Time series modelling and forecasting of Monkeypox outbreak trends Africa's in most affected countries

Abstract

Background: The recent outbreak of Monkeypox (Mpox), particularly the clade 1b variant, have shifted the epidemiological landscape, making it a Public Health Emergency of International Concern. Africa remains a hotspot with significant ongoing outbreaks, necessitating a focused study on outbreak trends and forecasting to guide health interventions. Methods: This study utilizes a comprehensive dataset from the four most affected African countries, covering weekly and cumulative Mpox cases from August 6, 2023, to August 18, 2024. Time series analysis techniques, including ARIMA models and Join Point Regression, were employed to forecast Mpox trends and analyse the annual percentage change in new cases. Results: Descriptive statistics highlighted significant variability in Mpox cases across the studied regions with the mean cases in Africa at 72.55 and a high standard deviation of 60.885. Forecasting models suggest a continued increase in Mpox cases, with cumulative cases expected to reach 6922.95 by the 65th week (95 % CI: 6158.62 to 7687.27) and new cases projected at 45.93 (95 % CI: −88.17 to 180.04). Conclusion: The study underscores the persistent nature of Mpox outbreaks in Africa and the critical need for continuous surveillance and adaptive public health strategies. The forecasts generated offer valuable insights into potential future trends, aiding in the allocation of resources and the implementation of targeted health interventions to curb the spread of the disease.

Keywords

• Mpox
• Africa
• Forecasting
• ARIMA
• Join point