Overview

We are currently in unprecedented times amidst a global pandemic due to COVID-19. COVID-19 is known to be a flu-like virus that is extremely contagious. Since it was discovered in late 2019, there is no known vaccination for COVID-19.

Some countries, such as Italy1 and Spain2 have experienced extreme hardship in the healthcare system where they do not have enough hospital beds. In order to prevent as similar situation, US healthcare officials are urging that US residents adopt social distancing measures. After a few weeks of reccomendations, US states have enacted their own policies to ensure social distancing and prevent the spread of COVID-19 and potentially the collapse of the healthcare system.

I thought it would be interesting to see how strict different states are during the crisis to see if other people are in similar situations as I am.

The above map takes into account the following social distancing policies:
  • The easing of social distancing measures
  • Stay at home orders
  • Mandatory quarantine for travelers
  • Non-essential business closures
  • Large gathering bans
  • School closures
  • Limits on restaruants and bars
  • The postponement of the primary elections
  • Whether the state has been declared in a State of Emergency

Data Description

The data was found on the Kaiser Family Foundation3 website. However, I was less interested in the individual policies than I was in the strictness of the policies as a whole. As such, I created my own scale to measure the how strict the social distancing measures were for each state.

The website produced a .csv file which I then loaded into a Python4 package called pandas5. I used pandas to preprocess the data and create the strictness measures.

I went through each category of the different measures and assigned them a point value. The stricter the policy, the higher the point value was. If the state did not have a policy in place for that particular category, a value of 0 was assigned. If the the state was easing up on the social distancing measures, a negative point value was assigned.

A breakdown of the point values are seen below:

State Is Easing Social Distancing Measures

Policy Type Point Allotment
None 0
Reopens Certain Businesses -1
Reopens Certain Businesses; Allows Limited Restaurant Dine-in Services -2
Changes to Stay at Home Order; Reopens Certain Businesses -2
Changes to Stay at Home Order; Reopens Certain Businesses; Eases Large Gatherings Ban -3
Changes to Stay at Home Order; Reopens Certain Businesses; Allows Limited Restaurant Dine-in Services; Eases Large Gatherings Ban -4

Stay at Home Order

Policy Type Point Allotment
Statewide 3
High-Risk Groups 2
High-Risk Groups* 1
Other 1
None 0
Expired* -1

Mandatory Quarantine for Travelers

Policy Type Point Allotment
All Travelers 3
All Air Travelers 2
From Certain States 1
Other 1
None 0

Non-Essential Business Closures

Policy Type Point Allotment
All Non-Essential Businesses 5
All Non-Essential Retail Businesses 4
Certain Non-Essential Businesses 3
Certain Non-Essential Businesses* 2
Open with Reduced Capacity* 1
None 0

Large Gatherings Bans

Policy Type Point Allotment
All Gatherings Prohibited 4
>10 People Prohibited 3
20+ People Prohibited* 2
Other 1
None 0
Expired* -1

School Closures

Policy Type Point Allotment
Closed for School Year 3
Closed 3
Recommended Closure for School Year 2
Recommended Closure 1
None 0

Bar and Restaurant Limits

Policy Type Point Allotment
Closed Except for Takeout/Delivery 3
Limited On-Site Service 3
Limited On-Site Service* 2
Other 1
None 0

Primary Election Postponement

Policy Type Point Allotment
Cancelled 3
Postponed 2
None 0

Emergency Declaration

Policy Type Point Allotment
Yes 1
None 0

A * indicates that the state has eased a more restricitve social distancing measure.

After creating the point system, I added up all the points for each state and used the Python package plotly6 to create a chloropleth map of the data. I then exported the visualization as a .png7 file.

Author: Mallika Suri. Last edited: 2020-05-04

  1. COVID-19 in Italy. Online: https://www.nejm.org/doi/full/10.1056/NEJMp2005492↩︎

  2. COVID-19 in Spain. Online: https://www.ncbi.nlm.nih.gov/pubmed/11149196↩︎

  3. Kaiser Family Foundation website. Online: https://www.kff.org↩︎

  4. Python. Copyright (C) 2001–2020. Python Software Foundation. Online: https://www.python.org↩︎

  5. Pandas. Copyright (C) 2020. Online: https://pandas.pydata.org↩︎

  6. Plotly. Copyright (C) 2020. Online: https://plotly.com↩︎

  7. Portable Network Graphics (PNG). Copyright 1995–2019 Online: http://www.libpng.org/pub/png/↩︎