#120 Innovations in Infectious Disease Modeling, with Liza Semenova & Chris Wymant
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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 Sam
