2019-nCOV Data Visualizations

Here I will be adding data visualizations (both found and original) depicting facets of the 2019/2020 coronavirus pandemic.

Let’s start with one found on https://coronavirus.1point3acres.com showing how the cumulative number of confirmed COVID19 cases in each state has evolved over time.

This shows pretty clearly the situation in New York is dire, but I wouldn’t be surprised if California starts to close the gap.

Here is a similar graph, for each country



Here is a data viz I made using ERIS ArcGIS…



The best covid19 dashboard I’ve found belongs to the group at Johns Hopkins University…



One way we might be able to predict which U.S. counties will be disproportionately affected by the coronavirus pandemic is whether there are enough hospitals (specifically hospital beds) in those regions to accomodate the number of people expected to become infected with COVID-19.

Using data compiled from the CDC, Johns Hopkins University, and the NYT databases…

… I’ve generated a map of the location of U.S. hospitals. Each dot is a hospital. The size of the dot is proportional to the number of beds each hospital contains. Each dot is colored according to how many beds there are per 1000 people in the county. Such a map might help reveal areas with a low number of hospital beds per service population.

(full MATLAB code tutorial here)



Here is another plot similar to the one above. In this one…

  • Dot size == total number of hospital beds in a given county
  • Dot color == log(Cases) in that county


This next graph shows U.S. COVID-19 related hospitalizations over time, broken down by age group. The y-axis represents the number of infections per 100,000 people.