Scholarlink

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.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Child Mortality Prediction in India: A Time Series Approach Using ARIMA and SARIMA Models

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Samadhan Pujari,
  • Hetvi Saroliya,
  • Vedika Gawde,
  • Ekansh Manral,
  • Jalpa Mehta,
  • Deepika Patil,
  • Rashmi Malvankar

摘要

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.