Disability and COVID-19 Reproduction

Sep-21 : In this lesson, we will reproduce county-level association of disability and COVID-19.

The purpose of this workshop is to reproduce Chakraborty (2021) and in doing so, learn about spatial scan statistics for epidemiology and about generalized estimating equations.

Chakraborty, J. 2021. Social inequities in the distribution of COVID-19: An intra-categorical analysis of people with disabilities in the U.S. Disability and Health Journal 14 (1):101007. DOI: 10.1016/j.dhjo.2020.101007.

Procedure

• fork the HEGSRR/RPr-Chakraborty-2021 repository to your own GitHub account
• clone the repository to your local machine
• execute the analysis
• document changes to your understanding of the research design by amending your workflow diagram
• comment any potential errors, uncertainties, or opportunities for improvement
• compare results to the original publication
• draft thoughts highlighting what you have learned from this reproduction study so far and identifying any errors, uncertain decisions, or opportunities for improving the study design or its reproducibility

API: Application Program Interface

This analysis makes use of the Census API. An API is an interface for passing data or commands between different software programs (Wikipedia). Examples of APIs in spatial data science include:

• DHS Surveys
• Census
• OpenStreetMap Overpass
• QGIS
• GRASS
• Twitter
• Google Earth Engine

If an API is useful for data science, it is very common to find packages facilitating connection to the API from Python or R.

• rdhs
• tidycensus
• osmdata
• rtweet
• rqgis
• rgrass7

Additionally, if a data course is useful for data science, it’s common to find an R package specific to loading that data source. For example:

• GADMTools for GADM
• rnaturalearth for Natural Earth

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