Pertussis Dynamics in Tshwane, South Africa: The Role of Seasonality and Forecasting Techniques

Background: B. Pertussis remains a significant public health concern, with periodic outbreaks despite vaccination efforts. Understanding the temporal trends and seasonality of pertussis incidence is important for improving its surveillance and pre-vention strategies. Objectives: The aim of this study was to examine pertussis trends in Tshwane Health District, South Africa, from 2015 to 2019. Methods: A retrospective time series analysis was performed on reported pertussis cases in the Tshwane Health District from 2015 to 2019. Descriptive statistical techniques and time series decom-position were employed to investigate seasonal patterns. Autoregressive Moving Average (ARMA) models were employed to assess short-term trends, while cubic trend modelling was used to forecast future case trajectories. Residual diagnostics assessed model validity and analysed correlations between pertussis incidence and climate var-iables. Results: The incidence of Pertussis infection incidence exhibited seasonal pat-terns, with peak cases being reported in Spring and Winter. Time series decomposition confirmed annual fluctuations, suggesting potential environmental and social factors influencing disease transmission. The ARMA(4) model provided the best fit for short-term forecasting, while the cubic model effectively captured long-term trends. Residual diagnostics confirmed model reliability. Conclusion: This study highlights the utility of time series modelling in predicting pertussis trends and underscores the role of seasonality and climate factors in disease dynamics. The findings support enhanced surveillance, climate-adaptive interventions, and optimised vaccine timing to mitigate outbreaks. Integrating predictive modelling with public health strategies can improve outbreak preparedness and resource allocation.