Module #1 Introduction to Epidemic Prediction Overview of the importance of epidemic prediction, machine learning applications, and course objectives
Module #2 Epidemiology Fundamentals Basics of epidemiology, types of epidemics, and common diseases studied
Module #3 Mathematical Modeling of Epidemics Introduction to compartmental models, SIR models, and other mathematical approaches
Module #4 Machine Learning for Epidemic Prediction Overview of machine learning concepts, supervised and unsupervised learning, and feature engineering
Module #5 Data Sources for Epidemic Prediction Introduction to data sources, including surveillance systems, social media, and genomic data
Module #6 Data Preprocessing and Feature Engineering Handling missing data, feature scaling, and dimensionality reduction
Module #7 Supervised Learning for Epidemic Prediction Regression, classification, and probability estimation for epidemic prediction
Module #8 Unsupervised Learning for Epidemic Prediction Clustering, anomaly detection, and density-based methods
Module #9 Deep Learning for Epidemic Prediction Introduction to deep learning, Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks
Module #10 Ensemble Methods for Epidemic Prediction Bagging, boosting, and stacking for improved prediction performance
Module #11 Evaluating Epidemic Prediction Models Metrics for evaluating model performance, including accuracy, precision, and recall
Module #12 Handling Imbalanced Data in Epidemic Prediction Strategies for addressing class imbalance, including oversampling, undersampling, and cost-sensitive learning
Module #13 Spatial and Temporal Analysis for Epidemic Prediction Incorporating spatial and temporal information into machine learning models
Module #14 Genomic Data Analysis for Epidemic Prediction Introduction to genomic data, phylogenetic analysis, and machine learning applications
Module #15 Social Network Analysis for Epidemic Prediction Incorporating social network data and graph-based methods into machine learning models
Module #16 Real-World Applications of Epidemic Prediction Case studies of machine learning for epidemic prediction, including influenza, Ebola, and COVID-19
Module #17 Challenges and Limitations of Epidemic Prediction Discussing data quality, model interpretability, and ethical considerations
Module #18 Future Directions in Epidemic Prediction Emerging trends and areas of research, including explainable AI and human-in-the-loop approaches
Module #19 Hands-on Exercise 1:Time Series Analysis for Epidemic Prediction Practical exercise using a popular machine learning library (e.g., scikit-learn, TensorFlow)
Module #20 Hands-on Exercise 2:Deep Learning for Epidemic Prediction Practical exercise using a deep learning library (e.g., Keras, PyTorch)
Module #21 Hands-on Exercise 3:Spatial Analysis for Epidemic Prediction Practical exercise using a spatial analysis library (e.g., geopandas, folium)
Module #22 Hands-on Exercise 4:Genomic Data Analysis for Epidemic Prediction Practical exercise using a genomic data analysis library (e.g., scikit-bio, Biopython)
Module #23 Hands-on Exercise 5:Social Network Analysis for Epidemic Prediction Practical exercise using a social network analysis library (e.g., NetworkX, igraph)
Module #24 Project Development and Presentation Students work on individual or group projects and present their results
Module #25 Peer Review and Feedback Students review and provide feedback on their peers projects
Module #26 Course Wrap-up and Future Directions Review of key concepts, lessons learned, and next steps in the field
Module #27 Appendix:Additional Resources for Epidemic Prediction Supplementary materials, including datasets, research articles, and online resources
Module #28 Appendix:Python for Epidemic Prediction Intro to Python programming for students without prior experience
Module #29 Appendix:R for Epidemic Prediction Intro to R programming for students without prior experience
Module #30 Course Wrap-Up & Conclusion Planning next steps in Machine Learning for Epidemic Prediction career
