Civics & Covid-19How you vote (or don't vote) affects your health. The rights and responsibilities of all citizens have been challenged by the Covid-19 pandemic. Civics & Covid-19 project curates data that is used to explore how different political views impact the spread of Covid-19.
Daily statistics have been computed since April 12, 2020 for states, provinces, and cruise ships in the USA. Daily statistics for other countries have been computed since May 29, 2020.
The Center for Disease Control tells us that showing trends over the past 28 days best reflects the incubation period and average time needed to shed the virus. Reports on this website show trends over the past 28 days.
Data is posted through September 30, 2022. We aim to compute data through the end of 2022. If you would like access to data from September 30 through December 31, 2022, please send a request
Research ResultsHere's a map showing colors that represent how citizens voted in the past four presidential elections. Suppose the Covid-19 pandemic never happened. All of the States are shown with bright colors.
|Blue - Voted for Democrat in all elections
Lightblue - Voted for Democrat in 3 of 4 elections
Purple - Voted Democrat twice and Republican twice
Lightred - Voted for Republican in 3 of 4 elections
Red - Voted for Republican in all elections
Unfortunately, the Covid-19 pandemic did happen. The following maps show darker colors where there were more cases of Covid-19.
On August 16, 2021, the reporting period changed from 21 days to 28 days
What are the cummulative statistics by USA political party?
What are the cummulative statistics per 100,000 people?
What is COVID-19?
Where does Covid-19 data come from?
How can I import data using the Civics & Covid-19 API?
Where can I ask more questions?
Step-by-step technical details
Per 100,000 is commonly used to compare data:
confirmed cases / population * 100,000 = confirmed cases per 100,000
deaths / population * 100,000 = deaths per 100,000
Ranking the number of cases or deaths
SciPy was used to compute least-squares linear regression and the slope was used to rank cases or deaths:
slope, intercept, r, p, std_err = stats.linregress(days, c) days is a numpy array listing values from 1-21 c is a numpy array with the number of cases or deaths