Monkeypox Outbreak Analysis: An Extensive Study Using Machine Learning Models and Time Series Analysis

The sudden unexpected rise in monkeypox cases worldwide has become an increasing concern. zoonotic disease characterized by smallpox-like symptoms already spread to nearly twenty countries and several continents is labeled a potential pandemic experts. infections do not have specific treatments. However, since smallpox viruses are similar administering antiviral drugs vaccines against could be used prevent treat monkeypox. Since the becoming global concern, it necessary analyze its impact population health. Analyzing key outcomes, such as number of people infected, deaths, medical visits, hospitalizations, etc., play significant role preventing spread. In this study, we virus across different using machine learning techniques linear regression (LR), decision trees (DT), random forests (RF), elastic net (EN), artificial neural networks (ANN), convolutional (CNN). Our study shows that CNNs perform best, performance these models evaluated statistical parameters mean absolute error (MAE), squared (MSE), percentage (MAPE), R-squared (R2). also presents time-series-based analysis autoregressive integrated moving averages (ARIMA) seasonal auto-regressive (SARIMA) for measuring events over time. Comprehending can lead understanding risk, which may further enable timely effective treatment.