A comprehensive, open-source handbook for understanding and applying artificial intelligence in public health practice, available at publichealthaihandbook.com. This resource provides evidence-based guidance for evaluating AI tools in disease surveillance, epidemic forecasting, outbreak response, and implementation across resource-constrained settings. Written for epidemiologists, health department staff, clinicians, and policymakers, the handbook addresses practical questions: which AI tools actually perform in real surveillance settings, how to evaluate models when data is messy and incomplete, and what happens when algorithms are wrong and public health action follows. Includes evaluation frameworks, implementation strategies, ethics and governance considerations, code examples, and case studies from real-world applications. Written by Bryan Tegomoh, MD, MPH.