzed 1,774 nasopharyngeal samples from patients with severe acute respiratory infections (SARI) and influenza-like illness (ILI) were analysed across five sentinel sites in Yaoundé. The ARIMA model assessed the relationship between HRSV activity and meteorological factors (temperature, humidity, rainfall, solar radiation), while Holt-Winter’s Additive model described HRSV activity without climate variables. Model performance was evaluated using stationary R 2 and root mean square error (RMSE). Results HRSV was detected in 8.5% (151/1774) samples. Holt-Winter’s model outperformed ARIMA, achieving a stationary R 2 of 77.6% and an RMSE of 7.40. ARIMA models for individual climate variables performed poorly (<6% R 2 ), but the combined 12-variable model improved to 56.4% and an RMSE of 12.94. Conclusion Holt-Winter’s model is more effective for predicting HRSV activity. These findings can guide public health interventions to reduce HRSV’s impact in Cameroon.