In agroimpacts/covid19interventions: Package for a COVID-19 related spatial interventions database for the U.S.

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(covid19interventions)

Goal

Compile and analyze COVID-19 related spatial interventions in the U.S. at the county level

Data & Methods

The data will be extracted from various sources and added to the COVID-19 US Spatial Response database from UArizona.We plan to ingest data from various sources, including county-level orders issued in response to COVID-19 and tweets posted through verified sources. States worst affected by COVID-19 and counties with the highest population within them will be prioritized in the data collection process. We hope to automate the data extraction process using R. We are exploring natural language processing (NLP) methods to parse government orders. We are also collecting data manually in parallel.

Responsibilities

Priyanka: Research and automation of the data collection process. Exploring NLP libraries in R to parse orders issued by local governments.

Noah: Research & data automation (ingestion) with Twitter data. Exploring TwitteR package to compile a list of verified accounts for counties or public health departments.

Priscilla: Research & analysis. Building database for Twitter scraping.

Max: Research & analysis. Building interventions database for MA.

Research Qs: - Did intervention measures affect the curve? - How many cases and deaths were recorded the date the intervention measures were put in place? - Which states observed the strictest and most lenient policies? - What variables were the main drivers of interventions (quantity of cases and deaths, location (contiguity), rurality, political party, population, etc.)? - How have different political parties responded in terms of time, intervention type, and location? - Did counties intervene before the state? In what way?

Other Qs: - How did supply shortage (binary Y/N) relate to cases and deaths? - How does rurality relate to cases and deaths?

Timeline

• Week 1: Identify data sources, investigate methods, create plan
• Week 2: Write scripts to extract data, QA/QC data
• Week 3: Analyze/vizualize data and combine data into unifed COVID-19 accessible data base

agroimpacts/covid19interventions documentation built on May 6, 2020, 12:35 a.m.