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Takeaways:

• Epidemiology focuses on health at various scales, while biology often looks at micro-level details.
• Bayesian statistics helps connect models to data and quantify uncertainty.
• Recent advancements in data collection have improved the quality of epidemiological research.
• Collaboration between domain experts and statisticians is essential for effective research.
• The COVID-19 pandemic has led to increased data availability and international cooperation.
• Modeling infectious diseases requires understanding complex dynamics and statistical methods.
• Challenges in coding and communication between disciplines can hinder progress.
• Innovations in machine learning and neural networks are shaping the future of epidemiology.
• The importance of understanding the context and limitations of data in research.

Chapters:

00:00 Introduction to Bayesian Statistics and Epidemiology

03:35 Guest Backgrounds and Their Journey

10:04 Understanding Computational Biology vs. Epidemiology

16:11 The Role of Bayesian Statistics in Epidemiology

21:40 Recent Projects and Applications in Epidemiology

31:30...