Time Series Analysis and Forecasting of Monkeypox Disease Using ARIMA and SARIMA Model

Infectious disease outbreak forecasts are just one application of the time series forecasting method. Despite its proven sophisticated analysis and trend preservation restrictions, time series forecasting can be investigated through single-step ahead as well as multi-step ahead forecasting. So, using this application, we can forecast the spread of the monkeypox outbreak. Commonly used models for time series forecasting include the Auto-regressive integrated moving average (ARIMA) and seasonal Autoregressive integrated moving average (SARIMA) have been used in this research. Various analytical methods and assessment criteria were used to validate the findings, and the resulting root mean square errors (RMSE) for the ARIMA and SARIMA models, respectively, were 3.6818 and 3.1180. According to the study's findings, the number of active cases is likely to increase soon. Future daily confirmed and cumulative confirmed cases can be predicted using the proposed models. This study will help in the development of effective public health strategies for the forthcoming monkeypox outbreak.