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EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting

  • S. Siva Shankar , R. Sunder , P. P. Rahoof , Thangiah Sathish Kumar , Lingidi Nageswar Rao and Gayatri Parasa
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Machine Learning in Healthcare
This chapter is in the book Machine Learning in Healthcare

Abstract

Real-time monitoring of public health is critical for predicting and evading the impacts of disease epidemics. Statistical models and deep learning algorithms have been shown to perform poorly in addressing the challenges that come with temporal and spatially distributed epidemic data. This article presents a new hybrid machine learning model known as EpiCastNet for predicting disease outbreaks and epidemic trends in real time. EpiCastNet integrates spatiotemporal modeling through graph neural networks and temporal convolutional networks and incorporates an adaptive online learning engine that improves over time. The model is trained from multisource information including electronic health records, mobility data, environmental information, and social media. EpiCastNet considerably improves key performance metrics including predictive accuracy, lead time, and spatial accuracy. In comparison to baseline models such as ARIMA, LSTM, and Prophet, EpiCastNet outperforms these in lead time accuracy, averaging a prediction lead of 6.2 days and is more spatially accurate with an intersection over union of 0.72. The composite indicator of early warning performance, the EpiCastNet alert quality index, reports 0.80, indicating a high proportion of actual positive alerts and a low number of false positives or negatives. This model provides a new way of early detection of outbreak, equipping public health agencies with the ability to forecast and respond to emerging epidemics more accurately and rapidly. Flexibility, interpretability, and privacy preserving abilities of the model render it a valuable tool for global health surveillance and real-time decision-making.

Abstract

Real-time monitoring of public health is critical for predicting and evading the impacts of disease epidemics. Statistical models and deep learning algorithms have been shown to perform poorly in addressing the challenges that come with temporal and spatially distributed epidemic data. This article presents a new hybrid machine learning model known as EpiCastNet for predicting disease outbreaks and epidemic trends in real time. EpiCastNet integrates spatiotemporal modeling through graph neural networks and temporal convolutional networks and incorporates an adaptive online learning engine that improves over time. The model is trained from multisource information including electronic health records, mobility data, environmental information, and social media. EpiCastNet considerably improves key performance metrics including predictive accuracy, lead time, and spatial accuracy. In comparison to baseline models such as ARIMA, LSTM, and Prophet, EpiCastNet outperforms these in lead time accuracy, averaging a prediction lead of 6.2 days and is more spatially accurate with an intersection over union of 0.72. The composite indicator of early warning performance, the EpiCastNet alert quality index, reports 0.80, indicating a high proportion of actual positive alerts and a low number of false positives or negatives. This model provides a new way of early detection of outbreak, equipping public health agencies with the ability to forecast and respond to emerging epidemics more accurately and rapidly. Flexibility, interpretability, and privacy preserving abilities of the model render it a valuable tool for global health surveillance and real-time decision-making.

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
  4. DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
  5. Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
  6. A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
  7. Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
  8. Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
  9. Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
  10. EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
  11. Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
  12. Machine Learning Techniques for Healthcare 193
  13. Applications and Benefits of Machine Learning in Healthcare 215
  14. Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
  15. Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
  16. Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
  17. MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
  18. On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
  19. Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
  20. AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
  21. A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
  22. Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
  23. Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
  24. Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
  25. MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
  26. Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
  27. Index
https://doi.org/10.1515/9783111541235-008

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
  4. DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
  5. Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
  6. A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
  7. Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
  8. Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
  9. Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
  10. EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
  11. Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
  12. Machine Learning Techniques for Healthcare 193
  13. Applications and Benefits of Machine Learning in Healthcare 215
  14. Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
  15. Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
  16. Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
  17. MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
  18. On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
  19. Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
  20. AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
  21. A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
  22. Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
  23. Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
  24. Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
  25. MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
  26. Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
  27. Index
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