Fundamentals of Statistical Modeling

This video is Part 5 of the advanced webinar series on spatiotemporal modeling of climate-sensitive diseases. It reviews key types of epidemiological and climate data used in model development and introduces hierarchical generalized linear models and Bayesian inference. The session covers core concepts such as fixed and random effects, data variability, model assumptions, and the influence of external climate factors on forecast accuracy. It also explores how predictive models can be applied to disease forecasting and early warning systems.

• Overview of the Webinar Series
• dhis2.org/climate