Authors - Samadhan Pujari, Hetvi Saroliya, Vedika Gawde, Ekansh Manral, Jalpa Mehta, Deepika Patil, Rashmi Malvankar Abstract - Mortality prediction is crucial for public health, aiding resource allocation, policy-making, and preventive strategies. This study applies ARIMA and SARIMA models to analyze mortality trends in India (1990–2022) using data on infectious diseases such as Malaria, HIV/AIDS, Tuberculosis, as well as non-communicable diseases like Nutritional deficiencies and neonatal disorders. ARIMA [1, 3, 4] captures non-seasonal trends, while SARIMA, incorporating seasonality, proves more accurate. Implemented using Python libraries like pandas, stats models, and scikit-learn, their accuracy is assessed using Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). Findings indicate that SARIMA outperforms ARIMA, emphasizing the role of seasonality in mortality patterns.[6,7,16] A significant decline in deaths from infectious diseases like Malaria and Measles is observed, attributed to public health initiatives, immunization programs, and improved healthcare facilities while neonatal and non-communicable diseases remain pressing concerns. Accurate data collection is essential for improving predictive modeling, and ARIMA/SARIMA provide critical insights for public health planning. Future research could integrate factors such as climate change, economic conditions, and machine learning techniques to refine forecasting models further. This study reinforces the significance of time series forecasting in public health decision-making and strategic healthcare interventions.
