Fundamentals of statistical modeling

Part 5 of the Advanced Webinar Series on Spatiotemporal Modeling of Climate-Sensitive Diseases.

This webinar reviews spatiotemporal epidemiological and climate data for model development and provides an introduction to hierarchical generalized linear models and Bayesian inference. During the session, we will review key aspects of model development, including understanding fixed and random effects, and issues like data variability, model assumptions, and external climate factors that impact forecast accuracy.

Finally, we will explore the application of predictive modeling to disease forecasting for early warning systems.

Note: While open to everyone, this webinar series is targeting technically prepared participants with Python/R skills, epi/stats expertise, and motivation to develop or work with early warning systems for climate-sensitive diseases.

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Learn more about this webinar series: Announcement on the CoP