Educational Resource Hub
This crowd-sourced database of educational resources is meant to encompass any tools relevant to people working in the climate and health space. This might include submissions by the content authors themselves, or simply recommendations from community members for resources they have found helpful. This collection includes only links directing users to existing resources - it is not meant to house or archive content.
Keep in mind, this is a crowd-sourced database. CAFE does not verify the quality nor endorse the use of any materials included in this database. Make sure to follow the terms of use and attribution requirements specific to each resource. If you have created or used sources that would be relevant to the community of practice, please add it to the database by entering it in the submission form below.
This booklet tells you how to use the R statistical software to carry out some simple analyses that are common in analyzing time series data. This booklet assumes that the reader has some basic knowledge of time series analysis, and the principal focus of the booklet is not to explain time series analysis, but rather to explain how to carry out these analyses using R.
En‑ROADS is a global climate simulator that allows users to explore the impact that dozens of policies—such as electrifying transport, pricing carbon, and improving agricultural practices—have on hundreds of factors like energy prices, temperature, air quality, and sea level rise.
Our Climate Connections radio stories are broadcast nationwide on more than 750 public, university, community, and alternative radio frequencies, and internationally on a handful of English-language stations. The program also is available as a podcast. A new 90-second episode is released each weekday year-round. You can subscribe and listen to our podcast on a number of major platforms.
We expect that the book will be helpful to anyone interested in causal inference, including epidemiologists, statisticians, psychologists, economists, sociologists, political scientists, computer scientists… The book is divided in three parts of increasing difficulty: (1) causal inference without models, (2) causal inference with models, and (3) causal inference from complex longitudinal data.
What is climate? Climate is commonly thought of as the expected weather conditions at a given location over time. People know when they go to New York City in winter, they should take a heavy coat. When they visit the Pacific Northwest, they should take an umbrella. Climate can be measured as many geographic scales - for example, cities, countries, or the entire globe - by such statistics as average temperatures, average number of rainy days, and the frequency of droughts. Climate change refers to changes in these statistics over years, decades, or even centuries.
Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny describes spatial and spatio-temporal statistical methods and visualization techniques to analyze georeferenced health data in R. The book covers the following topics: (1) Manipulating and transforming point, areal, and raster data, (2) Bayesian hierarchical models for disease mapping using areal and geostatistical data, (3) Fitting and interpreting spatial and spatio-temporal models with the integrated nested Laplace approximation (INLA) and the stochastic partial differential equation (SPDE) approaches, (4) Creating interactive and static visualizations such as disease maps and time plots, (5) Reproducible R Markdown reports, interactive dashboards, and Shiny web applications that facilitate the communication of insights to collaborators and policymakers.
Foundations of Epidemiology is an open access, introductory epidemiology text intended for students and practitioners in public or allied health fields. It covers epidemiologic thinking, causality, incidence and prevalence, public health surveillance, epidemiologic study designs and why we care about which one is used, measures of association, random error and bias, confounding and effect modification, and screening.
This course covers basic epidemiology principles, concepts, and procedures useful in thesurveillance and investigation of health-related states or events. It is designed for federal,state, and local government health professionals and private sector health professionalswho are responsible for disease surveillance or investigation. A basic understanding ofthe practices of public health and biostatistics is recommended.
Crowd-Sourced Climate Change and Health Educational Resources Collection Submission Form
Do you have a resource you’d like to share with the community in this educational resource collection? Please fill out the submission form below.
Your entry will be checked to ensure the content is appropriate, but will not be assessed for accuracy or completeness, and no other quality checks will be done.
If you have a dataset you’d like to share with the community, think about posting it to the CAFE collection on Dataverse!
Please fill out the form to add a resource you think might be helpful for the climate change and health community of practice.
The type of resources that should be shared here are one of the following:
- Book or reference text (e.g. textbook or guidebook on best practices or other essential knowledge)
- Code repository (e.g. a GitHub code bank of an existing analysis)
- Online code tutorial or vignette (e.g. a walkthrough of specific code or methods with examples and explanations)
- Online course (e.g. a series of learning objectives with content and assessment)
- Video or recorded webinar (e.g. educational resources presented in video format)
