index.qmd

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title: "spatial-epi-101"
subtitle: "An Introduction to Spatial Epidemiology using R"
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page-layout: full
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3D Visualization of Nördlinger Ries
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## Welcome to the Course

This course provides a comprehensive introduction to the principles and practices of spatial epidemiology. You will learn how to leverage the power of R to analyze, visualize, and interpret spatial health data, gaining critical skills for public health research and practice.

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## Course Modules

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### Module 1: Mastering R for Spatial Data
Build a rock-solid foundation in handling, manipulating, and visualizing vector and raster data using the essential `sf`, `terra`, and `tmap` packages.
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### Module 2: Advanced Spatial Data Operations
Learn the core data wrangling techniques of spatial analysis, including spatial joins, buffering, and extracting raster values for defined zones.
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### Module 3: Remote Sensing for Epidemiology
Process satellite imagery to derive key environmental variables like NDVI and NDWI, and understand their application in modeling vector-borne diseases.
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### Module 4: Validation and Accuracy Assessment
Learn how to validate your spatial models and maps against ground-truth data using confusion matrices, ROC curves, and other key statistical metrics.
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### Module 5: Analyzing Spatial Clustering
Move from visualization to formal testing. This module introduces spatial statistics like Moran's I and Ripley's K-function to identify disease clusters and hotspots.
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### Module 6: Resources & Best Practices
A curated list of key textbooks, R packages, and online resources to continue your learning journey. This module also covers principles of ethical and reproducible research.
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