Fundamentals of statistical modeling

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

This webinar reviews core statistical principles essential for climate-health modeling, including Bayesian statistics and time series analysis techniques. Participants will explore the foundational steps of modeling and forecasting using time series data, from data preparation and pattern identification to generating predictions for climate-sensitive diseases.

The session will also focus on understanding uncertainty in predictive modeling, including key sources like data variability, model assumptions, and external climate factors that impact forecast accuracy.

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